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Research Question Examples 🧑🏻‍🏫

25+ Practical Examples & Ideas To Help You Get Started 

By: Derek Jansen (MBA) | October 2023

A well-crafted research question (or set of questions) sets the stage for a robust study and meaningful insights.  But, if you’re new to research, it’s not always clear what exactly constitutes a good research question. In this post, we’ll provide you with clear examples of quality research questions across various disciplines, so that you can approach your research project with confidence!

Research Question Examples

• Psychology research questions
• Business research questions
• Education research questions
• Healthcare research questions
• Computer science research questions

Examples: Psychology

Let’s start by looking at some examples of research questions that you might encounter within the discipline of psychology.

How does sleep quality affect academic performance in university students?

This question is specific to a population (university students) and looks at a direct relationship between sleep and academic performance, both of which are quantifiable and measurable variables.

What factors contribute to the onset of anxiety disorders in adolescents?

The question narrows down the age group and focuses on identifying multiple contributing factors. There are various ways in which it could be approached from a methodological standpoint, including both qualitatively and quantitatively.

Do mindfulness techniques improve emotional well-being?

This is a focused research question aiming to evaluate the effectiveness of a specific intervention.

How does early childhood trauma impact adult relationships?

This research question targets a clear cause-and-effect relationship over a long timescale, making it focused but comprehensive.

Is there a correlation between screen time and depression in teenagers?

This research question focuses on an in-demand current issue and a specific demographic, allowing for a focused investigation. The key variables are clearly stated within the question and can be measured and analysed (i.e., high feasibility).

Examples: Business/Management

Next, let’s look at some examples of well-articulated research questions within the business and management realm.

How do leadership styles impact employee retention?

This is an example of a strong research question because it directly looks at the effect of one variable (leadership styles) on another (employee retention), allowing from a strongly aligned methodological approach.

What role does corporate social responsibility play in consumer choice?

Current and precise, this research question can reveal how social concerns are influencing buying behaviour by way of a qualitative exploration.

Does remote work increase or decrease productivity in tech companies?

Focused on a particular industry and a hot topic, this research question could yield timely, actionable insights that would have high practical value in the real world.

How do economic downturns affect small businesses in the homebuilding industry?

Vital for policy-making, this highly specific research question aims to uncover the challenges faced by small businesses within a certain industry.

Which employee benefits have the greatest impact on job satisfaction?

By being straightforward and specific, answering this research question could provide tangible insights to employers.

Examples: Education

Next, let’s look at some potential research questions within the education, training and development domain.

How does class size affect students’ academic performance in primary schools?

This example research question targets two clearly defined variables, which can be measured and analysed relatively easily.

Do online courses result in better retention of material than traditional courses?

Timely, specific and focused, answering this research question can help inform educational policy and personal choices about learning formats.

What impact do US public school lunches have on student health?

Targeting a specific, well-defined context, the research could lead to direct changes in public health policies.

To what degree does parental involvement improve academic outcomes in secondary education in the Midwest?

This research question focuses on a specific context (secondary education in the Midwest) and has clearly defined constructs.

What are the negative effects of standardised tests on student learning within Oklahoma primary schools?

This research question has a clear focus (negative outcomes) and is narrowed into a very specific context.

Need a helping hand?

Examples: Healthcare

Shifting to a different field, let’s look at some examples of research questions within the healthcare space.

What are the most effective treatments for chronic back pain amongst UK senior males?

Specific and solution-oriented, this research question focuses on clear variables and a well-defined context (senior males within the UK).

How do different healthcare policies affect patient satisfaction in public hospitals in South Africa?

This question is has clearly defined variables and is narrowly focused in terms of context.

Which factors contribute to obesity rates in urban areas within California?

This question is focused yet broad, aiming to reveal several contributing factors for targeted interventions.

Does telemedicine provide the same perceived quality of care as in-person visits for diabetes patients?

Ideal for a qualitative study, this research question explores a single construct (perceived quality of care) within a well-defined sample (diabetes patients).

Which lifestyle factors have the greatest affect on the risk of heart disease?

This research question aims to uncover modifiable factors, offering preventive health recommendations.

Examples: Computer Science

Last but certainly not least, let’s look at a few examples of research questions within the computer science world.

What are the perceived risks of cloud-based storage systems?

Highly relevant in our digital age, this research question would align well with a qualitative interview approach to better understand what users feel the key risks of cloud storage are.

Which factors affect the energy efficiency of data centres in Ohio?

With a clear focus, this research question lays a firm foundation for a quantitative study.

How do TikTok algorithms impact user behaviour amongst new graduates?

While this research question is more open-ended, it could form the basis for a qualitative investigation.

What are the perceived risk and benefits of open-source software software within the web design industry?

Practical and straightforward, the results could guide both developers and end-users in their choices.

Remember, these are just examples…

In this post, we’ve tried to provide a wide range of research question examples to help you get a feel for what research questions look like in practice. That said, it’s important to remember that these are just examples and don’t necessarily equate to good research topics . If you’re still trying to find a topic, check out our topic megalist for inspiration.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Home » Research Questions – Types, Examples and Writing Guide

Research Questions – Types, Examples and Writing Guide

Table of Contents

Research Questions

Definition:

Research questions are the specific questions that guide a research study or inquiry. These questions help to define the scope of the research and provide a clear focus for the study. Research questions are usually developed at the beginning of a research project and are designed to address a particular research problem or objective.

Types of Research Questions

Types of Research Questions are as follows:

Descriptive Research Questions

These aim to describe a particular phenomenon, group, or situation. For example:

• What are the characteristics of the target population?
• What is the prevalence of a particular disease in a specific region?

Exploratory Research Questions

These aim to explore a new area of research or generate new ideas or hypotheses. For example:

• What are the potential causes of a particular phenomenon?
• What are the possible outcomes of a specific intervention?

Explanatory Research Questions

These aim to understand the relationship between two or more variables or to explain why a particular phenomenon occurs. For example:

• What is the effect of a specific drug on the symptoms of a particular disease?
• What are the factors that contribute to employee turnover in a particular industry?

Predictive Research Questions

These aim to predict a future outcome or trend based on existing data or trends. For example :

• What will be the future demand for a particular product or service?
• What will be the future prevalence of a particular disease?

Evaluative Research Questions

These aim to evaluate the effectiveness of a particular intervention or program. For example:

• What is the impact of a specific educational program on student learning outcomes?
• What is the effectiveness of a particular policy or program in achieving its intended goals?

How to Choose Research Questions

Choosing research questions is an essential part of the research process and involves careful consideration of the research problem, objectives, and design. Here are some steps to consider when choosing research questions:

• Identify the research problem: Start by identifying the problem or issue that you want to study. This could be a gap in the literature, a social or economic issue, or a practical problem that needs to be addressed.
• Conduct a literature review: Conducting a literature review can help you identify existing research in your area of interest and can help you formulate research questions that address gaps or limitations in the existing literature.
• Define the research objectives : Clearly define the objectives of your research. What do you want to achieve with your study? What specific questions do you want to answer?
• Consider the research design : Consider the research design that you plan to use. This will help you determine the appropriate types of research questions to ask. For example, if you plan to use a qualitative approach, you may want to focus on exploratory or descriptive research questions.
• Ensure that the research questions are clear and answerable: Your research questions should be clear and specific, and should be answerable with the data that you plan to collect. Avoid asking questions that are too broad or vague.
• Get feedback : Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, feasible, and meaningful.

How to Write Research Questions

Guide for Writing Research Questions:

• Start with a clear statement of the research problem: Begin by stating the problem or issue that your research aims to address. This will help you to formulate focused research questions.
• Use clear language : Write your research questions in clear and concise language that is easy to understand. Avoid using jargon or technical terms that may be unfamiliar to your readers.
• Be specific: Your research questions should be specific and focused. Avoid broad questions that are difficult to answer. For example, instead of asking “What is the impact of climate change on the environment?” ask “What are the effects of rising sea levels on coastal ecosystems?”
• Use appropriate question types: Choose the appropriate question types based on the research design and objectives. For example, if you are conducting a qualitative study, you may want to use open-ended questions that allow participants to provide detailed responses.
• Consider the feasibility of your questions : Ensure that your research questions are feasible and can be answered with the resources available. Consider the data sources and methods of data collection when writing your questions.
• Seek feedback: Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, appropriate, and meaningful.

Examples of Research Questions

Some Examples of Research Questions with Research Titles:

Research Title: The Impact of Social Media on Mental Health

• Research Question : What is the relationship between social media use and mental health, and how does this impact individuals’ well-being?

Research Title: Factors Influencing Academic Success in High School

• Research Question: What are the primary factors that influence academic success in high school, and how do they contribute to student achievement?

Research Title: The Effects of Exercise on Physical and Mental Health

• Research Question: What is the relationship between exercise and physical and mental health, and how can exercise be used as a tool to improve overall well-being?

Research Title: Understanding the Factors that Influence Consumer Purchasing Decisions

• Research Question : What are the key factors that influence consumer purchasing decisions, and how do these factors vary across different demographics and products?

Research Title: The Impact of Technology on Communication

• Research Question : How has technology impacted communication patterns, and what are the effects of these changes on interpersonal relationships and society as a whole?

Research Title: Investigating the Relationship between Parenting Styles and Child Development

• Research Question: What is the relationship between different parenting styles and child development outcomes, and how do these outcomes vary across different ages and developmental stages?

Research Title: The Effectiveness of Cognitive-Behavioral Therapy in Treating Anxiety Disorders

• Research Question: How effective is cognitive-behavioral therapy in treating anxiety disorders, and what factors contribute to its success or failure in different patients?

Research Title: The Impact of Climate Change on Biodiversity

• Research Question : How is climate change affecting global biodiversity, and what can be done to mitigate the negative effects on natural ecosystems?

Research Title: Exploring the Relationship between Cultural Diversity and Workplace Productivity

• Research Question : How does cultural diversity impact workplace productivity, and what strategies can be employed to maximize the benefits of a diverse workforce?

Research Title: The Role of Artificial Intelligence in Healthcare

• Research Question: How can artificial intelligence be leveraged to improve healthcare outcomes, and what are the potential risks and ethical concerns associated with its use?

Applications of Research Questions

Here are some of the key applications of research questions:

• Defining the scope of the study : Research questions help researchers to narrow down the scope of their study and identify the specific issues they want to investigate.
• Developing hypotheses: Research questions often lead to the development of hypotheses, which are testable predictions about the relationship between variables. Hypotheses provide a clear and focused direction for the study.
• Designing the study : Research questions guide the design of the study, including the selection of participants, the collection of data, and the analysis of results.
• Collecting data : Research questions inform the selection of appropriate methods for collecting data, such as surveys, interviews, or experiments.
• Analyzing data : Research questions guide the analysis of data, including the selection of appropriate statistical tests and the interpretation of results.
• Communicating results : Research questions help researchers to communicate the results of their study in a clear and concise manner. The research questions provide a framework for discussing the findings and drawing conclusions.

Characteristics of Research Questions

Characteristics of Research Questions are as follows:

• Clear and Specific : A good research question should be clear and specific. It should clearly state what the research is trying to investigate and what kind of data is required.
• Relevant : The research question should be relevant to the study and should address a current issue or problem in the field of research.
• Testable : The research question should be testable through empirical evidence. It should be possible to collect data to answer the research question.
• Concise : The research question should be concise and focused. It should not be too broad or too narrow.
• Feasible : The research question should be feasible to answer within the constraints of the research design, time frame, and available resources.
• Original : The research question should be original and should contribute to the existing knowledge in the field of research.
• Significant : The research question should have significance and importance to the field of research. It should have the potential to provide new insights and knowledge to the field.
• Ethical : The research question should be ethical and should not cause harm to any individuals or groups involved in the study.

Purpose of Research Questions

Research questions are the foundation of any research study as they guide the research process and provide a clear direction to the researcher. The purpose of research questions is to identify the scope and boundaries of the study, and to establish the goals and objectives of the research.

The main purpose of research questions is to help the researcher to focus on the specific area or problem that needs to be investigated. They enable the researcher to develop a research design, select the appropriate methods and tools for data collection and analysis, and to organize the results in a meaningful way.

Research questions also help to establish the relevance and significance of the study. They define the research problem, and determine the research methodology that will be used to address the problem. Research questions also help to determine the type of data that will be collected, and how it will be analyzed and interpreted.

Finally, research questions provide a framework for evaluating the results of the research. They help to establish the validity and reliability of the data, and provide a basis for drawing conclusions and making recommendations based on the findings of the study.

Advantages of Research Questions

There are several advantages of research questions in the research process, including:

• Focus : Research questions help to focus the research by providing a clear direction for the study. They define the specific area of investigation and provide a framework for the research design.
• Clarity : Research questions help to clarify the purpose and objectives of the study, which can make it easier for the researcher to communicate the research aims to others.
• Relevance : Research questions help to ensure that the study is relevant and meaningful. By asking relevant and important questions, the researcher can ensure that the study will contribute to the existing body of knowledge and address important issues.
• Consistency : Research questions help to ensure consistency in the research process by providing a framework for the development of the research design, data collection, and analysis.
• Measurability : Research questions help to ensure that the study is measurable by defining the specific variables and outcomes that will be measured.
• Replication : Research questions help to ensure that the study can be replicated by providing a clear and detailed description of the research aims, methods, and outcomes. This makes it easier for other researchers to replicate the study and verify the results.

Limitations of Research Questions

Limitations of Research Questions are as follows:

• Subjectivity : Research questions are often subjective and can be influenced by personal biases and perspectives of the researcher. This can lead to a limited understanding of the research problem and may affect the validity and reliability of the study.
• Inadequate scope : Research questions that are too narrow in scope may limit the breadth of the study, while questions that are too broad may make it difficult to focus on specific research objectives.
• Unanswerable questions : Some research questions may not be answerable due to the lack of available data or limitations in research methods. In such cases, the research question may need to be rephrased or modified to make it more answerable.
• Lack of clarity : Research questions that are poorly worded or ambiguous can lead to confusion and misinterpretation. This can result in incomplete or inaccurate data, which may compromise the validity of the study.
• Difficulty in measuring variables : Some research questions may involve variables that are difficult to measure or quantify, making it challenging to draw meaningful conclusions from the data.
• Lack of generalizability: Research questions that are too specific or limited in scope may not be generalizable to other contexts or populations. This can limit the applicability of the study’s findings and restrict its broader implications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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How to Write a Good Research Question (w/ Examples)

What is a Research Question?

A research question is the main question that your study sought or is seeking to answer. A clear research question guides your research paper or thesis and states exactly what you want to find out, giving your work a focus and objective. Learning  how to write a hypothesis or research question is the start to composing any thesis, dissertation, or research paper. It is also one of the most important sections of a research proposal . 

A good research question not only clarifies the writing in your study; it provides your readers with a clear focus and facilitates their understanding of your research topic, as well as outlining your study’s objectives. Before drafting the paper and receiving research paper editing (and usually before performing your study), you should write a concise statement of what this study intends to accomplish or reveal.

Research Question Writing Tips

Listed below are the important characteristics of a good research question:

A good research question should:

• Be clear and provide specific information so readers can easily understand the purpose.
• Be focused in its scope and narrow enough to be addressed in the space allowed by your paper
• Be relevant and concise and express your main ideas in as few words as possible, like a hypothesis.
• Be precise and complex enough that it does not simply answer a closed “yes or no” question, but requires an analysis of arguments and literature prior to its being considered acceptable. 
• Be arguable or testable so that answers to the research question are open to scrutiny and specific questions and counterarguments.

Some of these characteristics might be difficult to understand in the form of a list. Let’s go into more detail about what a research question must do and look at some examples of research questions.

The research question should be specific and focused 

Research questions that are too broad are not suitable to be addressed in a single study. One reason for this can be if there are many factors or variables to consider. In addition, a sample data set that is too large or an experimental timeline that is too long may suggest that the research question is not focused enough.

A specific research question means that the collective data and observations come together to either confirm or deny the chosen hypothesis in a clear manner. If a research question is too vague, then the data might end up creating an alternate research problem or hypothesis that you haven’t addressed in your Introduction section .

The research question should be based on the literature 

An effective research question should be answerable and verifiable based on prior research because an effective scientific study must be placed in the context of a wider academic consensus. This means that conspiracy or fringe theories are not good research paper topics.

Instead, a good research question must extend, examine, and verify the context of your research field. It should fit naturally within the literature and be searchable by other research authors.

References to the literature can be in different citation styles and must be properly formatted according to the guidelines set forth by the publishing journal, university, or academic institution. This includes in-text citations as well as the Reference section . 

The research question should be realistic in time, scope, and budget

There are two main constraints to the research process: timeframe and budget.

A proper research question will include study or experimental procedures that can be executed within a feasible time frame, typically by a graduate doctoral or master’s student or lab technician. Research that requires future technology, expensive resources, or follow-up procedures is problematic.

A researcher’s budget is also a major constraint to performing timely research. Research at many large universities or institutions is publicly funded and is thus accountable to funding restrictions. 

The research question should be in-depth

Research papers, dissertations and theses , and academic journal articles are usually dozens if not hundreds of pages in length.

A good research question or thesis statement must be sufficiently complex to warrant such a length, as it must stand up to the scrutiny of peer review and be reproducible by other scientists and researchers.

Research Question Types

Qualitative and quantitative research are the two major types of research, and it is essential to develop research questions for each type of study. 

Quantitative Research Questions

Quantitative research questions are specific. A typical research question involves the population to be studied, dependent and independent variables, and the research design.

In addition, quantitative research questions connect the research question and the research design. In addition, it is not possible to answer these questions definitively with a “yes” or “no” response. For example, scientific fields such as biology, physics, and chemistry often deal with “states,” in which different quantities, amounts, or velocities drastically alter the relevance of the research.

As a consequence, quantitative research questions do not contain qualitative, categorical, or ordinal qualifiers such as “is,” “are,” “does,” or “does not.”

Categories of quantitative research questions

Qualitative research questions.

In quantitative research, research questions have the potential to relate to broad research areas as well as more specific areas of study. Qualitative research questions are less directional, more flexible, and adaptable compared with their quantitative counterparts. Thus, studies based on these questions tend to focus on “discovering,” “explaining,” “elucidating,” and “exploring.”

Categories of qualitative research questions

Quantitative and qualitative research question examples.

Good and Bad Research Question Examples

Below are some good (and not-so-good) examples of research questions that researchers can use to guide them in crafting their own research questions.

Research Question Example 1

The first research question is too vague in both its independent and dependent variables. There is no specific information on what “exposure” means. Does this refer to comments, likes, engagement, or just how much time is spent on the social media platform?

Second, there is no useful information on what exactly “affected” means. Does the subject’s behavior change in some measurable way? Or does this term refer to another factor such as the user’s emotions?

Research Question Example 2

In this research question, the first example is too simple and not sufficiently complex, making it difficult to assess whether the study answered the question. The author could really only answer this question with a simple “yes” or “no.” Further, the presence of data would not help answer this question more deeply, which is a sure sign of a poorly constructed research topic.

The second research question is specific, complex, and empirically verifiable. One can measure program effectiveness based on metrics such as attendance or grades. Further, “bullying” is made into an empirical, quantitative measurement in the form of recorded disciplinary actions.

Steps for Writing a Research Question

Good research questions are relevant, focused, and meaningful. It can be difficult to come up with a good research question, but there are a few steps you can follow to make it a bit easier.

1. Start with an interesting and relevant topic

Choose a research topic that is interesting but also relevant and aligned with your own country’s culture or your university’s capabilities. Popular academic topics include healthcare and medical-related research. However, if you are attending an engineering school or humanities program, you should obviously choose a research question that pertains to your specific study and major.

Below is an embedded graph of the most popular research fields of study based on publication output according to region. As you can see, healthcare and the basic sciences receive the most funding and earn the highest number of publications. 

2. Do preliminary research  

You can begin doing preliminary research once you have chosen a research topic. Two objectives should be accomplished during this first phase of research. First, you should undertake a preliminary review of related literature to discover issues that scholars and peers are currently discussing. With this method, you show that you are informed about the latest developments in the field.

Secondly, identify knowledge gaps or limitations in your topic by conducting a preliminary literature review . It is possible to later use these gaps to focus your research question after a certain amount of fine-tuning.

3. Narrow your research to determine specific research questions

You can focus on a more specific area of study once you have a good handle on the topic you want to explore. Focusing on recent literature or knowledge gaps is one good option. 

By identifying study limitations in the literature and overlooked areas of study, an author can carve out a good research question. The same is true for choosing research questions that extend or complement existing literature.

4. Evaluate your research question

Make sure you evaluate the research question by asking the following questions:

Is my research question clear?

The resulting data and observations that your study produces should be clear. For quantitative studies, data must be empirical and measurable. For qualitative, the observations should be clearly delineable across categories.

Is my research question focused and specific?

A strong research question should be specific enough that your methodology or testing procedure produces an objective result, not one left to subjective interpretation. Open-ended research questions or those relating to general topics can create ambiguous connections between the results and the aims of the study. 

Is my research question sufficiently complex?

The result of your research should be consequential and substantial (and fall sufficiently within the context of your field) to warrant an academic study. Simply reinforcing or supporting a scientific consensus is superfluous and will likely not be well received by most journal editors.  

Editing Your Research Question

Your research question should be fully formulated well before you begin drafting your research paper. However, you can receive English paper editing and proofreading services at any point in the drafting process. Language editors with expertise in your academic field can assist you with the content and language in your Introduction section or other manuscript sections. And if you need further assistance or information regarding paper compositions, in the meantime, check out our academic resources , which provide dozens of articles and videos on a variety of academic writing and publication topics.

Have a language expert improve your writing

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• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

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Rebecca Bevans

How to Write a Research Question: Types and Examples 

The first step in any research project is framing the research question. It can be considered the core of any systematic investigation as the research outcomes are tied to asking the right questions. Thus, this primary interrogation point sets the pace for your research as it helps collect relevant and insightful information that ultimately influences your work.   

Typically, the research question guides the stages of inquiry, analysis, and reporting. Depending on the use of quantifiable or quantitative data, research questions are broadly categorized into quantitative or qualitative research questions. Both types of research questions can be used independently or together, considering the overall focus and objectives of your research.  

What is a research question?

A research question is a clear, focused, concise, and arguable question on which your research and writing are centered. 1 It states various aspects of the study, including the population and variables to be studied and the problem the study addresses. These questions also set the boundaries of the study, ensuring cohesion. 

Designing the research question is a dynamic process where the researcher can change or refine the research question as they review related literature and develop a framework for the study. Depending on the scale of your research, the study can include single or multiple research questions. 

A good research question has the following features: 

• It is relevant to the chosen field of study. 
• The question posed is arguable and open for debate, requiring synthesizing and analysis of ideas. 
• It is focused and concisely framed. 
• A feasible solution is possible within the given practical constraint and timeframe. 

A poorly formulated research question poses several risks. 1   

• Researchers can adopt an erroneous design. 
• It can create confusion and hinder the thought process, including developing a clear protocol.  
• It can jeopardize publication efforts.  
• It causes difficulty in determining the relevance of the study findings.  
• It causes difficulty in whether the study fulfils the inclusion criteria for systematic review and meta-analysis. This creates challenges in determining whether additional studies or data collection is needed to answer the question.  
• Readers may fail to understand the objective of the study. This reduces the likelihood of the study being cited by others. 

Now that you know “What is a research question?”, let’s look at the different types of research questions. 

Types of research questions

Depending on the type of research to be done, research questions can be classified broadly into quantitative, qualitative, or mixed-methods studies. Knowing the type of research helps determine the best type of research question that reflects the direction and epistemological underpinnings of your research. 

The structure and wording of quantitative 2 and qualitative research 3 questions differ significantly. The quantitative study looks at causal relationships, whereas the qualitative study aims at exploring a phenomenon. 

• Quantitative research questions:  
• Seeks to investigate social, familial, or educational experiences or processes in a particular context and/or location.  
• Answers ‘how,’ ‘what,’ or ‘why’ questions. 
• Investigates connections, relations, or comparisons between independent and dependent variables. 

Quantitative research questions can be further categorized into descriptive, comparative, and relationship, as explained in the Table below. 

• Qualitative research questions  

Qualitative research questions are adaptable, non-directional, and more flexible. It concerns broad areas of research or more specific areas of study to discover, explain, or explore a phenomenon. These are further classified as follows: 

• Mixed-methods studies  

Mixed-methods studies use both quantitative and qualitative research questions to answer your research question. Mixed methods provide a complete picture than standalone quantitative or qualitative research, as it integrates the benefits of both methods. Mixed methods research is often used in multidisciplinary settings and complex situational or societal research, especially in the behavioral, health, and social science fields. 

What makes a good research question

A good research question should be clear and focused to guide your research. It should synthesize multiple sources to present your unique argument, and should ideally be something that you are interested in. But avoid questions that can be answered in a few factual statements. The following are the main attributes of a good research question. 

• Specific: The research question should not be a fishing expedition performed in the hopes that some new information will be found that will benefit the researcher. The central research question should work with your research problem to keep your work focused. If using multiple questions, they should all tie back to the central aim. 
• Measurable: The research question must be answerable using quantitative and/or qualitative data or from scholarly sources to develop your research question. If such data is impossible to access, it is better to rethink your question. 
• Attainable: Ensure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific. 
• You have the expertise 
• You have the equipment and resources 
• Realistic: Developing your research question should be based on initial reading about your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline. 
• Based on some sort of rational physics 
• Can be done in a reasonable time frame 
• Timely: The research question should contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on. 
• Novel 
• Based on current technologies. 
• Important to answer current problems or concerns. 
• Lead to new directions. 
• Important: Your question should have some aspect of originality. Incremental research is as important as exploring disruptive technologies. For example, you can focus on a specific location or explore a new angle. 
• Meaningful whether the answer is “Yes” or “No.” Closed-ended, yes/no questions are too simple to work as good research questions. Such questions do not provide enough scope for robust investigation and discussion. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation before providing an answer. 

Steps for developing a good research question

The importance of research questions cannot be understated. When drafting a research question, use the following frameworks to guide the components of your question to ease the process. 4  

• Determine the requirements: Before constructing a good research question, set your research requirements. What is the purpose? Is it descriptive, comparative, or explorative research? Determining the research aim will help you choose the most appropriate topic and word your question appropriately. 
• Select a broad research topic: Identify a broader subject area of interest that requires investigation. Techniques such as brainstorming or concept mapping can help identify relevant connections and themes within a broad research topic. For example, how to learn and help students learn. 
• Perform preliminary investigation: Preliminary research is needed to obtain up-to-date and relevant knowledge on your topic. It also helps identify issues currently being discussed from which information gaps can be identified. 
• Narrow your focus: Narrow the scope and focus of your research to a specific niche. This involves focusing on gaps in existing knowledge or recent literature or extending or complementing the findings of existing literature. Another approach involves constructing strong research questions that challenge your views or knowledge of the area of study (Example: Is learning consistent with the existing learning theory and research). 
• Identify the research problem: Once the research question has been framed, one should evaluate it. This is to realize the importance of the research questions and if there is a need for more revising (Example: How do your beliefs on learning theory and research impact your instructional practices). 

How to write a research question

Those struggling to understand how to write a research question, these simple steps can help you simplify the process of writing a research question. 

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 
• Example 2 

References:  

• Thabane, L., Thomas, T., Ye, C., & Paul, J. (2009). Posing the research question: not so simple.  Canadian Journal of Anesthesia/Journal canadien d’anesthésie ,  56 (1), 71-79. 
• Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research.  Nephrology Nursing Journal ,  45 (2), 209-213. 
• Kyngäs, H. (2020). Qualitative research and content analysis.  The application of content analysis in nursing science research , 3-11. 
• Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question.  The clinical teacher ,  15 (2), 104-108. 
• Fandino, W. (2019). Formulating a good research question: Pearls and pitfalls.  Indian Journal of Anaesthesia ,  63 (8), 611. 
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  ACP journal club ,  123 (3), A12-A13 

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Transitive and Intransitive Verbs in the World of Research

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10 Real-Life Experimental Research Examples

Experimental research is research that involves using a scientific approach to examine research variables.

Below are some famous experimental research examples. Some of these studies were conducted quite a long time ago. Some were so controversial that they would never be attempted today. And some were so unethical that they would never be permitted again.

A few of these studies have also had very practical implications for modern society involving criminal investigations, the impact of television and the media, and the power of authority figures.

Examples of Experimental Research

1. pavlov’s dog: classical conditioning.

Dr. Ivan Pavlov was a physiologist studying animal digestive systems in the 1890s. In one study, he presented food to a dog and then collected its salivatory juices via a tube attached to the inside of the animal’s mouth.

As he was conducting his experiments, an annoying thing kept happening; every time his assistant would enter the lab with a bowl of food for the experiment, the dog would start to salivate at the sound of the assistant’s footsteps.

Although this disrupted his experimental procedures, eventually, it dawned on Pavlov that something else was to be learned from this problem.

Pavlov learned that animals could be conditioned into responding on a physiological level to various stimuli, such as food, or even the sound of the assistant bringing the food down the hall.

Hence, the creation of the theory of classical conditioning. One of the most influential theories in psychology still to this day.

2. Bobo Doll Experiment: Observational Learning

Dr. Albert Bandura conducted one of the most influential studies in psychology in the 1960s at Stanford University.

His intention was to demonstrate that cognitive processes play a fundamental role in learning. At the time, Behaviorism was the predominant theoretical perspective, which completely rejected all inferences to constructs not directly observable .

So, Bandura made two versions of a video. In version #1, an adult behaved aggressively with a Bobo doll by throwing it around the room and striking it with a wooden mallet. In version #2, the adult played gently with the doll by carrying it around to different parts of the room and pushing it gently.

After showing children one of the two versions, they were taken individually to a room that had a Bobo doll. Their behavior was observed and the results indicated that children that watched version #1 of the video were far more aggressive than those that watched version #2.

Not only did Bandura’s Bobo doll study form the basis of his social learning theory, it also helped start the long-lasting debate about the harmful effects of television on children.

Worth Checking Out: What’s the Difference between Experimental and Observational Studies?

3. The Asch Study: Conformity  

Dr. Solomon Asch was interested in conformity and the power of group pressure. His study was quite simple. Different groups of students were shown lines of varying lengths and asked, “which line is longest.”

However, out of each group, only one was an actual participant. All of the others in the group were working with Asch and instructed to say that one of the shorter lines was actually the longest.

Nearly every time, the real participant gave an answer that was clearly wrong, but the same as the rest of the group.

The study is one of the most famous in psychology because it demonstrated the power of social pressure so clearly.  

4. Car Crash Experiment: Leading Questions

In 1974, Dr. Elizabeth Loftus and her undergraduate student John Palmer designed a study to examine how fallible human judgement is under certain conditions.

They showed groups of research participants videos that depicted accidents between two cars. Later, the participants were asked to estimate the rate of speed of the cars.

Here’s the interesting part. All participants were asked the same question with the exception of a single word: “How fast were the two cars going when they ______into each other?” The word in the blank varied in its implied severity.

Participants’ estimates were completely affected by the word in the blank. When the word “smashed” was used, participants estimated the cars were going much faster than when the word “contacted” was used. 

This line of research has had a huge impact on law enforcement interrogation practices, line-up procedures, and the credibility of eyewitness testimony .

5. The 6 Universal Emotions

The research by Dr. Paul Ekman has been influential in the study of emotions. His early research revealed that all human beings, regardless of culture, experience the same 6 basic emotions: happiness, sadness, disgust, fear, surprise, and anger.

In the late 1960s, Ekman traveled to Papua New Guinea. He approached a tribe of people that were extremely isolated from modern culture. With the help of a guide, he would describe different situations to individual members and take a photo of their facial expressions.

The situations included: if a good friend had come; their child had just died; they were about to get into a fight; or had just stepped on a dead pig.

The facial expressions of this highly isolated tribe were nearly identical to those displayed by people in his studies in California.

6. The Little Albert Study: Development of Phobias  

Dr. John Watson and Dr. Rosalie Rayner sought to demonstrate how irrational fears were developed.

Their study involved showing a white rat to an infant. Initially, the child had no fear of the rat. However, the researchers then began to create a loud noise each time they showed the child the rat by striking a steel bar with a hammer.

Eventually, the child started to cry and feared the white rat. The child also developed a fear of other white, furry objects such as white rabbits and a Santa’s beard.

This study is famous because it demonstrated one way in which phobias are developed in humans, and also because it is now considered highly unethical for its mistreatment of children, lack of study debriefing , and intent to instil fear.  

7. A Class Divided: Discrimination

Perhaps one of the most famous psychological experiments of all time was not conducted by a psychologist. In 1968, third grade teacher Jane Elliott conducted one of the most famous studies on discrimination in history. It took place shortly after the assassination of Dr. Martin Luther King, Jr.

She divided her class into two groups: brown-eyed and blue-eyed students. On the first day of the experiment, she announced the blue-eyed group as superior. They received extra privileges and were told not to intermingle with the brown-eyed students.

They instantly became happier, more self-confident, and started performing better academically.

The next day, the roles were reversed. The brown-eyed students were announced as superior and given extra privileges. Their behavior changed almost immediately and exhibited the same patterns as the other group had the day before.

This study was a remarkable demonstration of the harmful effects of discrimination.

8. The Milgram Study: Obedience to Authority

Dr. Stanley Milgram conducted one of the most influential experiments on authority and obedience in 1961 at Yale University.

Participants were told they were helping study the effects of punishment on learning. Their job was to administer an electric shock to another participant each time they made an error on a test. The other participant was actually an actor in another room that only pretended to be shocked.

However, each time a mistake was made, the level of shock was supposed to increase, eventually reaching quite high voltage levels. When the real participants expressed reluctance to administer the next level of shock, the experimenter, who served as the authority figure in the room, pressured the participant to deliver the next level of shock.

The results of this study were truly astounding. A surprisingly high percentage of participants continued to deliver the shocks to the highest level possible despite the very strong objections by the “other participant.”

This study demonstrated the power of authority figures.

9. The Marshmallow Test: Delay of Gratification

The Marshmallow Test was designed by Dr. Walter Mischel to examine the role of delay of gratification and academic success.

Children ages 4-6 years old were seated at a table with one marshmallow placed in front of them. The experimenter explained that if they did not eat the marshmallow, they would receive a second one. They could then eat both.

The children that were able to delay gratification the longest were rated as significantly more competent later in life and earned higher SAT scores than children that could not withstand the temptation.  

The study has since been conceptually replicated by other researchers that have revealed additional factors involved in delay of gratification and academic achievement.

10. Stanford Prison Study: Deindividuation

Dr. Philip Zimbardo conducted one of the most famous psychological studies of all time in 1971. The purpose of the study was to investigate how the power structure in some situations can lead people to behave in ways highly uncharacteristic of their usual behavior.

College students were recruited to participate in the study. Some were randomly assigned to play the role of prison guard. The others were actually “arrested” by real police officers. They were blindfolded and taken to the basement of the university’s psychology building which had been converted to look like a prison.

Although the study was supposed to last 2 weeks, it had to be halted due to the abusive actions of the guards.

The study demonstrated that people will behave in ways they never thought possible when placed in certain roles and power structures. Although the Stanford Prison Study is so well-known for what it revealed about human nature, it is also famous because of the numerous violations of ethical principles.

The studies above are varied and focused on many different aspects of human behavior . However, each example of experimental research listed above has had a lasting impact on society. Some have had tremendous sway in how very practical matters are conducted, such as criminal investigations and legal proceedings.

Psychology is a field of study that is often not fully understood by the general public. When most people hear the term “psychology,” they think of a therapist that listens carefully to the revealing statements of a patient. The therapist then tries to help their patient learn to cope with many of life’s challenges. Nothing wrong with that.

In reality however, most psychologists are researchers. They spend most of their time designing and conducting experiments to enhance our understanding of the human condition.

Asch SE. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority . Psychological Monographs: General and Applied, 70 (9),1-70. https://doi.org/doi:10.1037/h0093718

Bandura A. (1965). Influence of models’ reinforcement contingencies on the acquisition of imitative responses. Journal of Personality and Social Psychology, 1 (6), 589-595. https://doi.org/doi:10.1037/h0022070

Beck, H. P., Levinson, S., & Irons, G. (2009). Finding little Albert: A journey to John B. Watson’s infant laboratory.  American Psychologist, 64(7),  605-614.

Ekman, P. & Friesen, W. V. (1971).  Constants Across Cultures in the Face and motion .  Journal of Personality and Social Psychology, 17(2) , 124-129.

Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of

the interaction between language and memory. Journal of Verbal Learning and Verbal

Behavior, 13 (5), 585–589.

Milgram S (1965). Some Conditions of Obedience and Disobedience to Authority. Human Relations, 18(1), 57–76.

Mischel, W., & Ebbesen, E. B. (1970). Attention in delay of gratification . Journal of Personality and Social Psychology, 16 (2), 329-337.

Pavlov, I.P. (1927). Conditioned Reflexes . London: Oxford University Press.

Watson, J. & Rayner, R. (1920). Conditioned emotional reactions.  Journal of Experimental Psychology, 3 , 1-14. Zimbardo, P., Haney, C., Banks, W. C., & Jaffe, D. (1971). The Stanford Prison Experiment: A simulation study of the psychology of imprisonment . Stanford University, Stanford Digital Repository, Stanford.

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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80 fascinating psychology research questions for your next project

Last updated

15 February 2024

Reviewed by

Brittany Ferri, PhD, OTR/L

Psychology research is essential for furthering our understanding of human behavior and improving the diagnosis and treatment of psychological conditions.

When psychologists know more about how different social and cultural factors influence how humans act, think, and feel, they can recommend improvements to practices in areas such as education, sport, healthcare, and law enforcement.

Below, you will find 80 research question examples across 16 branches of psychology. First, though, let’s look at some tips to help you select a suitable research topic.

• How to choose a good psychology research topic

Psychology has many branches that break down further into topics. Choosing a topic for your psychology research paper can be daunting because there are so many to choose from. It’s an important choice, as the topic you select will open up a range of questions to explore.

The tips below can help you find a psychology research topic that suits your skills and interests.

Tip #1: Select a topic that interests you

Passion and interest should fuel every research project. A topic that fascinates you will most likely interest others as well. Think about the questions you and others might have and decide on the issues that matter most. Draw on your own interests, but also keep your research topical and relevant to others.

Don’t limit yourself to a topic that you already know about. Instead, choose one that will make you want to know more and dig deeper. This will keep you motivated and excited about your research.

Tip #2: Choose a topic with a manageable scope

If your topic is too broad, you can get overwhelmed by the amount of information available and have trouble maintaining focus. On the other hand, you may find it difficult to find enough information if you choose a topic that is too narrow.

To determine if the topic is too broad or too narrow, start researching as early as possible. If you find there’s an overwhelming amount of research material, you’ll probably need to narrow the topic down. For example, instead of researching the general population, it might be easier to focus on a specific age group. Ask yourself what area of the general topic interests you most and focus on that.

If your scope is too narrow, try to generalize or focus on a larger related topic. Expand your search criteria or select additional databases for information. Consider if the topic is too new to have much information published on it as well.

Tip #3: Select a topic that will produce useful and relevant insights

Doing some preliminary research will reveal any existing research on the topic. If there is existing research, will you be able to produce new insights? You might need to focus on a different area or see if the existing research has limitations that you can overcome.

Bear in mind that finding new information from which to draw fresh insights may be impossible if your topic has been over-researched.

You’ll also need to consider whether your topic is relevant to current trends and needs. For example, researching psychology topics related to social media use may be highly relevant today.

• 80 psychology research topics and questions

Psychology is a broad subject with many branches and potential areas of study. Here are some of them:

Developmental

Personality

Experimental

Organizational

Educational

Neuropsychology

Controversial topics

Below we offer some suggestions on research topics and questions that can get you started. Keep in mind that these are not all-inclusive but should be personalized to fit the theme of your paper.

Social psychology research topics and questions

Social psychology has roots as far back as the 18th century. In simple terms, it’s the study of how behavior is influenced by the presence and behavior of others. It is the science of finding out who we are, who we think we are, and how our perceptions affect ourselves and others. It looks at personalities, relationships, and group behavior.

Here are some potential research questions and paper titles for this topic:

How does social media use impact perceptions of body image in male adolescents?

2. Is childhood bullying a risk factor for social anxiety in adults?

Is homophobia in individuals caused by genetic or environmental factors?

What is the most important psychological predictor of a person’s willingness to donate to charity?

Does a person’s height impact how other people perceive them? If so, how?

Cognitive psychology research questions

Cognitive psychology is the branch that focuses on the interactions of thinking, emotion, creativity, and problem-solving. It also explores the reasons humans think the way they do.

This topic involves exploring how people think by measuring intelligence, thoughts, and cognition. 

Here are some research question ideas:

6. Is there a link between chronic stress and memory function?

7. Can certain kinds of music trigger memories in people with memory loss?

8. Do remote meetings impact the efficacy of team decision-making?

9. Do word games and puzzles slow cognitive decline in adults over the age of 80?

10. Does watching television impact a child’s reading ability?

Developmental psychology research questions

Developmental psychology is the study of how humans grow and change over their lifespan. It usually focuses on the social, emotional, and physical development of babies and children, though it can apply to people of all ages. Developmental psychology is important for understanding how we learn, mature, and adapt to changes.

Here are some questions that might inspire your research:

11. Does grief accelerate the aging process?

12. How do parent–child attachment patterns influence the development of emotion regulation in teenagers?

13. Does bilingualism affect cognitive decline in adults over the age of 70?

14. How does the transition to adulthood impact decision-making abilities

15. How does early exposure to music impact mental health and well-being in school-aged children?

Personality psychology research questions

Personality psychology studies personalities, how they develop, their structures, and the processes that define them. It looks at intelligence, disposition, moral beliefs, thoughts, and reactions.

The goal of this branch of psychology is to scientifically interpret the way personality patterns manifest into an individual’s behaviors. Here are some example research questions:

16. Nature vs. nurture: Which impacts personality development the most?

17. The role of genetics on personality: Does an adopted child take on their biological parents’ personality traits?

18. How do personality traits influence leadership styles and effectiveness in organizational settings?

19. Is there a relationship between an individual’s personality and mental health?

20. Can a chronic illness affect your personality?

Abnormal psychology research questions

As the name suggests, abnormal psychology is a branch that focuses on abnormal behavior and psychopathology (the scientific study of mental illness or disorders).

Abnormal behavior can be challenging to define. Who decides what is “normal”? As such, psychologists in this area focus on the level of distress that certain behaviors may cause, although this typically involves studying mental health conditions such as depression, obsessive-compulsive disorder (OCD), and phobias.

Here are some questions to consider:

21. How does technology impact the development of social anxiety disorder?

22. What are the factors behind the rising incidence of eating disorders in adolescents?

23. Are mindfulness-based interventions effective in the treatment of PTSD?

24. Is there a connection between depression and gambling addiction?

25. Can physical trauma cause psychopathy?

Clinical psychology research questions

Clinical psychology deals with assessing and treating mental illness or abnormal or psychiatric behaviors. It differs from abnormal psychology in that it focuses more on treatments and clinical aspects, while abnormal psychology is more behavioral focused.

This is a specialty area that provides care and treatment for complex mental health conditions. This can include treatment, not only for individuals but for couples, families, and other groups. Clinical psychology also supports communities, conducts research, and offers training to promote mental health. This category is very broad, so there are lots of topics to explore.

Below are some example research questions to consider:

26. Do criminals require more specific therapies or interventions?

27. How effective are selective serotonin reuptake inhibitors in treating mental health disorders?

28. Are there any disadvantages to humanistic therapy?

29. Can group therapy be more beneficial than one-on-one therapy sessions?

30. What are the factors to consider when selecting the right treatment plan for patients with anxiety?

Experimental psychology research questions

Experimental psychology deals with studies that can prove or disprove a hypothesis. Psychologists in this field use scientific methods to collect data on basic psychological processes such as memory, cognition, and learning. They use this data to test the whys and hows of behavior and how outside factors influence its creation.

Areas of interest in this branch relate to perception, memory, emotion, and sensation. The below are example questions that could inspire your own research:

31. Do male or female parents/carers have a more calming influence on children?

32. Will your preference for a genre of music increase the more you listen to it?

33. What are the psychological effects of posting on social media vs. not posting?

34. How is productivity affected by social connection?

35. Is cheating contagious?

Organizational psychology research questions

Organizational psychology studies human behavior in the workplace. It is most frequently used to evaluate an employee, group, or a company’s organizational dynamics. Researchers aim to isolate issues and identify solutions.

This area of study can be beneficial to both employees and employers since the goal is to improve the overall work environment and experience. Researchers apply psychological principles and findings to recommend improvements in performance, communication, job satisfaction, and safety. 

Some potential research questions include the following:

36. How do different leadership styles affect employee morale?

37. Do longer lunch breaks boost employee productivity?

38. Is gender an antecedent to workplace stress?

39. What is the most effective way to promote work–life balance among employees?

40. How do different organizational structures impact the effectiveness of communication, decision-making, and productivity?

Forensic psychology research questions

Some questions to consider exploring in this branch of psychology are:

41. How does incarceration affect mental health?

42. Is childhood trauma a driver for criminal behavior during adulthood?

43. Are people with mental health conditions more likely to be victims of crimes?

44. What are the drivers of false memories, and how do they impact the justice system?

45. Is the media responsible for copycat crimes?

Educational psychology research questions

Educational psychology studies children in an educational setting. It covers topics like teaching methods, aptitude assessment, self-motivation, technology, and parental involvement.

Research in this field of psychology is vital for understanding and optimizing learning processes. It informs educators about cognitive development, learning styles, and effective teaching strategies.

Here are some example research questions:

46. Are different teaching styles more beneficial for children at different times of the day?

47. Can listening to classical music regularly increase a student’s test scores?

48. Is there a connection between sugar consumption and knowledge retention in students?

49. Does sleep duration and quality impact academic performance?

50. Does daily meditation at school influence students’ academic performance and mental health?

Sports psychology research question examples

Sport psychology aims to optimize physical performance and well-being in athletes by using cognitive and behavioral practices and interventions. Some methods include counseling, training, and clinical interventions.

Research in this area is important because it can improve team and individual performance, resilience, motivation, confidence, and overall well-being

Here are some research question ideas for you to consider:

51. How can a famous coach affect a team’s performance?

52. How can athletes control negative emotions in violent or high-contact sports?

53. How does using social media impact an athlete’s performance and well-being?

54. Can psychological interventions help with injury rehabilitation?

55. How can mindfulness practices boost sports performance?

Cultural psychology research question examples

The premise of this branch of psychology is that mind and culture are inseparable. In other words, people are shaped by their cultures, and their cultures are shaped by them. This can be a complex interaction.

Cultural psychology is vital as it explores how cultural context shapes individuals’ thoughts, behaviors, and perceptions. It provides insights into diverse perspectives, promoting cross-cultural understanding and reducing biases.

Here are some ideas that you might consider researching:

56. Are there cultural differences in how people perceive and deal with pain?

57. Are different cultures at increased risk of developing mental health conditions?

58. Are there cultural differences in coping strategies for stress?

59. Do our different cultures shape our personalities?

60. How does multi-generational culture influence family values and structure?

Health psychology research question examples

Health psychology is a crucial field of study. Understanding how psychological factors influence health behaviors, adherence to medical treatments, and overall wellness enables health experts to develop effective interventions and preventive measures, ultimately improving health outcomes.

Health psychology also aids in managing stress, promoting healthy behaviors, and optimizing mental health, fostering a holistic approach to well-being.

Here are five ideas to inspire research in this field:

61. How can health psychology interventions improve lifestyle behaviors to prevent cardiovascular diseases?

62. What role do social norms play in vaping among adolescents?

63. What role do personality traits play in the development and management of chronic pain conditions?

64. How do cultural beliefs and attitudes influence health-seeking behaviors in diverse populations?

65. What are the psychological factors influencing the adherence to preventive health behaviors, such as vaccination and regular screenings?

Neuropsychology research paper question examples

Neuropsychology research explores how a person’s cognition and behavior are related to their brain and nervous system. Researchers aim to advance the diagnosis and treatment of behavioral and cognitive effects of neurological disorders.

Researchers may work with children facing learning or developmental challenges, or with adults with declining cognitive abilities. They may also focus on injuries or illnesses of the brain, such as traumatic brain injuries, to determine the effect on cognitive and behavioral functions.

Neuropsychology informs diagnosis and treatment strategies for conditions such as dementia, traumatic brain injuries, and psychiatric disorders. Understanding the neural basis of behavior enhances our ability to optimize cognitive functioning, rehabilitate people with brain injuries, and improve patient care.

Here are some example research questions to consider:

66. How do neurotransmitter imbalances in specific brain regions contribute to mood disorders such as depression?

67. How can a traumatic brain injury affect memory?

68. What neural processes underlie attention deficits in people with ADHD?

69. Do medications affect the brain differently after a traumatic brain injury?

70. What are the behavioral effects of prolonged brain swelling?

Psychology of religion research question examples

The psychology of religion is a field that studies the interplay between belief systems, spirituality, and mental well-being. It explores the application of the psychological methods and interpretive frameworks of religious traditions and how they relate to both religious and non-religious people.

Psychology of religion research contributes to a holistic understanding of human experiences. It fosters cultural competence and guides therapeutic approaches that respect diverse spiritual beliefs.

Here are some example research questions in this field:

71. What impact does a religious upbringing have on a child’s self-esteem?

72. How do religious beliefs shape decision-making and perceptions of morality?

73. What is the impact of religious indoctrination?

74. Is there correlation between religious and mindfulness practices?

75. How does religious affiliation impact attitudes towards mental health treatment and help-seeking behaviors?

Controversial topics in psychology research question examples

Some psychology topics don’t fit into any of the subcategories above, but they may still be worthwhile topics to consider. These topics are the ones that spark interest, conversation, debate, and disagreement. They are often inspired by current issues and assess the validity of older research.

Consider some of these research question examples:

76. How does the rise in on-screen violence impact behavior in adolescents.

77. Should access to social media platforms be restricted in children under the age of 12 to improve mental health?

78. Are prescription mental health medications over-prescribed in older adults? If so, what are the effects of this?

79. Cognitive biases in AI: what are the implications for decision-making?

80. What are the psychological and ethical implications of using virtual reality in exposure therapy for treating trauma-related conditions?

• Inspiration for your next psychology research project

You can choose from a diverse range of research questions that intersect and overlap across various specialties.

From cognitive psychology to clinical studies, each inquiry contributes to a deeper understanding of the human mind and behavior. Importantly, the relevance of these questions transcends individual disciplines, as many findings offer insights applicable across multiple areas of study.

As health trends evolve and societal needs shift, new topics emerge, fueling continual exploration and discovery. Diving into this ever-changing and expanding area of study enables you to navigate the complexities of the human experience and pave the way for innovative solutions to the challenges of tomorrow.

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415 Research Question Examples Across 15 Disciplines

A research question is a clearly formulated query that delineates the scope and direction of an investigation. It serves as the guiding light for scholars, helping them to dissect, analyze, and comprehend complex phenomena. Beyond merely seeking answers, a well-crafted research question ensures that the exploration remains focused and goal-oriented.

The significance of framing a clear, concise, and researchable question cannot be overstated. A well-defined question not only clarifies the objective of the research but also determines the methodologies and tools a researcher will employ. A concise question ensures precision, eliminating the potential for ambiguity or misinterpretation. Furthermore, the question must be researchable—posing a question that is too broad, too subjective, or unanswerable can lead to inconclusive results or an endless loop of investigation. In essence, the foundation of any meaningful academic endeavor rests on the articulation of a compelling and achievable research question.

Research questions can be categorized based on their intent and the nature of the information they seek. Recognizing the different types is essential for crafting an effective inquiry and guiding the research process. Let's delve into the various categories:

• Descriptive Research Questions: These types of questions aim to outline and characterize specific phenomena or attributes. They seek to provide a clear picture of a situation or context without necessarily diving into causal relationships. For instance, a question like "What are the main symptoms of the flu?" is descriptive as it seeks to list the symptoms.
• Explanatory (or Causal) Research Questions: Explanatory questions delve deeper, trying to uncover the reasons or causes behind certain phenomena. They are particularly common in experimental research where researchers are attempting to establish cause-and-effect relationships. An example might be, "Does smoking increase the risk of lung cancer?"
• Exploratory Research Questions: As the name suggests, these questions are used when researchers are entering uncharted territories. They are designed to gather preliminary information on topics that haven't been studied extensively. A question like "How do emerging technologies impact remote tribal communities?" can be seen as exploratory if there's limited existing research on the topic.
• Comparative Research Questions: These questions are formulated when the objective is to compare two or more groups, conditions, or variables. Comparative questions might look like "How do test scores differ between students who study regularly and those who cram?"
• Predictive Research Questions: The goal here is to forecast or predict potential outcomes based on certain variables or conditions. Predictive research might pose questions such as "Based on current climate trends, how will average global temperatures change by 2050?"

Here are examples of research questions across various disciplines, shedding light on queries that stimulate intellectual curiosity and advancement. In this post, we will delve into disciplines ranging from the Natural Sciences, such as Physics and Biology, to the Social Sciences, including Sociology and Anthropology, as well as the Humanities, like Literature and Philosophy. We'll also explore questions from fields as varied as Health Sciences, Engineering, Business, Environmental Sciences, Mathematics, Education, Law, Agriculture, Arts, Computer Science, Architecture, and Languages. This comprehensive overview aims to illustrate the breadth and depth of inquiries that shape our world of knowledge.

Agriculture and forestry examples

Architecture and planning examples, arts and design examples, business and finance examples, computer science and informatics examples, education examples, engineering and technology examples, environmental sciences examples, health sciences examples, humanities examples, languages and linguistics examples, law examples, mathematics and statistics examples, natural sciences examples, social sciences examples.

• Descriptive: What are the primary factors that influence crop yield in temperate climates?
• Explanatory: Why do certain soil types yield higher grain production than others?
• Exploratory: How might new organic farming techniques influence soil health over a decade?
• Comparative: How do the growth rates differ between genetically modified and traditional corn crops?
• Predictive: Based on current climate models, how will changing rain patterns impact wheat production in the next 20 years?

Animal science

• Descriptive: What are the common behavioral traits of domesticated cattle in grass-fed conditions?
• Explanatory: Why do certain breeds of chickens have a higher egg production rate?
• Exploratory: What potential benefits could arise from integrating tech wearables in livestock management?
• Comparative: How does the milk yield differ between Holstein and Jersey cows when given the same diet?
• Predictive: How might increasing global temperatures influence the reproductive cycles of swine?

Aquaculture

• Descriptive: What are the most commonly farmed fish species in Southeast Asia?
• Explanatory: Why do shrimp farms have a higher disease outbreak rate compared to fish farms?
• Exploratory: How might innovative recirculating aquaculture systems revolutionize the industry's environmental impact?
• Comparative: How do growth rates of salmon differ between open-net pens and land-based tanks?
• Predictive: What will be the impact of ocean acidification on mollusk farming over the next three decades?
• Descriptive: What tree species dominate the temperate rainforests of North America?
• Explanatory: Why are certain tree species more resistant to pest infestations?
• Exploratory: What are the potential benefits of integrating drone technology in forest health monitoring?
• Comparative: How do deforestation rates compare between legally protected and unprotected areas in the Amazon?
• Predictive: Given increasing global demand for timber, how might tree populations in Siberia change in the next half-century?

Horticulture

• Descriptive: What are the common characteristics of plants suitable for urban vertical farming?
• Explanatory: Why do roses require specific pH levels in the soil for optimal growth?
• Exploratory: What potential methods might promote year-round vegetable farming in colder regions?
• Comparative: How does fruit yield differ between traditionally planted orchards and high-density planting systems?
• Predictive: How might changing global temperatures affect wine grape production in traditional regions?

Soil science

• Descriptive: What are the main components of loamy soil?
• Explanatory: Why does clay-rich soil retain more water compared to sandy soil?
• Exploratory: How might biochar applications transform nutrient availability in degraded soils?
• Comparative: How do nutrient levels vary between soils managed with organic versus inorganic fertilizers?
• Predictive: Based on current farming practices, how will soil quality in the Midwest U.S. evolve over the next 30 years?

Architectural design

• Descriptive: What are the dominant architectural styles of public buildings constructed in the 21st century?
• Explanatory: Why do certain architectural elements from classical periods continue to influence modern designs?
• Exploratory: How might sustainable materials revolutionize the future of architectural design?
• Comparative: How do energy consumption levels differ between buildings with passive design elements and those without?
• Predictive: Based on urbanization trends, how will the design of residential buildings evolve in the next two decades?

Landscape architecture

• Descriptive: What are the primary components of a successful urban park design?
• Explanatory: Why do certain types of vegetation promote greater biodiversity in urban settings?
• Exploratory: What innovative techniques can be employed to restore and integrate wetlands into urban landscapes?
• Comparative: How does visitor satisfaction vary between nature-inspired landscapes and more structured, geometric designs?
• Predictive: With the effects of climate change, how might coastal landscape architecture adapt to rising sea levels over the coming century?

Urban planning

• Descriptive: What are the main components of a pedestrian-friendly city center?
• Explanatory: Why do certain urban layouts promote more efficient traffic flow than others?
• Exploratory: How might the integration of vertical farming impact urban food security and cityscape aesthetics?
• Comparative: How do the air quality levels differ between cities with green belts and those without?
• Predictive: Based on increasing telecommuting trends, how will urban planning strategies adjust to potentially reduced daily commutes in the future?

Graphic design

• Descriptive: What are the prevailing typography trends in modern branding?
• Explanatory: Why do certain color schemes evoke specific emotions or perceptions in consumers?
• Exploratory: How is augmented reality reshaping the landscape of interactive graphic design?
• Comparative: How do print and digital designs differ in terms of elements and principles when targeting a young adult audience?
• Predictive: Based on evolving digital platforms, what are potential future trends in web design aesthetics?

Industrial design

• Descriptive: What characterizes the ergonomic features of leading office chairs in the market?
• Explanatory: Why have minimalist designs become more prevalent in consumer electronics over the past decade?
• Exploratory: How might bio-inspired design influence the future of transportation vehicles?
• Comparative: How does user satisfaction differ between traditional versus modular product designs?
• Predictive: Given the push towards sustainability, how will material selection evolve in the next decade of product design?

Multimedia arts

• Descriptive: What techniques define the most popular virtual reality (VR) experiences currently available?
• Explanatory: Why do certain sound designs enhance immersion in video games more effectively than others?
• Exploratory: How might holographic technologies revolutionize stage performances or public installations in the future?
• Comparative: How do user engagement levels differ between 2D animations and 3D animations in educational platforms?
• Predictive: With the rise of augmented reality (AR) wearables, what might be the next frontier in multimedia art installations?

Performing arts

• Descriptive: What styles of dance are currently predominant in global theater productions?
• Explanatory: Why do certain rhythms or beats universally resonate with audiences across cultures?
• Exploratory: How might digital avatars or AI entities play roles in future theatrical performances?
• Comparative: How does audience reception differ between traditional plays and experimental, interactive performances?
• Predictive: Considering global digitalization, how might virtual theaters redefine the experience of live performances in the future?

Visual arts

• Descriptive: What themes are prevalent in contemporary art exhibitions worldwide?
• Explanatory: Why have mixed media installations gained prominence in the 21st-century art scene?
• Exploratory: How is the intersection of technology and art opening new mediums or platforms for artists?
• Comparative: How do traditional painting techniques, such as oil and watercolor, contrast in terms of texture and luminosity?
• Predictive: With the evolution of digital art platforms, how might the definition and appreciation of "original" artworks change in the coming years?

Entrepreneurship

• Descriptive: What are the main challenges faced by startups in the tech industry?
• Explanatory: Why do some entrepreneurial ventures succeed while others fail within their first five years?
• Exploratory: How are emerging digital platforms reshaping the entrepreneurial landscape?
• Comparative: How do funding opportunities for entrepreneurs differ between North America and Europe?
• Predictive: What sectors are predicted to see the most startup growth in the next decade?
• Descriptive: What are the primary sources of external funding for large corporations?
• Explanatory: Why did the stock market experience a significant drop in Q4 2022?
• Exploratory: How might blockchain technology revolutionize the future of banking?
• Comparative: How do the financial markets in developing countries compare to those in developed countries?
• Predictive: Based on current economic indicators, what is the forecasted health of the global economy for the next five years?

Human resources

• Descriptive: What are the most sought-after employee benefits in the tech industry?
• Explanatory: Why is there a high turnover rate in the retail sector?
• Exploratory: How might the rise of remote work affect HR practices in the next decade?
• Comparative: How do HR practices in multinational corporations differ from those in local companies?
• Predictive: What skills will be in highest demand in the workforce by 2030?
• Descriptive: What are the core responsibilities of middle management in large manufacturing firms?
• Explanatory: Why do some management strategies fail in diverse cultural environments?
• Exploratory: How are companies adapting their management structures in response to the gig economy?
• Comparative: How does management style in Eastern companies compare with Western businesses?
• Predictive: How might artificial intelligence reshape management practices in the next decade?
• Descriptive: What are the most effective digital marketing channels for e-commerce businesses?
• Explanatory: Why did a particular viral marketing campaign succeed in reaching a global audience?
• Exploratory: How might virtual reality change the landscape of product advertising?
• Comparative: How do marketing strategies differ between B2B and B2C sectors?
• Predictive: What consumer behaviors are forecasted to dominate online shopping trends in the next five years?

Operations research

• Descriptive: What are the primary optimization techniques used in supply chain management?
• Explanatory: Why do certain optimization algorithms perform better in specific industries?
• Exploratory: How can quantum computing impact the future of operations research?
• Comparative: How does operations strategy differ between service and manufacturing industries?
• Predictive: Based on current technological advancements, how might automation reshape supply chain strategies by 2035?

Artificial intelligence

• Descriptive: What are the primary algorithms used in deep learning?
• Explanatory: Why do certain neural network architectures outperform others in image recognition tasks?
• Exploratory: How might quantum computing influence the development of AI models?
• Comparative: How do reinforcement learning methods compare to supervised learning in game playing scenarios?
• Predictive: Based on current trends, how will AI impact the job market over the next decade?

Cybersecurity

• Descriptive: What are the most common types of cyberattacks reported in 2022?
• Explanatory: Why are certain industries more vulnerable to ransomware attacks?
• Exploratory: How might advances in quantum computing challenge existing encryption methods?
• Comparative: How do open-source software vulnerabilities compare to those in proprietary systems?
• Predictive: Given emerging technologies, what types of cyber threats will likely dominate in the next five years?

Data science

• Descriptive: What are the main tools used by data scientists in large-scale data analysis?
• Explanatory: Why does algorithm X yield more accurate predictions than algorithm Y for certain datasets?
• Exploratory: How can machine learning models improve real-time data processing in IoT devices?
• Comparative: How does the performance of traditional statistical models compare to machine learning models in predicting stock prices?
• Predictive: Based on current data trends, what industries will likely benefit the most from data analytics advancements in the coming decade?

Information systems

• Descriptive: What are the core components of a modern enterprise resource planning (ERP) system?
• Explanatory: Why have cloud-based information systems seen a rapid adoption rate in recent years?
• Exploratory: How might the integration of blockchain technology revolutionize supply chain information systems?
• Comparative: How do information system strategies differ between e-commerce and brick-and-mortar retailers?
• Predictive: Given the rise of remote work, how will information systems evolve to support decentralized teams in the future?

Software engineering

• Descriptive: What are the standard practices in agile software development?
• Explanatory: Why do some software projects face significant delays despite rigorous planning?
• Exploratory: How are emerging programming languages shaping the future of software development?
• Comparative: How does the software development lifecycle in startup environments compare to that in large corporations?
• Predictive: Based on current development trends, which software platforms are forecasted to dominate market share by 2030?

Adult education

• Descriptive: What are the primary motivations behind adults seeking further education later in life?
• Explanatory: Why do some adult education programs have a higher success rate compared to others?
• Exploratory: How might online learning platforms revolutionize adult education in the next decade?
• Comparative: How do adult education methodologies differ from traditional collegiate teaching techniques?
• Predictive: Given current trends, how will the demand for adult education courses change in the upcoming years?

Curriculum studies

• Descriptive: What are the core components of a modern high school curriculum in the United States?
• Explanatory: Why have certain subjects, like financial literacy, become more emphasized in recent curriculum updates?
• Exploratory: How can interdisciplinary studies be better incorporated into traditional curricula?
• Comparative: How does the math curriculum in the US compare to that in other developed countries?
• Predictive: Based on pedagogical research, what subjects are forecasted to gain prominence in curricula over the next decade?

Educational administration

• Descriptive: What are the main responsibilities of a school principal in large urban schools?
• Explanatory: Why do some schools consistently perform better in standardized testing than others, despite similar resources?
• Exploratory: How might emerging technologies shape the administrative tasks of educational institutions in the future?
• Comparative: How does school administration differ between private and public educational institutions?
• Predictive: Given the rise of online education, how will the role of educational administrators evolve in the coming years?

Educational psychology

• Descriptive: What cognitive strategies are commonly used by students to enhance memory retention during studies?
• Explanatory: Why do certain teaching methodologies resonate better with students having specific learning styles?
• Exploratory: How can insights from behavioral psychology improve student engagement in virtual classrooms?
• Comparative: How does the motivation level of students differ between self-paced versus instructor-led courses?
• Predictive: With the increasing integration of technology in education, how will student learning behaviors change in the next decade?

Special education

• Descriptive: What interventions are commonly used to support students with autism spectrum disorders in inclusive classrooms?
• Explanatory: Why do some special education programs yield better academic outcomes for students with specific learning disabilities?
• Exploratory: How can augmented reality technologies be utilized to enhance learning for students with visual impairments?
• Comparative: How does special education support differ between urban and rural school districts?
• Predictive: Based on advancements in assistive technologies, how will the landscape of special education transform in the near future?

Aerospace engineering

• Descriptive: What are the key materials and technologies utilized in modern spacecraft design?
• Explanatory: Why are certain alloys preferred in high-temperature aerospace applications?
• Exploratory: How might advances in propulsion technologies revolutionize space travel in the next decade?
• Comparative: How do commercial aircraft designs differ from military aircraft designs in terms of aerodynamics?
• Predictive: Given current research trends, how will the efficiency of jet engines change in the upcoming years?

Biomedical engineering

• Descriptive: What are the foundational principles behind the design of modern prosthetic limbs?
• Explanatory: Why have bio-compatible materials like titanium become crucial in implantable medical devices?
• Exploratory: How can nanotechnology be leveraged to improve drug delivery systems in the future?
• Comparative: How do MRI machines differ from CT scanners in terms of their underlying technology and application?
• Predictive: Based on emerging trends, how will wearable health monitors evolve in the next decade?

Chemical engineering

• Descriptive: What processes are involved in the large-scale production of ethylene?
• Explanatory: Why is distillation the most common separation method in the petroleum industry?
• Exploratory: How might green chemistry principles transform traditional chemical manufacturing processes?
• Comparative: How does the production of biofuels compare to traditional fossil fuels in terms of yield and environmental impact?
• Predictive: Given global sustainability goals, how will the chemical industry's reliance on fossil resources shift in the future?

Civil engineering

• Descriptive: What are the primary considerations in the structural design of skyscrapers in earthquake-prone regions?
• Explanatory: Why are steel-reinforced concrete beams commonly used in bridge construction?
• Exploratory: How can smart city concepts influence the infrastructure planning of urban centers in the future?
• Comparative: How do tunneling methods differ between soft soil and hard rock terrains?
• Predictive: With the increasing threat of climate change, how will coastal infrastructure design criteria change to account for rising sea levels?

Computer engineering

• Descriptive: What are the main components of a modern central processing unit (CPU) and their functions?
• Explanatory: Why is silicon predominantly used in semiconductor manufacturing?
• Exploratory: How might quantum computing redefine the landscape of traditional computing architectures?
• Comparative: How do solid-state drives (SSDs) compare to traditional hard disk drives (HDDs) in terms of performance and longevity?
• Predictive: Given advancements in chip miniaturization, how will the form factor of consumer electronics evolve in the coming years?

Electrical engineering

• Descriptive: What are the standard stages involved in the transmission and distribution of electrical power?
• Explanatory: Why are transformers essential in the power distribution network?
• Exploratory: How can emerging smart grid technologies improve the efficiency and reliability of electrical distribution systems?
• Comparative: How do AC and DC transmission methods differ in terms of efficiency and infrastructure requirements?
• Predictive: With the rise of renewable energy sources, how will power grid management complexities change in the next decade?

Mechanical engineering

• Descriptive: What are the fundamental principles behind the operation of a four-stroke internal combustion engine?
• Explanatory: Why are certain polymers used as vibration dampeners in machinery?
• Exploratory: How might advancements in materials science impact the design of future automotive systems?
• Comparative: How do hydraulic systems compare to pneumatic systems in terms of energy efficiency and application?
• Predictive: With the push towards sustainability, how will traditional manufacturing methods evolve to reduce their carbon footprint?

Climatology

• Descriptive: What are the primary factors that influence the El Niño and La Niña phenomena?
• Explanatory: Why have certain regions experienced more intense and frequent heatwaves in the past decade?
• Exploratory: How might changing atmospheric CO2 concentrations impact global wind patterns in the future?
• Comparative: How do urban areas differ from rural areas in terms of microclimate conditions?
• Predictive: Given current greenhouse gas emission trends, what will be the average global temperature increase by the end of the century?

Conservation science

• Descriptive: What are the primary threats faced by tropical rainforests around the world?
• Explanatory: Why are certain species more vulnerable to habitat fragmentation than others?
• Exploratory: How can community involvement enhance conservation efforts in protected areas?
• Comparative: How does the effectiveness of in-situ conservation compare to ex-situ conservation for endangered species?
• Predictive: If current deforestation rates continue, how many species are predicted to go extinct in the next 50 years?
• Descriptive: What are the dominant flora and fauna in a temperate deciduous forest biome?
• Explanatory: Why do certain ecosystems, like wetlands, have higher biodiversity than others?
• Exploratory: How might the spread of invasive species alter nutrient cycling in freshwater lakes?
• Comparative: How do the trophic dynamics of grassland ecosystems differ from those of desert ecosystems?
• Predictive: How will global ecosystems change if bee populations continue to decline at current rates?

Environmental health

• Descriptive: What are the major pollutants found in urban air?
• Explanatory: Why do certain pollutants cause respiratory diseases in humans?
• Exploratory: How might green building designs reduce the health risks associated with indoor air pollutants?
• Comparative: How do the health impacts of living near coal-fired power plants compare to living near nuclear power plants?
• Predictive: Given increasing urbanization trends, how will air quality in major cities change over the next two decades?

Marine biology

• Descriptive: What are the primary species that comprise a coral reef ecosystem?
• Explanatory: Why are coral reefs particularly sensitive to changes in sea temperature?
• Exploratory: How might deep-sea exploration reveal unknown marine species and their adaptations?
• Comparative: How do the feeding strategies of pelagic fish differ from benthic fish in oceanic ecosystems?
• Predictive: If ocean acidification trends continue, what will be the impact on shell-forming marine organisms in the next 30 years?
• Descriptive: What are the most common oral health issues faced by elderly individuals?
• Explanatory: Why do sugary foods lead to a higher prevalence of cavities?
• Exploratory: How might emerging technologies revolutionize dental procedures in the coming decade?
• Comparative: How do the effects of electric toothbrushes compare to manual ones in reducing plaque?
• Predictive: Given current trends, how might the prevalence of gum diseases change in populations with increased sugar consumption over the next decade?

Kinesiology

• Descriptive: What are the primary physiological changes that occur during aerobic exercise?
• Explanatory: Why do some athletes experience muscle cramps during extensive physical activity?
• Exploratory: How might different stretching routines impact athletic performance?
• Comparative: How do the biomechanics of running on a treadmill differ from running outdoors?
• Predictive: If sedentary lifestyles continue to rise, what could be the potential impact on musculoskeletal health in the next 20 years?
• Descriptive: What are the main symptoms associated with the early stages of Parkinson's disease?
• Explanatory: Why are some viruses, like the flu, more prevalent in colder months?
• Exploratory: How might genetic editing technologies, like CRISPR, be utilized to treat hereditary diseases in the future?
• Comparative: How does the efficacy of traditional chemotherapy compare to targeted therapy in treating certain cancers?
• Predictive: Given advances in telemedicine, how might patient-doctor interactions evolve over the next decade?
• Descriptive: What are the primary responsibilities of nurses in intensive care units?
• Explanatory: Why is there a higher burnout rate among nurses compared to other healthcare professionals?
• Exploratory: How can training programs be improved to better equip nurses for challenges in emergency situations?
• Comparative: How does the patient recovery rate differ when cared for by specialized nurses versus general ward nurses?
• Predictive: How will the role of nurses change with the integration of more AI-based diagnostic tools in hospitals?
• Descriptive: What are the main nutritional components of a Mediterranean diet?
• Explanatory: Why does a diet high in processed sugars lead to increased risks of type 2 diabetes?
• Exploratory: How might gut microbiota be influenced by various diets and what are the potential health implications?
• Comparative: How does the nutritional profile of plant-based proteins compare to animal-based proteins?
• Predictive: If global meat consumption trends continue, what could be the implications for population-wide nutritional health in 30 years?
• Descriptive: What are the primary active ingredients in over-the-counter pain relievers?
• Explanatory: Why do certain medications cause drowsiness as a side effect?
• Exploratory: How might nanoparticle-based drug delivery systems enhance the efficacy of certain treatments?
• Comparative: How do the effects of generic drugs compare to their brand-name counterparts?
• Predictive: Given the rise of antibiotic-resistant bacteria, how might pharmaceutical approaches to bacterial infections change in the future?

Public health

• Descriptive: What are the main factors contributing to public health disparities in urban vs rural areas?
• Explanatory: Why did certain regions have higher transmission rates during the COVID-19 pandemic?
• Exploratory: How can community engagement strategies be optimized for more effective health campaigns?
• Comparative: How do vaccination rates and outcomes differ between countries with public vs private healthcare systems?
• Predictive: Based on current trends, how will global public health challenges evolve over the next 50 years?

Art history

• Descriptive: What are the primary artistic styles observed in the Renaissance era?
• Explanatory: Why did the Baroque art movement emerge after the Renaissance?
• Exploratory: How might newly discovered ancient art pieces reshape our understanding of prehistoric artistic practices?
• Comparative: How does European Romantic art differ from Asian Romantic art of the same period?
• Predictive: Given current trends, how might digital art impact traditional art gallery setups in the next decade?
• Descriptive: What are the primary themes in Homer's "Odyssey"?
• Explanatory: Why did Greek tragedies place a strong emphasis on the concept of fate?
• Exploratory: Are there undiscovered works that might provide more insight into daily life in ancient Rome?
• Comparative: How do Roman epics compare to their Greek counterparts in terms of character development?
• Predictive: How will emerging technologies like virtual reality affect the study of ancient ruins?

Cultural studies

• Descriptive: How is the concept of family portrayed in contemporary American media?
• Explanatory: Why has the influence of Western culture grown in certain Eastern countries over the last century?
• Exploratory: What are the emerging subcultures in the digital age and how do they communicate?
• Comparative: How does the representation of masculinity vary between Eastern and Western films?
• Predictive: In what ways might globalization affect cultural identities in the next two decades?
• Descriptive: What events led to the fall of the Berlin Wall?
• Explanatory: Why did the Industrial Revolution begin in Britain?
• Exploratory: Are there undocumented civilizational interactions in ancient times that new archaeological findings might reveal?
• Comparative: How did the responses to the Black Plague differ between European and Asian nations?
• Predictive: Given historical patterns, how might major global powers react to dwindling natural resources in the future?
• Descriptive: What are the main narrative techniques used in James Joyce's "Ulysses"?
• Explanatory: Why did the Gothic novel become popular in 19th-century England?
• Exploratory: How might translations of ancient texts reveal different interpretations based on the translator's cultural background?
• Comparative: How does the portrayal of war differ between post-WWII American and French literature?
• Predictive: How might the rise of AI-authored literature change the publishing industry?
• Descriptive: What are the core principles of existentialism as described by Jean-Paul Sartre?
• Explanatory: Why did the philosophy of existentialism gain prominence post-WWII?
• Exploratory: How might ancient Eastern philosophies provide insights into modern ethical dilemmas surrounding technology?
• Comparative: How does Nietzsche's concept of the "Ubermensch" compare to Aristotle's "virtuous person"?
• Predictive: As AI becomes more prevalent, how might philosophical discussions around consciousness evolve?

Religious studies

• Descriptive: What are the Five Pillars of Islam?
• Explanatory: Why did Protestantism emerge within Christianity during the 16th century?
• Exploratory: Are there common motifs in creation myths across various religions?
• Comparative: How do concepts of the afterlife compare between Christianity, Buddhism, and Ancient Egyptian beliefs?
• Predictive: How might interfaith dialogue shape religious practices in multi-faith societies over the next decade?

Classic languages

• Descriptive: What are the primary grammatical structures in Ancient Greek?
• Explanatory: Why did Latin play a foundational role in the development of many modern European languages?
• Exploratory: Are there yet-to-be-deciphered scripts from ancient civilizations that might provide insight into lost languages?
• Comparative: How do the verb conjugation patterns in Latin compare to those in Sanskrit?
• Predictive: Given the ongoing research in classical studies, how might our understanding of certain ancient texts change in the next decade?

Comparative literature

• Descriptive: What are the main themes in Japanese Haiku and English Sonnets?
• Explanatory: Why do certain folklore tales appear with variations across different cultures?
• Exploratory: How might newly translated works from lesser-known languages reshape the world literature canon?
• Comparative: How does the role of the tragic hero in French literature differ from its portrayal in Russian literature?
• Predictive: As global communication becomes more interconnected, how might the study of world literature evolve in universities?

Modern languages

• Descriptive: What are the primary tonal patterns observed in Mandarin Chinese?
• Explanatory: Why has English become a dominant lingua franca in international business and diplomacy?
• Exploratory: Which lesser-studied languages might become more prominent due to socio-political changes in their regions?
• Comparative: How do the grammatical complexities of Russian compare to those of German?
• Predictive: Given current global trends, which languages are predicted to become more widely spoken in the next two decades?
• Descriptive: What are the primary articulatory features of plosive sounds?
• Explanatory: Why do certain accents develop specific pitch fluctuations and intonations?
• Exploratory: How do various environmental factors affect vocal cord vibrations and sound production?
• Comparative: How does the pronunciation of fricatives differ between Spanish and Portuguese speakers?
• Predictive: How might advancements in voice recognition technology influence phonetics research in the next decade?
• Descriptive: What are the primary signs and symbols used in American road signage?
• Explanatory: Why do red roses universally symbolize love or passion in many cultures?
• Exploratory: Are there emerging symbols in digital communication that could become universally recognized signs in the future?
• Comparative: How do the semiotic structures in print advertisements differ between Western and Eastern cultures?
• Predictive: As emoji usage becomes more widespread, how might they impact written language semantics in the coming years?
• Descriptive: What are the key statutes governing tenant rights in residential leases?
• Explanatory: Why do personal injury claims vary significantly in settlement amounts even under similar circumstances?
• Exploratory: How might alternative dispute resolution mechanisms evolve in civil law contexts over the next decade?
• Comparative: How do defamation laws differ between jurisdictions that adopt the British common law system versus the Napoleonic code?
• Predictive: How might the rise of online transactions affect the volume and nature of civil law cases related to contract disputes?

Constitutional law

• Descriptive: What are the main principles enshrined in the First Amendment of the U.S. Constitution?
• Explanatory: Why have some constitutional rights been subject to varying interpretations over time?
• Exploratory: Are there emerging debates around digital rights and freedoms that might reshape constitutional interpretations in the future?
• Comparative: How does the protection of freedom of speech differ between the U.S. Constitution and the German Basic Law?
• Predictive: Given global socio-political trends, how might constitutional democracies adjust their foundational texts in the next two decades?

Corporate law

• Descriptive: What are the primary duties and liabilities of a board of directors in a publicly traded company?
• Explanatory: Why do mergers and acquisitions often involve extensive due diligence processes?
• Exploratory: How might the rise of digital currencies impact the regulatory landscape for corporations in the finance sector?
• Comparative: How does the legal framework for shareholder rights in the U.S. compare to that of Japan?
• Predictive: How might changing global trade dynamics influence corporate structuring and international partnerships?

Criminal law

• Descriptive: What constitutes first-degree murder in the majority of jurisdictions?
• Explanatory: Why are certain offenses classified as misdemeanors while others are felonies?
• Exploratory: Are there emerging patterns in cybercrime that suggest new areas of legal vulnerability?
• Comparative: How does the treatment of juvenile offenders differ between Scandinavian countries and the U.S.?
• Predictive: Given advancements in technology, how might criminal law evolve to address potential misuses of artificial intelligence?

International law

• Descriptive: What are the foundational principles of the Geneva Conventions?
• Explanatory: Why have some nations refused to recognize or be bound by certain international treaties?
• Exploratory: How might global climate change reshape international agreements and treaties in the coming years?
• Comparative: How do regional trade agreements in Africa compare to those in Southeast Asia in terms of provisions and enforcement mechanisms?
• Predictive: How might geopolitical shifts influence the role and effectiveness of international courts in resolving state disputes?

Applied mathematics

• Descriptive: What are the primary mathematical models used to predict the spread of infectious diseases?
• Explanatory: Why does the Navier–Stokes equation play a pivotal role in fluid dynamics?
• Exploratory: How might new computational methods enhance the efficiency of existing algorithms in applied mathematics?
• Comparative: How do optimization techniques in operations research differ from those in machine learning applications?
• Predictive: Given the rapid growth of quantum computing, how might it reshape the landscape of applied mathematical problems in the next decade?

Applied statistics

• Descriptive: What are the standard procedures for handling missing data in a large-scale survey?
• Explanatory: Why do statisticians use bootstrapping techniques in hypothesis testing?
• Exploratory: How might emerging data sources, like wearables and IoT devices, introduce new challenges and opportunities in applied statistics?
• Comparative: How does the performance of Bayesian methods compare to frequentist methods in complex hierarchical models?
• Predictive: With the increasing availability of big data, how might the role of applied statisticians evolve in the next five years?

Pure mathematics

• Descriptive: What are the axioms underpinning Euclidean geometry?
• Explanatory: Why is Gödel's incompleteness theorem considered a foundational result in the philosophy of mathematics?
• Exploratory: Are there newly emerging areas of study within number theory due to advancements in computational mathematics?
• Comparative: How do algebraic structures differ between rings and fields?
• Predictive: Considering current research trends, what areas of pure mathematics are poised for significant breakthroughs in the next decade?

Theoretical statistics

• Descriptive: What foundational principles underlie the Central Limit Theorem?
• Explanatory: Why is the concept of sufficiency crucial in the design of statistical tests?
• Exploratory: How might advances in artificial intelligence influence theoretical developments in statistical inference?
• Comparative: How do likelihood-based inference methods compare to Bayesian methods in terms of theoretical underpinnings?
• Predictive: As data generation mechanisms evolve, how might the theoretical foundations of statistics need to adapt in the future?
• Descriptive: What are the key features and behaviors of black holes?
• Explanatory: Why does the expansion of the universe appear to be accelerating?
• Exploratory: What potential insights might the study of exoplanets provide about the conditions necessary for life?
• Comparative: How do the properties of spiral galaxies differ from those of elliptical galaxies?
• Predictive: Based on current data, what are the projected future behaviors of our sun as it ages?
• Descriptive: What are the primary functions and structures of ribosomes in a cell?
• Explanatory: Why does DNA replication occur semi-conservatively?
• Exploratory: How might emerging technologies like CRISPR redefine our understanding of genetic engineering?
• Comparative: How do the metabolic processes of prokaryotic cells differ from those of eukaryotic cells?
• Predictive: Given the current trajectory of climate change, how might the biodiversity in tropical rainforests be affected over the next century?
• Descriptive: What are the key properties and uses of the noble gases?
• Explanatory: Why do exothermic reactions release heat?
• Exploratory: How might advances in nanochemistry influence drug delivery systems?
• Comparative: How do ionic bonds differ in strength and characteristics from covalent bonds?
• Predictive: Considering the rise in antibiotic-resistant bacteria, how might the field of medicinal chemistry adapt to produce effective treatments in the future?

Earth science

• Descriptive: What are the primary layers of Earth's atmosphere and their respective characteristics?
• Explanatory: Why do certain regions experience more seismic activity than others?
• Exploratory: How might the study of ancient ice cores provide insights into past climate conditions?
• Comparative: How do the processes of weathering differ between arid and humid climates?
• Predictive: Given current data on deforestation, what could be its impact on global soil quality and erosion patterns over the next 50 years?
• Descriptive: What are the fundamental principles underlying quantum mechanics?
• Explanatory: Why does the speed of light in a vacuum remain constant regardless of the observer's frame of reference?
• Exploratory: How might studies in string theory reshape our understanding of the universe at the smallest scales?
• Comparative: How do the effects of general relativity contrast with predictions from Newtonian physics under extreme gravitational conditions?
• Predictive: With advancements in particle physics, what potential new particles or phenomena might be discovered in the next decade?

Anthropology

• Descriptive: What are the primary rituals and customs of the indigenous tribes of the Amazon?
• Explanatory: Why did the ancient Mayan civilization collapse?
• Exploratory: How might modern urbanization impact the preservation of ancient burial sites?
• Comparative: How do hunter-gatherer societies differ from agricultural societies in terms of social structures?
• Predictive: Given global trends, how might indigenous cultures evolve over the next century?

Communication

• Descriptive: What are the main modes of communication used by millennials compared to baby boomers?
• Explanatory: Why has the usage of social media platforms surged in the last two decades?
• Exploratory: How might advancements in virtual reality reshape interpersonal communication in the future?
• Comparative: How do written communication skills differ between those educated in traditional schools versus online schools?
• Predictive: How might the nature of journalism change with the rise of automated content generation?
• Descriptive: What are the primary components of a nation's gross domestic product (GDP)?
• Explanatory: Why did the economic recession of 2008 occur?
• Exploratory: How might the concept of universal basic income impact labor market dynamics?
• Comparative: How do free market economies differ from command economies in terms of resource allocation?
• Predictive: Based on current global economic trends, which industries are predicted to boom in the next decade?
• Descriptive: What are the geographical features of the Himalayan mountain range?
• Explanatory: Why do desert regions exist on the western coasts of continents, such as the Atacama in South America?
• Exploratory: How might rising sea levels reshape the world's coastlines over the next century?
• Comparative: How does urban planning in European cities differ from that in American cities?
• Predictive: Given current urbanization rates, which cities are poised to become megacities by 2050?

Political science

• Descriptive: What are the foundational principles of a parliamentary democracy?
• Explanatory: Why do certain nations adopt federal systems while others prefer unitary systems?
• Exploratory: How might the rise of populism influence global diplomatic relations in the 21st century?
• Comparative: How do the rights of citizens in liberal democracies differ from those in authoritarian regimes?
• Predictive: Based on current political trends, which nations might see significant shifts in governance models over the next two decades?
• Descriptive: What are the primary stages of cognitive development in children according to Piaget?
• Explanatory: Why do certain individuals develop phobias?
• Exploratory: How might emerging neuroscientific tools, like fMRI, alter our understanding of human emotions?
• Comparative: How do coping mechanisms differ between individuals with high resilience versus those with low resilience?
• Predictive: Given the rise in digital communication, how might human attention spans evolve in future generations?

Social work

• Descriptive: What are the core principles and practices in child protective services?
• Explanatory: Why do certain communities have higher rates of child neglect and abuse?
• Exploratory: How might the integration of artificial intelligence in social work affect decision-making in child welfare cases?
• Comparative: How do intervention strategies for substance abuse differ between urban and rural settings?
• Predictive: Based on current societal trends, what challenges might social workers face in the next decade?
• Descriptive: What are the defining characteristics of Generation Z as a social cohort?
• Explanatory: Why have nuclear families become less prevalent in Western societies?
• Exploratory: How might the widespread adoption of virtual realities impact social interactions and community structures in the future?
• Comparative: How do the roles and perceptions of elderly individuals differ between Eastern and Western societies?
• Predictive: Given the rise in remote work, how might urban and suburban living patterns change over the next three decades?

In synthesizing the vast range of research questions posed across diverse disciplines, it becomes clear that every academic field, from the humanities to the social sciences, offers unique perspectives and methodologies to uncover and understand various facets of our world. These questions, whether descriptive, explanatory, exploratory, comparative, or predictive, serve as guiding lights, driving scholarship and innovation. As academia continues to evolve and adapt, these inquiries not only define the boundaries of current knowledge but also pave the way for future discoveries and insights, emphasizing the invaluable role of continuous inquiry in the ever-evolving tapestry of human understanding.

Header image by Zetong Li .

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them. 

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive. 
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure. 

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Conclusion  

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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VanCleave's Science Fun

Your Guide to Science Projects, Fun Experiments, and Science Research

How to Write An Experimental Science Project Question

By Janice VanCleave

Building an Experimental Science Project Question

An experimental question is a cause-effect question.

Note: Things that can be changed or change on their own are called variables.

In an experimental question , the variable that causes another variable to change  is called the independent variable. The  variable that is affected (caused to change) is called the dependent variable

For example: In the question, “How does water affect plant growth?”

1. what has been affected? PLANT GROWTH 2. what is the cause of the plant growth? WATER

In the example experimental question, “How does water affect plant growth?,” the independent variable and dependent variable are too general.

Yes! There is one independent variable which is water, but the question needs to be more specific. Such as: 1. What is the source or kind of water? Tap water, distilled water, ocean water, lake water, etc…. 2. How much water? 3. What is the temperature of the water? Yes! There is one dependent variable which is plant growth, but the question needs to identify how this variable will be changed. Also, the question should identify the plant. Such as: 1. Changes in the height of a pinto bean plant? 2. Changes in the number of leaves on a pinto bean plant?

A good testable experimental question that identifies one specific independent variable and one specific dependent variable might be:

How does the amount of tap water affect the height of pinto bean seedlings ?

The question points out one specific cause– AMOUNT OF TAP WATER– and what it will be affecting– height of pinto bean seedlings.

More About Experimental Science Project Questions

The question, “How do plants grow toward a light?,” is not an experimental question that could easily be determined. How this happens occurs inside the plant resulting in the stems bending toward the light and it is not something you could easily discover by experimenting. While the question does identify an effect–plants growing toward a light-the question does not identify a cause nor does it identify the type of plant or light source.

You could discover how different things (called variables ) affect the plant’s growth toward light. In other words, the growth of the plant toward the light DEPENDS ON what variables?

Examples of variables that might CAUSE a plant’s growth toward light include,  sources of  light-sun, lamp, etc.., color of the light, distance of the plant from the light, direct or indirect light– barriers, etc….

INDEPENDENT VARIABLES : Things that MIGHT CAUSE  a plant to grow toward light.

DEPENDENT VARIABLE : A measurable effect of the independent variable. In other words, how to measure the results.

Following are examples of experimental science project questions:

1. What affect does the amount of sunlight have on the rate pinto bean seedlings grow toward the light?

2. How does the color of artificial light effect the rate pinto bean seedlings grow toward the light?

Plants: Mind Boggling Project Ideas

Why are plants green? * How does water move through a leaf? * What are the parts of a flower?

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143 Experiment Research Topics

Welcome to our collection of experimental research topics! Experiments are the cornerstone of empirical research, allowing scholars to test hypotheses and expand knowledge. With our experimental research questions ideas, you can uncover the diverse realms of empirical studies, from the natural sciences to social sciences and beyond.

🧪 7 Best Experimental Research Questions Ideas

🏆 best experimental research topics, 💡 simple experimental essay titles, 👍 catchy experimental research questions ideas, ❓ more experimental research questions ideas, 🎓 interesting experimental research topics.

• Bean Seed Germination Experiment Results
• Physical Health Indicator: Pulse Rate Experiment
• Static and Kinetic Friction: A Lab Experiment
• Experiment: Flame Test and Chemical Fingerprinting
• “Stanford Prison Experiment Ethics” by Philip Zimbardo
• Metal and Non-metal Redox Reactions Experiment
• Water Quality and Contamination Experiment Report
• Human Transport Systems: The Pulse Rate Experiment The report provides an analysis of the pulse rate experiment aimed at determining the pulse rates before and after a five-minute exercise conducted by the researcher.
• Hawthorne Experiments – Elton Mayo With Roethlisberger and Dickson The Hawthorne theories have brought about a positive change in the behavior and attitude of the managers as well as the workers.
• John Watson and the “Little Albert” Experiment John Watson is considered to be the founder of behaviorism, a psychological theory that focuses on visible behavior while diminishing the notion of consciousness.
• Putnam’s “Twin Earth” Thought-Experiment Throughout the history of analytic philosophy, the problem of meaning has been and remains one of its central themes.
• Why People Obey Authority: Milgram Experiment and Real-World Situation Human beings would obey authority depending on the overall rewards, potential personal gains, and the consequences of failing to do so.
• Ideal Gas Expansion Law: Experiment The purpose of the experiment was to understand the differences between different types of ideal gas expansions, paying attention to the amount of work done.
• Scientific Report Draft on Osmosis Egg Experiment Understanding how an egg reacts when placed in solutions of different concentrations enables one to understand the role of osmosis in the human body.
• Fiji Water Quality: Biology Lab Experiment Since Fiji water is among the popular brands in the US, it is essential to evaluate whether it is clean, that is, safe for human consumption.
• Experiment on Effect of Energy Drinks on Athletic Performance Experimental research is a study that a researcher sets up to evaluate a given situation, such as a drug or treatment intervention.
• Inductor-Capacitor-Resistor Circuit Experiment The article presents the experiment that will demonstrate the relationship between an inductor, voltmeter, and resistor in an inductor-capacitor-resistor (LCR) circuit.
• Unethical Research Experiments Violation of ethical principles can be traced in two analyzed cases; only in Landis’s experiment harm and killing were real in relation to animals.
• Air Pressure Experiment Methods and Results The plastic mesh fabric was placed over the mouth of the Mason jar, and the metal screw band of the latter was fastened firmly over the plastic mesh sheet.
• Kant’s Ethical Philosophy and Milgram’s Experiments The problem for Kant’s ethical philosophy is whether moral principles are applicable to nonhumans, such as Galacticans.
• Helicopter Experiment Assessment This report of a paper helicopter experiment involved designating a paper helicopter in varied designs and then dropping it severally while recording the flight time.
• Chemical Experiment on Enzyme Amylase This paper presents an experiment that was conducted to determine the activity of amylase on starch at various pH levels.
• Acoustics Experiment in Brunel’s Thames Tunnel In this project, tunnels that exist below London streets for a variety of communications, civil defense, and military purposes will be used as the objects of the experiment.
• Virtue Ethics in Stanford and Milgram’s Experiments This paper investigates the notion of virtue ethics, discussing two major studies, the Stanford prison experiment, and Milgram’s obedience studies.
• “Tuskegee Syphilis Experiment – The Deadly Deception”: Unethical Scientific Experiment “Tuskegee Syphilis Experiment – The Deadly Deception” reviews an unethical scientific experiment on humans that was conducted by White physicians on African-Americans.
• Archimedes’ Principle Experiment: Determining Gravity of Objects This paper describes an experiment that aims in determining the specific gravity of different metallic objects, a liquid, and wood.
• A Hypothesis and an Experiment: A Case Study On the control experiment, there would be a seed grown at normal aeration, and wind conditions. All should have a viable bean seed planted centrally on watered soil preferably.
• Ideal Experiment Design: Independent and Dependent Variables This work describes the ideal experiment, that is designed to verify the causal relationship between independent and dependent variables.
• Social Experiment: Informal Norms of Gender Issues The social experiment presents a contradiction between the socially-accepted norms and the understanding of equality between men and women.
• Milgram Experiment: The Question of Ethics This essay will discuss the Milgram experiment and also argue that it was ethical as medical research standards were met, and no undue harm to the participants was caused.
• An Observable Experiment: Control Over the Variables An observable experiment is defined as the experiment in which the independent variables cannot possibly be controlled by the person or person setting the test.
• Bolted & Welded Connections and Tension Experiment Exploring and comparing the expected and actual failure modes of both bottled and welded connections in tension are the primary purposes of the paper.
• Osmosis Experiment With Parsnip Through Differing Concentration of Sucrose
• Identifying the Benefits of Home Ownership: A Swedish Experiment
• Experiment for Cancer Risk Factors
• Hydrochloric Acid Into Tubes of Water and Sodium Thiosulphate Experiment
• General Information about Monkey Drug Trials Experiment
• Reaction Rates Experiment Hydrochloric Acid
• Hydrochloric Acid and Marble Chips Experiment
• Physical Disability and Labor Market Discrimination: Evidence From a Field Experiment
• Canadian Advanced Nanosatellite Experiment Biology
• Dr. Heidegger’s Experiment: Reality or Illusion
• Experiment and Multi-Grid Modeling of Evacuation From a Classroom
• High-Performance Liquid Chromatography Experiment
• Social Capital and Contributions in a Public-Goods Experiment
• Illusory Gains From Chile’s Targeted School Voucher Experiment
• Short Selling and Earnings Management: A Controlled Experiment
• Theft and Rural Poverty: Results of a Natural Experiment
• Lab Experiment: The Effectiveness of Different Antibiotics on Bacteria
• Brucellosis and Its Treatment: Experiment With Doxycycline
• The Link Between Stanford Prison Experiment and Milgram Study
• Four Fundamental Results From the Mice Experiment
• Metrology Experiment with Measurement Tools The experiment concerned testing the efficacy of the measurement tools such as the Vernier caliper, a depth gauge, a micrometer, and a gauge in an uncertainty analysis.
• The Stanford Prison Experiment The Stanford prison experiment is an example of how outside social situations influence changes in thought and behavior among humans.
• Miles Davis and Steve Reich: Geniuses of Experiments and Creativity Although Miles Davis’ and Steve Reich’s music belongs to different genres, they are connected in their constant search for a new sound by experimenting and improvising.
• Lab Experiment on Animals’ Taste or Smell Senses The hypothesis of the study is that taste perception and detection of different sugars by insects were similar to that of humans.
• Medical Pharmacology: The Langendorff Experiment The Langendorff experiment aimed at using an ex vivo isolated rat heart preparation to demonstrate the pharmacological effects of two unknown drugs.
• Studying Organisations: The Hawthorne Experiments The Hawthorn experiments marked a new direction in research of motivation and productivity. More than half a century has passed, and productivity remains a concern of management.
• Pasture Experiment: Fertiliser Treatments Response This work is an experiment that defines the role of fertilizers in pasture production and to establish the appropriate use of pasture sampling to assess pasture mass.
• Scientific Experiments on Animals from Ethical Perspectives This paper discusses using animals in scientific experiments from the consequentialist, Kantian deontological and Donna Yarri’s Christian character-based perspectives.
• Conducting a Titration Experiment Titration studies are conducted to quantify the amount of an unidentified element in the sample using a methodological approach.
• Thought Experiment: The Morality of Human Actions A thought experiment aimed at assessing the morality of human actions motivated by divine punishment or reward raises the question of morality and religion correlation.
• Ethical Implications of the Early Studies in Psychology: Milgram’s Experiment Milgram’s experiment on obedience content and results are valuable for understanding the ethical issues that may occur in social and behavioral research.
• Should Animals Be Used for Scientific Experiments? Unfortunately, at the moment, the use of animals in science and medicine cannot be excluded entirely. However, it is possible to conduct experiments using mathematical models.
• Stanford Prison Experiment: Behind the Mask Stanford Prison Experiment organized by Stanford researcher Philip Zimbardo led to a strong public response and still discussed today.
• Extraneous Variables in Experiments There are some variables in experiments besides the independent variables that usually cause a variation or a change to the dependent variables.
• The Use of Animals in Psychological Experiments The method of experimentation is of great significance for multiple fields of psychology, especially for the behaviorist branch.
• The Stanford Prison Experiment Analysis Abuse between guards and prisoners is an imminent factor attributed to the differential margin on duties and responsibilities.
• The Stanford Prison Experiment’s Historical Record The Stanford Prison Experiment is a seminal investigation into the dynamics of peer pressure in human psychology.
• Socioeconomic Status and Sentencing Severity Experiment There are two types of validity threats: external and internal. External validity refers to the degree to which the study can be applied to situations outside the research context.
• Psychology: Zimbardo Prison Experiment Despite all the horrors that contradict ethics, Zimbardo’s research contributed to the formation of social psychology. It was unethical to conduct this experiment.
• Post-Covid Adaptation Laboratory Experiment The goal of the laboratory experiment that this paper will outline is to test the hypothesis about the needs of senior citizens in the post-pandemic era.
• Psychology: Milgram Obedience Experiment Milgram’s experiment may be the last psychological experiment that has had a significant impact on psychology and public opinion.
• Predicting the Replicability of Social Science Lab Experiments The quality of work is the most significant factor for any academic organization. A research process for any scientific project requires careful evaluation of information sources.
• Moral Dilemma and Thought Experiments The aim of this essay is to set up a thought experiment in which a moral dilemma must be resolved. A person is invited to make a choice as a result of which people should suffer.
• Experiments in High-Frequency Trading High-frequency trading (HFT) is becoming increasingly popular with private businesses and traders. HFT allows traders to make transactions within fractions of seconds.
• The Ethical Issues in 1940’s U.S. Experiments With Syphilis in Guatemala The Guatemala tests have been viewed as a dark side of the U.S. clinical examination: in the 1940s, they purposely uncovered over 5,000 individuals with syphilis and gonorrhea.
• Isopods and Their Use in Experiments Isopod is a large family belonging to the crayfish order. The fact that isopods are good to use in various experiments is related to their habitat.
• Sociological Experiment: The Salience of Social Norms Based on the sociological experiment described in the paper, the author demonstrated the salience of social norms that exist in our culture.
• Blue-Eyed vs. Brown-Eyed Experiment Elliot exposed the learners to discrimination, in which blue-eyed children were initially preferred and given more privileges in the classroom than brown-eyed students.
• Experiment: Science Meets Real Life The experiment involves the sequential study of the dog’s behavior and its reaction to a change in some factors, such as food and bowl.
• P. Zimbardo’s Stanford Experiment A psychological experiment is an event conducted to acquire new scientific knowledge about psychology through the researcher’s deliberate intervention in the life of the examinee.
• Smoking: An Idea for a Statistical Experiment The hypothesis is that people who smoke cigarettes daily tend to earn more than others: this is a personal observation that requires careful experimental testing.
• The Stanford Jail Experiment Critiques One of the most important critiques leveled at the Stanford Jail Experiment is the length of time it took Zimbardo to call a halt to the experiment.
• Can Nonrandomized Experiments Yield Accurate Answers?
• What Kind of Experiments Are Done on Animals?
• Is It Good to Use Animals for Experiments?
• What Are the Types of Experiments?
• Is There Any Healthy Way to Experiment With Drugs?
• What Are the Top Experiments of All Time?
• Are Breaching Experiments Ethical?
• What Does It Mean to Experiment With a Drug?
• Why Do We Use Factorial Experiments?
• How Does Temperature Affect the Rate of Reaction Experiment?
• What Are the Easiest Experiments to Do?
• How Can Rushing Harm the Data and the Experiment Overall?
• What Are the Steps to a Science Experiment?
• How Do Errors Affect the Experiment?
• What Is the Purpose of the Wax Experiment and What Conclusion Does Descartes Reach on Its Basis?
• Can an Experiment Be Invalid but Reliable?
• What Is the Most Influential Experiment in Psychology?
• Why Are Fruit Flies Used for Experiments?
• How Can You Improve the Accuracy of an Experiment?
• What Was Galileo’s Famous Cannonball Drop Experiment?
• What Can Knowledge Be Gained From Conducting a Breaching Experiment?
• How Do You Identify the Independent and Dependent Variables in an Experiment?
• What Was Griffith’s Experiment and Why Was It Important?
• What Is the Difference Between Contingent Valuation and Choice Experiment?
• What Is the Choice Experiment Valuation Method?
• Super Size Me and Jogn Cisna Experiments In comparison to Super Size Me, the experiment of John Cisna immediately stands out with a positive attitude towards fast food.
• The Milgram Experiment: Ethical Issues The Milgram experiment is a controversial study on the subject of obedience to authority figures. The participants were asked to deliver electric shocks to other people.
• Health and Medicine: Experiments and Discussions In the first experiment, researchers tested the subjectivity of polygraph examiners’ assessments. The specialist was given a specific name before the test began to do it.
• An Experiment in DNA Cloning and Sequencing The aim of this experiment is to clone a fragment of DNA that includes the Green Fluorescent Protein (GFP) gene into the vector pTTQ18, which is an expression vector.
• An Enzyme Linked Immunosorbent Assay Experiment In our society presently, immunoassay techniques used in data analyses have assumed a place of high significance, particularly as it applies to pure/applied research.
• Anaerobic Threshold: An Experiment Anaerobic Threshold refers to the minimum level below which no increase in blood lactose can occur. At levels above AT, supplementing aerobic production needs aerobic energy.
• Comparative Effectiveness of Various Surfactants: Experiment Surfactants refer to chemical substances that lessen the surface tension in water. This experiment aimed at establishing the comparative effectiveness of various surfactants.
• An Experiment on Data Mining Extend This experiment aims to utilize knowledge and principles of data mining in depicting the investigation of emergent data in biology- particularly on the development of ELISAs.
• Lab Experiment on Photovoltaics The experiment was done specifically to ascertain how various connected units could be coordinated to give a more reliable and controllable functioning.
• Mind Control: Ethics of the Experiment The topics of mind control and free will has always been seen as a morally grey area in terms of its research potential.
• A Personal Behavior Modification Experiment Using Operant Conditioning This research paper points out the positive outcomes of swearing: it can relieve stress and help one cope with emotional work.
• Jane Elliott’s Experiment on Discrimination The teacher Jane Elliott from Iowa decided to conduct an experiment demonstrating to her students what discrimination is and what it feels like.
• The Tuskegee Syphilis Experiment When the Tuskegee Syphilis Experiment was begun, over 75 years ago, no such principles were officially in place.
• The Power of Conformity: Asch’s Experiments The article examines a series of experiments by Asch that helped him identify the factors influencing social conformity.
• The Critical Characteristics of an Experiment The main aim of this assignment is to evaluate the thought control experiment by famous psychologist Ellen Langer and determine whether it is a qualitative experiment.
• Ethical Analysis of the Tuskegee Syphilis Experiments The Tuskegee Syphilis Study failed to take into account several critical ethical considerations. This essay examines some of the ethical problems linked to the investigation.
• Boston’s Experiment: Harvard Business Review’s Lessons In Harvard Business Review’s Lessons from Boston’s Experiment with The One Fund, Mitchell discusses his experience with fund distribution to the victims of the Boston bombing.
• The Stanford Prison Experiment Review The video presents an experiment held in 1971. In general, a viewer can observe that people are subjected to behavior and opinion change when affected by others.
• The Way to Come To Terms With Yourself: Social Distancing Experiment In this work, the author describes the course and results of an experiment on social distance: refusal to use gadgets, any communication, and going out.
• Experiment: Bacteria vs Antibiotics The experiment aimed was to test the reaction of bacteria towards some antibiotics and determine the effectiveness of those antibiotics in treating some diseases.
• Ethics: Experiments on Animals Industrial and biomedical research is often painful and most of the test ends up killing the animals. Experiments such as these often incur the wrath of the animal rights movement.
• Impact of the Stanford Prison Experiment Have on Psychology This essay will begin with a brief description of Zimbardo’s Stanford Prison Experiment then it will move to explore two main issues that arose from the said experiment.
• Chemistry of Cooking. Saffron Rice Experiment This research project outlines an experiment that aims to determine the temperature at which Saffron rice turns yellow.
• Worldview Changes After the Blindness Experiment Our senses are the central source of information about the world and events that happen around us. So, the loss of one of these is a significant challenge for a person.
• Evaluation of the Stanford Prison Experiment’ Role The Stanford Prison Experiment is a study that was conducted on August 20, 1971 by a group of researchers headed by the psychology professor Philip Zimbardo.
• Social Experiment: Wrong Outfit in a Wedding Event The attendees of the wedding event displayed disappointment, discomfort, and open resentment towards the dressing style.
• Heat Transfer Rates in a Hot Jet: Experiment The experiment is aimed at determining the heat transfer rates in a hot jet. The reasons for the hot jet to have different heat rates in different areas will be determined.
• Inattentive Blindness in Psychological Experiment The features of the human consciousness not to notice quite obvious changes are natural and innate. Such blindness can be caused by several factors.
• Situation, Institutional Norms, and Roles: The Stanford Experiment of Zimbardo Philip Zimbardo’s Stanford Experiment brought him critical acclaim. At the same time, it accorded him a certain level of notoriety; because of the methodologies he utilized to conduct the experiment.
• Tuskegee Syphilis Experiment: Ethical Controversy Tuskegee case set the background for the reconsideration of healthcare ethics, which means that the ethical value of the given case deserves reconsideration.
• Gender Stereotyping Experiment: The Level of Gender Stereotyping in Society The present study measures the effects of stereotyping women. It examines the first impression formed by subjects based on the information about a fictitious man or a woman.
• Psychological Studies and Experiments: Code of Conduct The following paper is based on past psychological studies i.e. Stanly Milgram’s ‘Obedience Experiment’, Philip Zimbardo’s ‘Stanford Prison Experiment, and Jane Elliott’s ‘Class Divided’.
• Using Animals in Medical Experiments This paper explores how the principles of the character-based ethical approach can be applied to the discussion of using animals in the medical research and experiments.
• The Stanford Experiment by Philip Zimbardo Philip Zimbardo’s Stanford Experiment shows that situational power and norms dictate the behavior of the individual more than the core beliefs that made up his personal identity.

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These essay examples and topics on Experiment were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

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121+ Experimental Research Topics Across Different Disciplines

Experimental research is a cornerstone of scientific inquiry, providing a systematic approach to investigating phenomena and testing hypotheses. This method allows researchers to establish cause-and-effect relationships, contributing valuable insights to diverse fields.

In this blog post, we’ll delve into the world of experimental research topics, exploring their significance, ethical considerations, and providing a rich array of ideas spanning psychology, biology, physics, and education.

Definition and Importance of Experimental Research

Table of Contents

At its core, experimental research involves manipulating one or more variables to observe the effects on another variable, while controlling for extraneous influences. This method is crucial in establishing causation, distinguishing it from correlational studies that merely identify relationships between variables.

Experimental research holds immense importance across various disciplines. 

• In psychology, it helps unravel the complexities of human behavior, cognition, and social dynamics. 
• In biology, it uncovers the mysteries of genetics, ecology, and environmental science. 
• Physics relies on experimental research to test and refine theories, while education benefits from insights into effective teaching methods and learning environments.

General Considerations for Experimental Research Topics

Before delving into specific topics, it’s essential to consider general principles when selecting experimental research ideas:

Ethical Considerations

Ethical guidelines are paramount in experimental research. Researchers must ensure the well-being of participants, obtain informed consent, and uphold confidentiality. Ethical considerations extend to the treatment of animals in biological experiments and the responsible use of technology in various fields.

Feasibility and Resources

Selecting research topics should align with available resources, including time, funding, and access to necessary equipment. Researchers must carefully assess the feasibility of their experiments and plan accordingly.

Relevance to Current Issues or Trends

To maximize the impact of experimental research, topics should address current issues or trends within a given field. This ensures that the findings contribute meaningfully to existing knowledge and potentially address real-world challenges.

121+ Experimental Research Topics in Different Categories

• The impact of sleep deprivation on cognitive performance
• Effects of mindfulness meditation on stress reduction
• Relationship between screen time and mental health in adolescents
• Influence of music tempo on productivity and mood
• Investigating the placebo effect in pain management
• The role of nutrition in cognitive function and memory
• Effects of color on consumer perceptions and behavior
• Impact of social support on recovery from traumatic events
• Examining the effectiveness of virtual reality in therapy
• The relationship between exercise and mental well-being
• Exploring the link between creativity and sleep patterns
• Effects of bilingualism on cognitive abilities
• Investigating the impact of social media on body image
• The role of laughter in stress reduction and health
• Effects of environmental factors on workplace productivity
• Examining the impact of video games on attention span
• Influence of weather on mood and emotional well-being
• Investigating the effectiveness of cognitive-behavioral therapy
• The relationship between personality traits and job satisfaction
• Effects of caffeine on cognitive performance and alertness
• Impact of childhood trauma on adult mental health
• The role of scent in influencing consumer behavior
• Investigating the effects of positive affirmations on self-esteem
• Examining the relationship between music and learning
• Effects of social isolation on mental and physical health
• The impact of exercise on the aging process
• Investigating the relationship between diet and depression
• Effects of technology use on interpersonal relationships
• Influence of parental involvement on academic achievement
• Examining the effects of nature exposure on stress reduction
• The relationship between personality and response to stress
• Impact of workplace design on employee satisfaction
• Investigating the effectiveness of art therapy in trauma recovery
• Effects of color in marketing and consumer behavior
• The role of emotional intelligence in leadership
• Examining the impact of gender stereotypes on career choices
• Influence of social support on weight loss and fitness goals
• Investigating the effects of video game violence on behavior
• The relationship between music and exercise performance
• Effects of mindfulness interventions on anxiety levels
• Impact of parental involvement in early childhood education
• Examining the effectiveness of peer mentoring programs
• Effects of environmental noise on cognitive performance
• Influence of social media on political opinions and beliefs
• Investigating the relationship between gratitude and well-being
• The role of humor in coping with stress and adversity
• Effects of aroma therapy on sleep quality and relaxation
• Impact of workplace diversity on team performance
• Examining the relationship between humor and creativity
• Influence of cultural factors on mental health stigma
• Investigating the effects of technology on sleep patterns
• The relationship between personality and response to pain
• Effects of nature exposure on creativity and problem-solving
• Impact of parental involvement on childhood development
• Examining the effectiveness of group therapy for depression
• Influence of social media on political polarization
• Investigating the effects of social exclusion on behavior
• The role of nutrition in athletic performance and recovery
• Effects of positive reinforcement on behavior modification
• Impact of workplace flexibility on employee satisfaction
• Examining the relationship between gratitude and happiness
• Influence of social support on cardiovascular health
• Investigating the effects of aromatherapy on stress levels
• The relationship between personality and response to medication
• Effects of mindfulness interventions on academic performance
• Impact of parental involvement on adolescent mental health
• Examining the effectiveness of peer support programs
• Influence of social media on body image dissatisfaction
• Investigating the effects of laughter therapy on well-being
• The role of scent in enhancing learning and memory
• Effects of positive affirmations on athletic performance
• Impact of workplace culture on employee mental health
• Examining the relationship between humor and resilience
• Influence of social support on weight management
• Investigating the effects of technology on social skills
• The relationship between personality and response to treatment
• Effects of nature exposure on mood and emotional well-being
• Impact of parental involvement on academic motivation
• Examining the effectiveness of art therapy for stress reduction
• Influence of social media on consumer purchasing decisions
• Investigating the effects of mindfulness on sleep quality
• The role of scent in enhancing emotional experiences
• Effects of positive affirmations on academic achievement
• Impact of workplace design on employee well-being
• Examining the relationship between humor and job satisfaction
• Influence of social support on coping with chronic illness
• Investigating the effects of technology on attention span
• The relationship between personality and response to stressors
• Effects of nature exposure on cognitive performance
• Impact of parental involvement on child behavior
• Examining the effectiveness of group therapy for anxiety
• Influence of social media on social connectedness
• Investigating the effects of social isolation on mental health
• The role of scent in enhancing cognitive performance
• Effects of positive affirmations on goal achievement
• Impact of workplace diversity on organizational performance
• Examining the relationship between humor and team dynamics
• Influence of social support on academic success
• Investigating the effects of technology on sleep quality
• The relationship between personality and response to challenges
• Effects of nature exposure on creativity and innovation
• Impact of parental involvement on adolescent behavior
• Examining the effectiveness of art therapy for trauma recovery
• Influence of social media on political engagement
• Investigating the effects of mindfulness on emotional regulation
• Effects of positive affirmations on stress resilience
• Impact of workplace culture on employee satisfaction
• Examining the relationship between humor and job performance
• Influence of social support on coping with grief
• Investigating the effects of technology on social relationships
• The relationship between personality and response to therapy
• Effects of nature exposure on mood and psychological well-being
• Impact of parental involvement on academic achievement motivation
• Influence of social media on body image and self-esteem
• The role of scent in enhancing cognitive performance and memory
• Effects of positive affirmations on athletic performance and motivation
• Impact of workplace design on employee mental and physical well-being
• Examining the relationship between humor and workplace satisfaction

Tips for Selecting Experimental Research Topics

Interest and Passion

• Choose a topic that genuinely interests you. Your enthusiasm for the subject will sustain you through the research process.
• Consider areas of personal or professional passion, as this can drive motivation and dedication.
• Ensure that your chosen topic is relevant to your field of study. Consider current trends, emerging issues, or gaps in existing knowledge that your research could address.

Feasibility

• Assess the feasibility of your research topic in terms of time, resources, and accessibility. Ensure you have the means to conduct the experiments and gather data effectively.
• Look for gaps or areas with limited research in your chosen field. Novelty in your research can contribute significantly to academic discussions and the advancement of knowledge.

Practicality

• Consider the practical implications of your research. Can the findings be applied in real-world situations? Practical relevance adds value to your work.
• Ensure that your research adheres to ethical guidelines. Consider the potential impact on human subjects, animals, or the environment and address these concerns appropriately.

Collaboration Opportunities

• Explore the possibility of collaborating with experts in related fields. Interdisciplinary research can provide a broader perspective and enhance the impact of your work.

Literature Review

• Conduct a thorough literature review to understand existing research on the chosen topic. Identify gaps, controversies, or areas where further exploration is needed.
• Define the scope of your research clearly. Ensure that the topic is neither too broad nor too narrow. A well-defined scope allows for focused and meaningful investigation.

Methodology

• Consider the methodologies you will use in your experiments. Ensure they are appropriate for the research question and feasible given your resources.
• Consider the potential impact of your research. Will it contribute significantly to the field, address practical problems, or open avenues for further exploration?

Consultation

• Discuss your ideas with mentors, colleagues, or experts in the field. Their insights can help refine your topic and provide valuable perspectives.

Accessibility of Data

• Ensure that the data required for your experiments is accessible. If your research involves data collection, make sure you can obtain the necessary information.

Peer Review

• Share your proposed topics with peers or advisors and seek feedback. Constructive criticism can help refine your ideas and identify potential challenges.

Flexibility

• Be open to adjusting your research topic based on evolving circumstances or new insights. Flexibility is crucial in the dynamic landscape of research.

Experimental research topics form the bedrock of scientific advancement, driving our understanding of the world and contributing to innovations across disciplines. As we explore the vast landscape of experimental research, it’s crucial to recognize the ethical considerations, feasibility, and relevance of chosen topics. 

Whether probing the intricacies of the human mind, unraveling the mysteries of the natural world, or enhancing educational practices, experimental research continues to push the boundaries of knowledge and shape the future of scientific inquiry. 

As researchers embark on these explorations, they contribute not only to their respective fields but also to the collective pursuit of understanding and progress.

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19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

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45+ Experimental Research Topics And Examples For School & College Students

Sourav Mahahjan

Whether it is school or college, identifying a good and quality research topic can take time for students. Experimental research, also known as methodological or analytical research, uses two or more variables and arguments for a particular scenario. In this type of argument, the influence of the independent variable on the dependent variable is considered when conducting an experimental exploration. To make a particular decision in empirical research, it is important to provide a large number of evidence. The evidence collected in practical research helps identify the consequences and reasons related to different quantities of the variables. Experimental research design is an important part of the academic cycle of any student, and often, the student needs help in preparing experimental research designs. Different types of experimental research are available for the students, such as pre-experimental research, accurate experimental research, and quasi-experimental research.

What are the different types of experimental research?

Different subjects and topics required different types of experimental research. Some commonly used experimental research are quasi-experimental research, true experiment research, and pre-experimental research.

What are the different elements of experimental research?

Any experimental research consists of three essential elements. The first element is the independent variable, which the researcher manipulates. The second variable is the dependent variable, which changes according to the first variable's manipulation. The third element is the controlled variable, which is kept constant to prevent any kind of impact on the effects created by the independent variable after the manipulation by the researcher.

What are the advantages and disadvantages of experimental research?

The use of experimental research by the researcher helps provide strong evidence regarding the different types of cause-and-impact relationships in different scenarios. The experimental research service allows the researcher to maintain control of various elements of the experimental environment. On the other hand, one of the significant disadvantages of experimental research is that it is a very time-consuming process, and sometimes, the results obtained may be disconnected from the ordinary world. 

Examples of experimental research titles:

Creating an experimental research design is very frustrating, and selecting the appropriate title becomes essential as it forms the basis of experimental research. Before choosing a topic, it becomes necessary for the students to find out literature providing disparity and research provision. This results in investing significant time and effort to search for an appropriate experimental research title. This makes the students lose patience and select the wrong research topic, impacting the overall quality of experimental research.  Examples of experimental research design are

Experimental research titles on natural science for school students:

• Impact of Light  on the Plant Growth
• Role of Different Salt Concentrations over the Freezing Point of Water
• Comparing Battery Life among Different Brands
• Analysis of  pH on Enzyme Activity
• Impact of Magnet Strength on a Paperclip over a long distance

Experimental research design on behavioural science for school students:

• Role of music in affecting Concentration
• Individual Study vs Group Study on Academic Performance
• Part of Reward Systems on Increasing Student Motivation
• Impact of Various Colors on Mood
• How Sleep Patterns Effect Academic Performance

Experimental Research title on Social Science for college students:

• Part of  Socioeconomic Status over the Mental Health
• How Media Representation influences the body image of an individual 
• Bilingual Education and their Role in Academic Success
• importance of Social Media during Political Campaigns
• How Gender Stereotypes Influence the Career Choices in the society

Experimental Research title on natural Science for college students:

• What is the role of Genetics in causing Obesity? 
• How Climate Change Affects the Marine Life
• Role of Pesticides in declining Bee Populations
• Increasing Pollution and Its Impact on Urban Wildlife
• What is the role of microplastics in the destruction of Freshwater Ecosystems

Experimental Research title on applied Science for college students:

• How Machine Learning Algorithms are helping in predicting Stock Prices? 
• How is data Encryption improving Data Security?
• How does Aerodynamics influence the vehicle Fuel Efficiency? 
• Bridge Stability and its dependency on the material properties.
• How do different Angles of solar panel impacts their efficiency?

Experimental research titles in health science for college students:

• How does Exercise help in managing Type 2 Diabetes? 
• Cognitive Performance under the influence of caffeine
• How do Plant-Based Diets improve our heart health?
• How do Different Forms of Physical therapy help speed the process of Knee Rehabilitation?
• Mindfulness Meditation and their Impact on Stress Reduction

Experimental titles on environmental studies for college students:

• How does deforestation affect the  Local Climate?
• What are the Different types of Oil Spill Cleanup methods, and how effective are they? 
• Does Organic Farming help in improving Crop Yield?
• What is the role of noise Pollution on the growth of  Urban Wildlife?
• Impacts of increasing E-Waste on Soil Quality

Experimental research topics for computer studies in colleges:

• What are the  different Sorting Algorithms
• Analysing the security efficiency of various types of  password Policies
• How User Experience depends on the user interface
• Artificial Intelligence  and Its Importance in Image Recognition
• Energy Efficiency analysis between different types of  computer processors

Experimental research topics for college students on economics:

• How do economic policies impact the Inflation growth in the economy?
• How does microfinance can help in reducing poverty in the society? 
• Globalisation and its Impact on Small Businesses
• Why do exchange rates are essential for the export market?
• Role of Large Scale Unemployment Rates in increasing crime Rates

Tips for selecting suitable experimental research title:

Establishing the appropriate research title is very helpful in completing a practical research assignment . Some of the recommendations for the students are 

• Interest:  The research tile should be based on the student's interest. This helps in improving the quality of the research.
• Relevance:  The selected title should be relevant to the subject of the student.  It should fulfil the objectives of the course. 
• Feasibility:  The selected topic should be practical and have adequate resources required for the study. 

Conclusion 

Experimental research is essential in conducting scientific inquiry during an academic study. Experimental research helps students use their knowledge to improve their problem-solving and critical-thinking abilities in their academic cycle.

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Top 100 Experimental Research Topics for School & College Students

Top 100 Experimental Research Topics for School & College Students: Are you a student looking for inspiration for your next research project? Research is a vital aspect of your educational journey, and choosing the right topic is often the first step to success. Whether you’re in school or college, finding a compelling experimental research topic can be a daunting task. But fear not! We’ve compiled a list of the top 100 experimental research topics to ignite your curiosity and help you embark on an exciting research journey.

What is Experimental Research?

Experimental research is a research approach that entails the deliberate manipulation of one or more independent variables to assess their impact on one or more dependent variables. It is widely regarded as the “gold standard” of research methodologies due to its capacity to establish causal relationships between variables.

Typically, experimental research designs involve the creation of two distinct groups: the experimental group and the control group. The experimental group is exposed to the independent variable, while the control group is not. Subsequently, the researcher compares the outcomes of these two groups to identify any disparities.

Two primary categories of experimental research designs exist: true experiments and quasi-experiments. True experiments employ random assignment of participants to the experimental and control groups, ensuring initial equivalency between the groups and minimizing alternative explanations for observed differences.

Conversely, quasi-experiments lack random assignment, potentially introducing disparities between the experimental and control groups at the outset, which may confound the results. Nevertheless, quasi-experiments can still be valuable in studying cause-and-effect relationships, particularly when random assignment is impractical or ethically challenging.

Experimental research finds applications across diverse fields such as science, medicine, education, and business. It serves as a potent tool for comprehending how various factors influence outcomes and for developing novel products and interventions.

Consider the following examples of experimental research :

A scientist aims to assess a new drug’s effectiveness in treating high blood pressure. Participants are randomly assigned to receive either the new drug or a placebo. After several weeks, their blood pressure is measured, and the results between the two groups are compared.

A teacher seeks to investigate the impact of various teaching methods on student achievement. Students are randomly allocated to different classrooms, each utilizing a distinct teaching method. At the end of the semester, the students’ test scores are compared to identify the most effective teaching method.

A marketing manager intends to evaluate the influence of a new advertising campaign on product sales. A random sample of customers is chosen and assigned to either view the new advertising campaign or not. After several weeks, sales data from the two groups are compared to determine the campaign’s effectiveness.

Major Types of Experimental Research Design

There are three main types of experimental research designs:

1. Pre-experimental research designs

Pre-experimental research designs are the simplest type of experimental design. They do not involve random assignment, and the researcher typically only tests one group of participants. Pre-experimental research designs are often used to generate preliminary data or to explore new research questions. However, they are not considered to be as rigorous as other types of experimental designs because they are more prone to confounding variables.

Here are some examples of pre-experimental research designs:

• One-shot case study design: The researcher tests a single group of participants after they have been exposed to the independent variable.
• One-group pretest-posttest design: The researcher tests a single group of participants before and after they have been exposed to the independent variable.
• Static-group comparison design: The researcher compares two groups of participants, one of which has been exposed to the independent variable and the other of which has not.

2. Quasi-experimental research designs

Quasi-experimental research designs are more rigorous than pre-experimental research designs because they involve some form of control group. However, they do not involve random assignment. Quasi-experimental research designs are often used in situations where random assignment is not feasible or ethical.

Here are some examples of quasi-experimental research designs:

• Non-equivalent control group design: The researcher compares two groups of participants, one of which has been exposed to the independent variable and the other of which has not. The two groups are not randomly assigned, but the researcher tries to match them on relevant characteristics to reduce the risk of confounding variables.
• Time series design: The researcher tests a single group of participants multiple times over time, both before and after they have been exposed to the independent variable.
• Interrupted time series design: The researcher tests a single group of participants multiple times over time, both before and after they have been exposed to the independent variable. However, there is an interruption in the time series, such as a change in policy or practice, that may affect the dependent variable.

3. True experimental research designs

True experimental research designs are the most rigorous type of experimental design. They involve random assignment and a control group. True experimental research designs are considered to be the best way to establish cause-and-effect relationships between variables.

Here are some examples of true experimental research designs:

• Randomized controlled trial (RCT): The researcher randomly assigns participants to either the experimental group or the control group. The experimental group is exposed to the independent variable, while the control group is not. The researcher then compares the outcomes of the two groups to see if there is a difference.
• Posttest-only control group design: The researcher randomly assigns participants to either the experimental group or the control group. The experimental group is exposed to the independent variable, while the control group is not. The researcher then measures the dependent variable in both groups after the experiment is complete.
• Solomon four-group design: This design is similar to the posttest-only control group design, but it also includes two additional groups: a pretest-posttest experimental group and a pretest-posttest control group. This allows the researcher to control for the effects of testing.

Experimental research is a powerful tool for understanding the world around us and developing new ways to improve our lives. By understanding the different types of experimental research designs, we can better evaluate the quality of research and make informed decisions about the findings.

Elements of Experimental Research

Experimental research typically comprises several essential elements that help structure and conduct a rigorous scientific investigation. These elements are crucial for designing, executing, and analyzing experiments effectively. Here are the key elements of experimental research:

• Research Question or Hypothesis : Every experiment begins with a clear research question or a testable hypothesis. This question or hypothesis specifies what the researcher aims to investigate or the relationship they seek to explore.
• Independent Variable : The independent variable is the factor that the researcher intentionally manipulates or varies in the experiment. It is the presumed cause and is under the researcher’s control. In some cases, there may be more than one independent variable.
• Dependent Variable : The dependent variable is the outcome or response that the researcher measures or observes. It is the variable that may be influenced by changes in the independent variable. The dependent variable is what researchers are trying to understand or explain.
• Experimental and Control Groups : To assess the impact of the independent variable, participants or subjects are typically divided into at least two groups: the experimental group and the control group. The experimental group is exposed to the independent variable, while the control group is not. This comparison helps determine whether any observed effects are due to the manipulation of the independent variable.
• Random Assignment : In true experimental designs, participants are randomly assigned to the experimental and control groups. Random assignment helps ensure that the groups are comparable and minimizes bias, increasing the internal validity of the experiment.
• Controlled Conditions : Experimental research strives to control and minimize the influence of extraneous variables, which are factors other than the independent variable that could affect the results. This control helps isolate the effects of the independent variable.
• Experimental Procedure : Researchers outline the specific steps and procedures that participants will undergo during the experiment. This includes how the independent variable will be manipulated, how data will be collected, and the sequence of events.
• Data Collection : Data collection involves gathering information about the dependent variable’s responses or outcomes. This is typically done through measurements, observations, surveys, or other data collection methods.
• Data Analysis : After data collection, researchers analyze the collected data using statistical methods to determine whether there are significant differences or relationships between groups. This analysis helps draw conclusions about the impact of the independent variable on the dependent variable.
• Replication : To enhance the reliability of experimental findings, replication involves repeating the experiment under similar conditions to see if the results can be consistently reproduced.
• Ethical Considerations : Researchers must adhere to ethical principles when conducting experiments involving human or animal subjects. This includes obtaining informed consent, ensuring participant well-being, and minimizing harm.
• Reporting and Communication : Researchers communicate their findings by writing research papers or reports that describe the experiment, its methods, results, and conclusions. This enables other scientists to assess and build upon the research.

These elements collectively form the foundation of experimental research, allowing researchers to systematically investigate and establish cause-and-effect relationships between variables in a controlled and methodical manner.

Top Best Experimental Research Topics for School Students

Natural sciences research topics for school students:.

• Investigating How Light Intensity Affects Plant Growth
• Exploring the Relationship Between Salt Concentrations and the Freezing Point of Water
• Comparing Battery Lifespan Among Various Brands
• Studying the Influence of pH on Enzyme Activity
• Examining the Effect of Magnet Strength on the Attraction Distance of a Paperclip

Behavioral Sciences Research Topics for School Students:

• Analyzing the Impact of Music on Concentration
• Contrasting Group Study and Individual Study to Assess Their Effects on Academic Performance
• Investigating the Influence of Reward Systems on Student Motivation
• Exploring the Role of Different Colors in Shaping Mood
• Assessing How Sleep Patterns Affect Academic Performance

Environmental Studies Research Topics for School Students:

• Investigating How Temperature Affects Composting Processes
• Assessing the Consequences of Water Pollution on Aquatic Life
• Exploring the Impact of Urbanization on Local Bird Species
• Studying the Influence of Different Soil Types on Plant Growth
• Examining the Effects of Acid Rain on Plant Growth

Best Experimental Research Topics for College Students

Social sciences research topics for college students:.

• Examining the Relationship Between Socioeconomic Status and Mental Health
• Analyzing the Influence of Media Portrayals on Body Image
• Investigating the Effects of Bilingual Education on Academic Achievement
• Exploring the Role of Social Media in Political Campaigns
• Assessing the Impact of Gender Stereotypes on Career Choices

Business and Economics:

• Evaluating the Influence of Online Reviews on Consumer Purchasing Decisions
• The Effect of Advertising on Brand Loyalty
• Analyzing the Impact of Corporate Social Responsibility on Profitability
• The Efficacy of Different Pricing Strategies on Sales
• Investigating the Relationship Between Employee Satisfaction and Productivity
• Effects of Economic Policy Changes on Small Businesses
• The Role of Market Research in Product Development
• The Impact of Globalization on International Trade
• Comparing the Performance of Different Investment Strategies
• Evaluating the Effects of Tax Policies on Economic Growth

Natural Sciences Research Topics for College Students:

• Investigating the Genetic Factors Contributing to Obesity
• Analyzing the Effects of Climate Change on Marine Ecosystems
• Assessing the Impact of Pesticides on Bee Populations
• Studying the Consequences of Pollution on Urban Wildlife
• Examining the Role of Microplastics in Freshwater Ecosystems

Applied Sciences Research Topics for College Students:

• Evaluating the Effectiveness of Machine Learning Algorithms in Predicting Stock Prices
• Analyzing the Significance of Encryption in Ensuring Data Security
• Investigating the Influence of Aerodynamics on Vehicle Fuel Efficiency
• Assessing the Impact of Material Properties on Bridge Stability
• Studying the Efficiency of Solar Panels at Different Angles

Health Sciences Research Topics for College Students:

• Investigating the Role of Exercise in the Management of Type 2 Diabetes
• Analyzing the Effects of Caffeine on Cognitive Performance
• Assessing the Impact of Plant-Based Diets on Heart Health
• Evaluating the Effectiveness of Various Physical Therapy Methods in Knee Rehabilitation
• Studying the Role of Mindfulness Meditation in Reducing Stress

Environmental Sciences Research Topics for College Students:

• Examining the Consequences of Deforestation on Local Climate Patterns
• Investigating the Efficacy of Different Oil Spill Cleanup Techniques
• Analyzing the Effects of Organic Farming on Crop Yield
• Assessing the Impact of Noise Pollution on Urban Wildlife
• Examining the Influence of Electronic Waste (E-Waste) on Soil Quality

Computer Sciences Research Topics for College Students:

• Comparing Various Sorting Algorithms for Efficiency
• Evaluating the Security Implications of Different Password Policies
• Analyzing the Impact of User Interface Design on User Experience
• Investigating the Role of Artificial Intelligence in Image Recognition
• Assessing the Energy Efficiency of Different Computer Processors

Economics Research Topics for College Students:

• Examining the Effects of Economic Policies on Inflation
• Analyzing the Role of Microfinance in Alleviating Poverty
• Assessing the Impact of Globalization on Small Businesses
• Investigating the Influence of Exchange Rates on the Export Market
• Evaluating the Relationship Between Unemployment and Crime Rates

Tips for Selecting an Appropriate Experimental Research Topic

Choosing the right topic is fundamental to the success of an experimental research project. Here are some valuable tips to assist students in this selection process:

• Interest : Opt for a topic that genuinely piques your interest. Your passion for the subject will serve as a motivating force throughout the research journey.
• Relevance : Pick a topic that aligns with your field of study. It should complement your academic objectives and enrich your comprehension of the subject matter.
• Feasibility : Ensure that the chosen topic is practical and feasible for research. Consider factors such as resource availability, time constraints, and ethical considerations.
• Uniqueness : Choose a topic that is original and distinctive. This not only enhances the appeal of your research but also contributes to the advancement of your academic field.

Conclusion: 100 Experimental Research Topics for Students

Experimental research is a pivotal component of scientific exploration. It empowers us to establish causal relationships, expand our comprehension of the world, and discover solutions to issues across diverse fields of study.

Engaging in an experimental research project can be a gratifying experience. It enables students to apply their knowledge, cultivate critical thinking and problem-solving skills, and make meaningful contributions to their academic discipline.

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Original research article, learning scientific observation with worked examples in a digital learning environment.

• 1 Department Educational Sciences, Chair for Formal and Informal Learning, Technical University Munich School of Social Sciences and Technology, Munich, Germany
• 2 Aquatic Systems Biology Unit, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

Science education often aims to increase learners’ acquisition of fundamental principles, such as learning the basic steps of scientific methods. Worked examples (WE) have proven particularly useful for supporting the development of such cognitive schemas and successive actions in order to avoid using up more cognitive resources than are necessary. Therefore, we investigated the extent to which heuristic WE are beneficial for supporting the acquisition of a basic scientific methodological skill—conducting scientific observation. The current study has a one-factorial, quasi-experimental, comparative research design and was conducted as a field experiment. Sixty two students of a German University learned about scientific observation steps during a course on applying a fluvial audit, in which several sections of a river were classified based on specific morphological characteristics. In the two experimental groups scientific observation was supported either via faded WE or via non-faded WE both presented as short videos. The control group did not receive support via WE. We assessed factual and applied knowledge acquisition regarding scientific observation, motivational aspects and cognitive load. The results suggest that WE promoted knowledge application: Learners from both experimental groups were able to perform the individual steps of scientific observation more accurately. Fading of WE did not show any additional advantage compared to the non-faded version in this regard. Furthermore, the descriptive results reveal higher motivation and reduced extraneous cognitive load within the experimental groups, but none of these differences were statistically significant. Our findings add to existing evidence that WE may be useful to establish scientific competences.

1 Introduction

Learning in science education frequently involves the acquisition of basic principles or generalities, whether of domain-specific topics (e.g., applying a mathematical multiplication rule) or of rather universal scientific methodologies (e.g., performing the steps of scientific observation) ( Lunetta et al., 2007 ). Previous research has shown that worked examples (WE) can be considered particularly useful for developing such cognitive schemata during learning to avoid using more cognitive resources than necessary for learning successive actions ( Renkl et al., 2004 ; Renkl, 2017 ). WE consist of the presentation of a problem, consecutive solution steps and the solution itself. This is especially advantageous in initial cognitive skill acquisition, i.e., for novice learners with low prior knowledge ( Kalyuga et al., 2001 ). With growing knowledge, fading WE can lead from example-based learning to independent problem-solving ( Renkl et al., 2002 ). Preliminary work has shown the advantage of WE in specific STEM domains like mathematics ( Booth et al., 2015 ; Barbieri et al., 2021 ), but less studies have investigated their impact on the acquisition of basic scientific competencies that involve heuristic problem-solving processes (scientific argumentation, Schworm and Renkl, 2007 ; Hefter et al., 2014 ; Koenen et al., 2017 ). In the realm of natural sciences, various basic scientific methodologies are employed to acquire knowledge, such as experimentation or scientific observation ( Wellnitz and Mayer, 2013 ). During the pursuit of knowledge through scientific inquiry activities, learners may encounter several challenges and difficulties. Similar to the hurdles faced in experimentation, where understanding the criteria for appropriate experimental design, including the development, measurement, and evaluation of results, is crucial ( Sirum and Humburg, 2011 ; Brownell et al., 2014 ; Dasgupta et al., 2014 ; Deane et al., 2014 ), scientific observation additionally presents its own set of issues. In scientific observation, e.g., the acquisition of new insights may be somewhat incidental due to spontaneous and uncoordinated observations ( Jensen, 2014 ). To address these challenges, it is crucial to provide instructional support, including the use of WE, particularly when observations are carried out in a more self-directed manner.

For this reason, the aim of the present study was to determine the usefulness of digitally presented WE to support the acquisition of a basic scientific methodological skill—conducting scientific observations—using a digital learning environment. In this regard, this study examined the effects of different forms of digitally presented WE (non-faded vs. faded) on students’ cognitive and motivational outcomes and compared them to a control group without WE. Furthermore, the combined perspective of factual and applied knowledge, as well as motivational and cognitive aspects, represent further value added to the study.

2 Theoretical background

2.1 worked examples.

WE have been commonly used in the fields of STEM education (science, technology, engineering, and mathematics) ( Booth et al., 2015 ). They consist of a problem statement, the steps to solve the problem, and the solution itself ( Atkinson et al., 2000 ; Renkl et al., 2002 ; Renkl, 2014 ). The success of WE can be explained by their impact on cognitive load (CL) during learning, based on assumptions from Cognitive Load Theory ( Sweller, 2006 ).

Learning with WE is considered time-efficient, effective, and superior to problem-based learning (presentation of the problem without demonstration of solution steps) when it comes to knowledge acquisition and transfer (WE-effect, Atkinson et al., 2000 ; Van Gog et al., 2011 ). Especially WE can help by reducing the extraneous load (presentation and design of the learning material) and, in turn, can lead to an increase in germane load (effort of the learner to understand the learning material) ( Paas et al., 2003 ; Renkl, 2014 ). With regard to intrinsic load (difficulty and complexity of the learning material), it is still controversially discussed if it can be altered by instructional design, e.g., WE ( Gerjets et al., 2004 ). WE have a positive effect on learning and knowledge transfer, especially for novices, as the step-by-step presentation of the solution requires less extraneous mental effort compared to problem-based learning ( Sweller et al., 1998 ; Atkinson et al., 2000 ; Bokosmaty et al., 2015 ). With growing knowledge, WE can lose their advantages (due to the expertise-reversal effect), and scaffolding learning via faded WE might be more successful for knowledge gain and transfer ( Renkl, 2014 ). Faded WE are similar to complete WE, but fade out solution steps as knowledge and competencies grow. Faded WE enhance near-knowledge transfer and reduce errors compared to non-faded WE ( Renkl et al., 2000 ).

In addition, the reduction of intrinsic and extraneous CL by WE also has an impact on learner motivation, such as interest ( Van Gog and Paas, 2006 ). Um et al. (2012) showed that there is a strong positive correlation between germane CL and the motivational aspects of learning, like satisfaction and emotion. Gupta (2019) mentions a positive correlation between CL and interest. Van Harsel et al. (2019) found that WE positively affect learning motivation, while no such effect was found for problem-solving. Furthermore, learning with WE increases the learners’ belief in their competence in completing a task. In addition, fading WE can lead to higher motivation for more experienced learners, while non-faded WE can be particularly motivating for learners without prior knowledge ( Paas et al., 2005 ). In general, fundamental motivational aspects during the learning process, such as situational interest ( Lewalter and Knogler, 2014 ) or motivation-relevant experiences, like basic needs, are influenced by learning environments. At the same time, their use also depends on motivational characteristics of the learning process, such as self-determined motivation ( Deci and Ryan, 2012 ). Therefore, we assume that learning with WE as a relevant component of a learning environment might also influence situational interest and basic needs.

2.1.1 Presentation of worked examples

WE are frequently used in digital learning scenarios ( Renkl, 2014 ). When designing WE, the application via digital learning media can be helpful, as their content can be presented in different ways (video, audio, text, and images), tailored to the needs of the learners, so that individual use is possible according to their own prior knowledge or learning pace ( Mayer, 2001 ). Also, digital media can present relevant information in a timely, motivating, appealing and individualized way and support learning in an effective and needs-oriented way ( Mayer, 2001 ). The advantages of using digital media in designing WE have already been shown in previous studies. Dart et al. (2020) presented WE as short videos (WEV). They report that the use of WEV leads to increased student satisfaction and more positive attitudes. Approximately 90% of the students indicated an active learning approach when learning with the WEV. Furthermore, the results show that students improved their content knowledge through WEV and that they found WEV useful for other courses as well.

Another study ( Kay and Edwards, 2012 ) presented WE as video podcasts. Here, the advantages of WE regarding self-determined learning in terms of learning location, learning time, and learning speed were shown. Learning performance improved significantly after use. The step-by-step, easy-to-understand explanations, the diagrams, and the ability to determine the learning pace by oneself were seen as beneficial.

Multimedia WE can also be enhanced with self-explanation prompts ( Berthold et al., 2009 ). Learning from WE with self-explanation prompts was shown to be superior to other learning methods, such as hypertext learning and observational learning.

In addition to presenting WE in different medial ways, WE can also comprise different content domains.

2.1.2 Content and context of worked examples

Regarding the content of WE, algorithmic and heuristic WE, as well as single-content and double-content WE, can be distinguished ( Reiss et al., 2008 ; Koenen et al., 2017 ; Renkl, 2017 ). Algorithmic WE are traditionally used in the very structured mathematical–physical field. Here, an algorithm with very specific solution steps is to learn, for example, in probability calculation ( Koenen et al., 2017 ). In this study, however, we focus on heuristic double-content WE. Heuristic WE in science education comprise fundamental scientific working methods, e.g., conducting experiments ( Koenen et al., 2017 ). Furthermore, double-content WE contain two learning domains that are relevant for the learning process: (1) the learning domain describes the primarily to be learned abstract process or concept, e.g., scientific methodologies like observation (see section 2.2), while (2) the exemplifying domain consists of the content that is necessary to teach this process or concept, e.g., mapping of river structure ( Renkl et al., 2009 ).

Depending on the WE content to be learned, it may be necessary for learning to take place in different settings. This can be in a formal or informal learning setting or a non-formal field setting. In this study, the focus is on learning scientific observation (learning domain) through river structure mapping (exemplary domain), which takes place with the support of digital media in a formal (university) setting, but in an informal context (nature).

2.2 Scientific observation

Scientific observation is fundamental to all scientific activities and disciplines ( Kohlhauf et al., 2011 ). Scientific observation must be clearly distinguished from everyday observation, where observation is purely a matter of noticing and describing specific characteristics ( Chinn and Malhotra, 2001 ). In contrast to this everyday observation, scientific observation as a method of knowledge acquisition can be described as a rather complex activity, defined as the theory-based, systematic and selective perception of concrete systems and processes without any fundamental manipulation ( Wellnitz and Mayer, 2013 ). Wellnitz and Mayer (2013) described the scientific observation process via six steps: (1) formulation of the research question (s), (2) deduction of the null hypothesis and the alternative hypothesis, (3) planning of the research design, (4) conducting the observation, (5) analyzing the data, and (6) answering the research question(s) on this basis. Only through reliable and qualified observation, valid data can be obtained that provide solid scientific evidence ( Wellnitz and Mayer, 2013 ).

Since observation activities are not trivial and learners often observe without generating new knowledge or connecting their observations to scientific explanations and thoughts, it is important to provide support at the related cognitive level, so that observation activities can be conducted in a structured way according to pre-defined criteria ( Ford, 2005 ; Eberbach and Crowley, 2009 ). Especially during field-learning experiences, scientific observation is often spontaneous and uncoordinated, whereby random discoveries result in knowledge gain ( Jensen, 2014 ).

To promote successful observing in rather unstructured settings like field trips, instructional support for the observation process seems useful. To guide observation activities, digitally presented WE seem to be an appropriate way to introduce learners to the individual steps of scientific observation using concrete examples.

2.3 Research questions and hypothesis

The present study investigates the effect of digitally presented double-content WE that supports the mapping of a small Bavarian river by demonstrating the steps of scientific observation. In this analysis, we focus on the learning domain of the WE and do not investigate the exemplifying domain in detail. Distinct ways of integrating WE in the digital learning environment (faded WE vs. non-faded WE) are compared with each other and with a control group (no WE). The aim is to examine to what extent differences between those conditions exist with regard to (RQ1) learners’ competence acquisition [acquisition of factual knowledge about the scientific observation method (quantitative data) and practical application of the scientific observation method (quantified qualitative data)], (RQ2) learners’ motivation (situational interest and basic needs), and (RQ3) CL. It is assumed that (Hypothesis 1), the integration of WE (faded and non-faded) leads to significantly higher competence acquisition (factual and applied knowledge), significantly higher motivation and significantly lower extraneous CL as well as higher germane CL during the learning process compared to a learning environment without WE. No differences between the conditions are expected regarding intrinsic CL. Furthermore, it is assumed (Hypothesis 2) that the integration of faded WE leads to significantly higher competence acquisition, significantly higher motivation, and lower extraneous CL as well as higher germane CL during the learning processes compared to non-faded WE. No differences between the conditions are expected with regard to intrinsic CL.

The study took place during the field trips of a university course on the application of a fluvial audit (FA) using the German working aid for mapping the morphology of rivers and their floodplains ( Bayerisches Landesamt für Umwelt, 2019 ). FA is the leading fluvial geomorphological tool for application to data collection contiguously along all watercourses of interest ( Walker et al., 2007 ). It is widely used because it is a key example of environmental conservation and monitoring that needs to be taught to students of selected study programs; thus, knowing about the most effective ways of learning is of high practical relevance.

3.1 Sample and design

3.1.1 sample.

The study was conducted with 62 science students and doctoral students of a German University (age M  = 24.03 years; SD  = 4.20; 36 females; 26 males). A total of 37 participants had already conducted a scientific observation and would rate their knowledge in this regard at a medium level ( M  = 3.32 out of 5; SD  = 0.88). Seven participants had already conducted an FA and would rate their knowledge in this regard at a medium level ( M  = 3.14 out of 5; SD  = 0.90). A total of 25 participants had no experience at all. Two participants had to be excluded from the sample afterward because no posttest results were available.

3.1.2 Design

The study has a 1-factorial quasi-experimental comparative research design and is conducted as a field experiment using a pre/posttest design. Participants were randomly assigned to one of three conditions: no WE ( n  = 20), faded WE ( n  = 20), and non-faded WE ( n  = 20).

3.2 Implementation and material

3.2.1 implementation.

The study started with an online kick-off meeting where two lecturers informed all students within an hour about the basics regarding the assessment of the structural integrity of the study river and the course of the field trip days to conduct an FA. Afterward, within 2 weeks, students self-studied via Moodle the FA following the German standard method according to the scoresheets of Bayerisches Landesamt für Umwelt (2019) . This independent preparation using the online presented documents was a necessary prerequisite for participation in the field days and was checked in the pre-testing. The preparatory online documents included six short videos and four PDF files on the content, guidance on the German protocol of the FA, general information on river landscapes, information about anthropogenic changes in stream morphology and the scoresheets for applying the FA. In these sheets, the river and its floodplain are subdivided into sections of 100 m in length. Each of these sections is evaluated by assessing 21 habitat factors related to flow characteristics and structural variability. The findings are then transferred into a scoring system for the description of structural integrity from 1 (natural) to 7 (highly modified). Habitat factors have a decisive influence on the living conditions of animals and plants in and around rivers. They included, e.g., variability in water depth, stream width, substratum diversity, or diversity of flow velocities.

3.2.2 Materials

On the field trip days, participants were handed a tablet and a paper-based FA worksheet (last accessed 21st September 2022). 1 This four-page assessment sheet was accompanied by a digital learning environment presented on Moodle that instructed the participants on mapping the water body structure and guided the scientific observation method. All three Moodle courses were identical in structure and design; the only difference was the implementation of the WE. Below, the course without WE are described first. The other two courses have an identical structure, but contain additional WE in the form of learning videos.

3.2.3 No worked example

After a short welcome and introduction to the course navigation, the FA started with the description of a short hypothetical scenario: Participants should take the role of an employee of an urban planning office that assesses the ecomorphological status of a small river near a Bavarian city. The river was divided into five sections that had to be mapped separately. The course was structured accordingly. At the beginning of each section, participants had to formulate and write down a research question, and according to hypotheses regarding the ecomorphological status of the river’s section, they had to collect data in this regard via the mapping sheet and then evaluate their data and draw a conclusion. Since this course serves as a control group, no WE videos supporting the scientific observation method were integrated. The layout of the course is structured like a book, where it is not possible to scroll back. This is important insofar as the participants do not have the possibility to revisit information in order to keep the conditions comparable as well as distinguishable.

3.2.4 Non-faded worked example

In the course with no-faded WE, three instructional videos are shown for each of the five sections. In each of the three videos, two steps of the scientific observation method are presented so that, finally, all six steps of scientific observation are demonstrated. The mapping of the first section starts after the general introduction (as described above) with the instruction to work on the first two steps of scientific observation: the formulation of a research question and hypotheses. To support this, a video of about 4 min explains the features of scientific sound research questions and hypotheses. To this aim, a practical example, including explanations and tips, is given regarding the formulation of research questions and hypotheses for this section (e.g., “To what extent does the building development and the closeness of the path to the water body have an influence on the structure of the water body?” Alternative hypothesis: It is assumed that the housing development and the closeness of the path to the water body have a negative influence on the water body structure. Null hypothesis: It is assumed that the housing development and the closeness of the path to the watercourse have no negative influence on the watercourse structure.). Participants should now formulate their own research questions and hypotheses, write them down in a text field at the end of the page, and then skip to the next page. The next two steps of scientific observation, planning and conducting, are explained in a short 4-min video. To this aim, a practical example including explanations and tips is given regarding planning and conducting scientific for this section (e.g., “It’s best to go through each evaluation category carefully one by one that way you are sure not to forget anything!”). Now, participants were asked to collect data for the first section using their paper-based FA worksheet. Participants individually surveyed the river and reported their results in the mapping sheet by ticking the respective boxes in it. After collecting this data, they returned to the digital learning environment to learn how to use these data by studying the last two steps of scientific observation, evaluation, and conclusion. The third 4-min video explained how to evaluate and interpret collected data. For this purpose, a practical example with explanations and tips is given regarding evaluating and interpreting data for this section (e.g., “What were the individual points that led to the assessment? Have there been points that were weighted more than others? Remember the introduction video!”). At the end of the page, participants could answer their before-stated research questions and hypotheses by evaluating their collected data and drawing a conclusion. This brings participants to the end of the first mapping section. Afterward, the cycle begins again with the second section of the river that has to be mapped. Again, participants had to conduct the steps of scientific observation, guided by WE videos, explaining the steps in slightly different wording or with different examples. A total of five sections are mapped, in which the structure of the learning environment and the videos follow the same procedure.

3.2.5 Faded worked example

The digital learning environment with the faded WE follow the same structure as the version with the non-faded WE. However, in this version, the information in the WE videos is successively reduced. In the first section, all three videos are identical to the version with the non-faded WE. In the second section, faded content was presented as follows: the tip at the end was omitted in all three videos. In the third section, the tip and the practical example were omitted. In the fourth and fifth sections, no more videos were presented, only the work instructions.

3.3 Procedure

The data collection took place on four continuous days on the university campus, with a maximum group size of 15 participants on each day. The students were randomly assigned to one of the three conditions (no WE vs. faded WE vs. non-faded WE). After a short introduction to the procedure, the participants were handed the paper-based FA worksheet and one tablet per person. Students scanned the QR code on the first page of the worksheet that opened the pretest questionnaire, which took about 20 min to complete. After completing the questionnaire, the group walked for about 15 min to the nearby small river that was to be mapped. Upon arrival, there was first a short introduction to the digital learning environment and a check that the login (via university account on Moodle) worked. During the next 4 h, the participants individually mapped five segments of the river using the cartography worksheet. They were guided through the steps of scientific observation using the digital learning environment on the tablet. The results of their scientific observation were logged within the digital learning environment. At the end of the digital learning environment, participants were directed to the posttest via a link. After completing the test, the tablets and mapping sheets were returned. Overall, the study took about 5 h per group each day.

3.4 Instruments

In the pretest, sociodemographic data (age and gender), the study domain and the number of study semesters were collected. Additionally, the previous scientific observation experience and the estimation of one’s own ability in this regard were assessed. For example, it was asked whether scientific observation had already been conducted and, if so, how the abilities were rated on a 5-point scale from very low to very high. Preparation for the FA on the basis of the learning material was assessed: Participants were asked whether they had studied all six videos and all four PDF documents, with the response options not at all, partially, and completely. Furthermore, a factual knowledge test about scientific observation and questions about self-determination theory was administered. The posttest used the same knowledge test, and additional questions on basic needs, situational interest, measures of CL and questions about the usefulness of the WE. All scales were presented online, and participants reached the questionnaire via QR code.

3.4.1 Scientific observation competence acquisition

For the factual knowledge (quantitative assessment of the scientific observation competence), a single-choice knowledge test with 12 questions was developed and used as pre- and posttest with a maximum score of 12 points. It assesses the learners’ knowledge of the scientific observation method regarding the steps of scientific observation, e.g., formulating research questions and hypotheses or developing a research design. The questions are based on Wahser (2008 , adapted by Koenen, 2014 ) and adapted to scientific observation: “Although you are sure that you have conducted the scientific observation correctly, an unexpected result turns up. What conclusion can you draw?” Each question has four answer options (one of which is correct) and, in addition, one “I do not know” option.

For the applied knowledge (quantified qualitative assessment of the scientific observation competence), students’ scientific observations written in the digital learning environment were analyzed. A coding scheme was used with the following codes: 0 = insufficient (text field is empty or includes only insufficient key points), 1 = sufficient (a research question and no hypotheses or research question and inappropriate hypotheses are stated), 2 = comprehensive (research question and appropriate hypothesis or research question and hypotheses are stated, but, e.g., incorrect null hypothesis), 3 = very comprehensive (correct research question, hypothesis and null hypothesis are stated). One example of a very comprehensive answer regarding the research question and hypothesis is: To what extent does the lack of riparian vegetation have an impact on water body structure? Hypothesis: The lack of shore vegetation has a negative influence on the water body structure. Null hypothesis: The lack of shore vegetation has no influence on the water body structure. Afterward, a sum score was calculated for each participant. Five times, a research question and hypotheses (steps 1 and 2 in the observation process) had to be formulated (5 × max. 3 points = 15 points), and five times, the research questions and hypotheses had to be answered (steps 5 and 6 in the observation process: evaluation and conclusion) (5 × max. 3 points = 15 points). Overall, participants could reach up to 30 points. Since the observation and evaluation criteria in data collection and analysis were strongly predetermined by the scoresheet, steps 3 and 4 of the observation process (planning and conducting) were not included in the analysis.

All 600 cases (60 participants, each 10 responses to code) were coded by the first author. For verification, 240 cases (24 randomly selected participants, eight from each course) were cross-coded by an external coder. In 206 of the coded cases, the raters agreed. The cases in which the raters did not agree were discussed together, and a solution was found. This results in Cohen’s κ = 0.858, indicating a high to very high level of agreement. This indicates that the category system is clearly formulated and that the individual units of analysis could be correctly assigned.

3.4.2 Self-determination index

For the calculation of the self-determination index (SDI-index), Thomas and Müller (2011) scale for self-determination was used in the pretest. The scale consists of four subscales: intrinsic motivation (five items; e.g., I engage with the workshop content because I enjoy it; reliability of alpha = 0.87), identified motivation (four items; e.g., I engage with the workshop content because it gives me more options when choosing a career; alpha = 0.84), introjected motivation (five items; e.g., I engage with the workshop content because otherwise I would have a guilty feeling; alpha = 0.79), and external motivation (three items, e.g., I engage with the workshop content because I simply have to learn it; alpha = 0.74). Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree. To calculate the SDI-index, the sum of the self-determined regulation styles (intrinsic and identified) is subtracted from the sum of the external regulation styles (introjected and external), where intrinsic and external regulation are scored two times ( Thomas and Müller, 2011 ).

3.4.3 Motivation

Basic needs were measured in the posttest with the scale by Willems and Lewalter (2011) . The scale consists of three subscales: perceived competence (four items; e.g., during the workshop, I felt that I could meet the requirements; alpha = 0.90), perceived autonomy (five items; e.g., during the workshop, I felt that I had a lot of freedom; alpha = 0.75), and perceived autonomy regarding personal wishes and goals (APWG) (four items; e.g., during the workshop, I felt that the workshop was how I wish it would be; alpha = 0.93). We added all three subscales to one overall basic needs scale (alpha = 0.90). Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree.

Situational interest was measured in the posttest with the 12-item scale by Lewalter and Knogler (2014 ; Knogler et al., 2015 ; Lewalter, 2020 ; alpha = 0.84). The scale consists of two subscales: catch (six items; e.g., I found the workshop exciting; alpha = 0.81) and hold (six items; e.g., I would like to learn more about parts of the workshop; alpha = 0.80). Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree.

3.4.4 Cognitive load

In the posttest, CL was used to examine the mental load during the learning process. The intrinsic CL (three items; e.g., this task was very complex; alpha = 0.70) and extraneous CL (three items; e.g., in this task, it is difficult to identify the most important information; alpha = 0.61) are measured with the scales from Klepsch et al. (2017) . The germane CL (two items; e.g., the learning session contained elements that supported me to better understand the learning material; alpha = 0.72) is measured with the scale from Leppink et al. (2013) . Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree.

3.4.5 Attitudes toward worked examples

To measure how effective participants rated the WE, we used two scales related to the WE videos as instructional support. The first scale from Renkl (2001) relates to the usefulness of WE. The scale consists of four items (e.g., the explanations were helpful; alpha = 0.71). Two items were recoded because they were formulated negatively. The second scale is from Wachsmuth (2020) and relates to the participant’s evaluation of the WE. The scale consists of nine items (e.g., I always did what was explained in the learning videos; alpha = 0.76). Four items were recoded because they were formulated negatively. Participants could indicate their answers on a 5-point Likert scale ranging from 1 = completely disagree to 5 = completely agree.

3.5 Data analysis

An ANOVA was used to calculate if the variable’s prior knowledge and SDI index differed between the three groups. However, as no significant differences between the conditions were found [prior factual knowledge: F (2, 59) = 0.15, p  = 0.865, η 2  = 0.00 self-determination index: F (2, 59) = 0.19, p  = 0.829, η 2  = 0.00], they were not included as covariates in subsequent analyses.

Furthermore, a repeated measure, one-way analysis of variance (ANOVA), was conducted to compare the three treatment groups (no WE vs. faded WE vs. non-faded WE) regarding the increase in factual knowledge about the scientific observation method from pretest to posttest.

A MANOVA (multivariate analysis) was calculated with the three groups (no WE vs. non-faded WE vs. faded WE) as a fixed factor and the dependent variables being the practical application of the scientific observation method (first research question), situational interest, basic needs (second research question), and CL (third research question).

Additionally, to determine differences in applied knowledge even among the three groups, Bonferroni-adjusted post-hoc analyses were conducted.

The descriptive statistics between the three groups in terms of prior factual knowledge about the scientific observation method and the self-determination index are shown in Table 1 . The descriptive statistics revealed only small, non-significant differences between the three groups in terms of factual knowledge.

Table 1 . Means (standard deviations) of factual knowledge tests (pre- and posttest) and self-determination index for the three different groups.

The results of the ANOVA revealed that the overall increase in factual knowledge from pre- to posttest just misses significance [ F (1, 57) = 3.68, p  = 0.060, η 2  = 0 0.06]. Furthermore, no significant differences between the groups were found regarding the acquisition of factual knowledge from pre- to posttest [ F (2, 57) = 2.93, p  = 0.062, η 2  = 0.09].

An analysis of the descriptive statistics showed that the largest differences between the groups were found in applied knowledge (qualitative evaluation) and extraneous load (see Table 2 ).

Table 2 . Means (standard deviations) of dependent variables with the three different groups.

Results of the MANOVA revealed significant overall differences between the three groups [ F (12, 106) = 2.59, p  = 0.005, η 2  = 0.23]. Significant effects were found for the application of knowledge [ F (2, 57) = 13.26, p  = <0.001, η 2  = 0.32]. Extraneous CL just missed significance [ F (2, 57) = 2.68, p  = 0.065, η 2  = 0.09]. There were no significant effects for situational interest [ F (2, 57) = 0.44, p  = 0.644, η 2  = 0.02], basic needs [ F (2, 57) = 1.22, p  = 0.302, η 2  = 0.04], germane CL [ F (2, 57) = 2.68, p  = 0.077, η 2  = 0.09], and intrinsic CL [ F (2, 57) = 0.28, p  = 0.757, η 2  = 0.01].

Bonferroni-adjusted post hoc analysis revealed that the group without WE had significantly lower scores in the evaluation of the applied knowledge than the group with non-faded WE ( p  = <0.001, M diff  = −8.90, 95% CI [−13.47, −4.33]) and then the group with faded WE ( p  = <0.001, M diff  = −7.40, 95% CI [−11.97, −2.83]). No difference was found between the groups with faded and non-faded WE ( p  = 1.00, M diff  = −1.50, 95% CI [−6.07, 3.07]).

The descriptive statistics regarding the perceived usefulness of WE and participants’ evaluation of the WE revealed that the group with the faded WE rated usefulness slightly higher than the participants with non-faded WE and also reported a more positive evaluation. However, the results of a MANOVA revealed no significant overall differences [ F (2, 37) = 0.32, p  = 0.732, η 2  = 0 0.02] (see Table 3 ).

Table 3 . Means (standard deviations) of dependent variables with the three different groups.

5 Discussion

This study investigated the use of WE to support students’ acquisition of science observation. Below, the research questions are answered, and the implications and limitations of the study are discussed.

5.1 Results on factual and applied knowledge

In terms of knowledge gain (RQ1), our findings revealed no significant differences in participants’ results of the factual knowledge test both across all three groups and specifically between the two experimental groups. These results are in contradiction with related literature where WE had a positive impact on knowledge acquisition ( Renkl, 2014 ) and faded WE are considered to be more effective in knowledge acquisition and transfer, in contrast to non-faded WE ( Renkl et al., 2000 ; Renkl, 2014 ). A limitation of the study is the fact that the participants already scored very high on the pretest, so participation in the intervention would likely not yield significant knowledge gains due to ceiling effects ( Staus et al., 2021 ). Yet, nearly half of the students reported being novices in the field prior to the study, suggesting that the difficulty of some test items might have been too low. Here, it would be important to revise the factual knowledge test, e.g., the difficulty of the distractors in further study.

Nevertheless, with regard to application knowledge, the results revealed large significant differences: Participants of the two experimental groups performed better in conducting scientific observation steps than participants of the control group. In the experimental groups, the non-faded WE group performed better than the faded WE group. However, the absence of significant differences between the two experimental groups suggests that faded and non-faded WE used as double-content WE are suitable to teach applied knowledge about scientific observation in the learning domain ( Koenen, 2014 ). Furthermore, our results differ from the findings of Renkl et al. (2000) , in which the faded version led to the highest knowledge transfer. Despite the fact that the non-faded WE performed best in our study, the faded version of the WE was also appropriate to improve learning, confirming the findings of Renkl (2014) and Hesser and Gregory (2015) .

5.2 Results on learners’ motivation

Regarding participants’ motivation (RQ2; situational interest and basic needs), no significant differences were found across all three groups or between the two experimental groups. However, descriptive results reveal slightly higher motivation in the two experimental groups than in the control group. In this regard, our results confirm existing literature on a descriptive level showing that WE lead to higher learning-relevant motivation ( Paas et al., 2005 ; Van Harsel et al., 2019 ). Additionally, both experimental groups rated the usefulness of the WE as high and reported a positive evaluation of the WE. Therefore, we assume that even non-faded WE do not lead to over-instruction. Regarding the descriptive tendency, a larger sample might yield significant results and detect even small effects in future investigations. However, because this study also focused on comprehensive qualitative data analysis, it was not possible to evaluate a larger sample in this study.

5.3 Results on cognitive load

Finally, CL did not vary significantly across all three groups (RQ3). However, differences in extraneous CL just slightly missed significance. In descriptive values, the control group reported the highest extrinsic and lowest germane CL. The faded WE group showed the lowest extrinsic CL and a similar germane CL as the non-faded WE group. These results are consistent with Paas et al. (2003) and Renkl (2014) , reporting that WE can help to reduce the extraneous CL and, in return, lead to an increase in germane CL. Again, these differences were just above the significance level, and it would be advantageous to retest with a larger sample to detect even small effects.

Taken together, our results only partially confirm H1: the integration of WE (both faded and non-faded WE) led to a higher acquisition of application knowledge than the control group without WE, but higher factual knowledge was not found. Furthermore, higher motivation or different CL was found on a descriptive level only. The control group provided the basis for comparison with the treatment in order to investigate if there is an effect at all and, if so, how large the effect is. This is an important point to assess whether the effort of implementing WE is justified. Additionally, regarding H2, our results reveal no significant differences between the two WE conditions. We assume that the high complexity of the FA could play a role in this regard, which might be hard to handle, especially for beginners, so learners could benefit from support throughout (i.e., non-faded WE).

In addition to the limitations already mentioned, it must be noted that only one exemplary topic was investigated, and the sample only consisted of students. Since only the learning domain of the double-content WE was investigated, the exemplifying domain could also be analyzed, or further variables like motivation could be included in further studies. Furthermore, the influence of learners’ prior knowledge on learning with WE could be investigated, as studies have found that WE are particularly beneficial in the initial acquisition of cognitive skills ( Kalyuga et al., 2001 ).

6 Conclusion

Overall, the results of the current study suggest a beneficial role for WE in supporting the application of scientific observation steps. A major implication of these findings is that both faded and non-faded WE should be considered, as no general advantage of faded WE over non-faded WE was found. This information can be used to develop targeted interventions aimed at the support of scientific observation skills.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval was not required for the study involving human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants in accordance with the national legislation and the institutional requirements.

Author contributions

ML: Writing – original draft. SM: Writing – review & editing. JP: Writing – review & editing. JG: Writing – review & editing. DL: Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2024.1293516/full#supplementary-material

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Keywords: digital media, worked examples, scientific observation, motivation, cognitive load

Citation: Lechner M, Moser S, Pander J, Geist J and Lewalter D (2024) Learning scientific observation with worked examples in a digital learning environment. Front. Educ . 9:1293516. doi: 10.3389/feduc.2024.1293516

Received: 13 September 2023; Accepted: 29 February 2024; Published: 18 March 2024.

Reviewed by:

Copyright © 2024 Lechner, Moser, Pander, Geist and Lewalter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miriam Lechner, [email protected]

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Creating a Corporate Social Responsibility Program with Real Impact

• Emilio Marti,
• David Risi,
• Eva Schlindwein,
• Andromachi Athanasopoulou

Lessons from multinational companies that adapted their CSR practices based on local feedback and knowledge.

Exploring the critical role of experimentation in Corporate Social Responsibility (CSR), research on four multinational companies reveals a stark difference in CSR effectiveness. Successful companies integrate an experimental approach, constantly adapting their CSR practices based on local feedback and knowledge. This strategy fosters genuine community engagement and responsive initiatives, as seen in a mining company’s impactful HIV/AIDS program. Conversely, companies that rely on standardized, inflexible CSR methods often fail to achieve their goals, demonstrated by a failed partnership due to local corruption in another mining company. The study recommends encouraging broad employee participation in CSR and fostering a culture that values CSR’s long-term business benefits. It also suggests that sustainable investors and ESG rating agencies should focus on assessing companies’ experimental approaches to CSR, going beyond current practices to examine the involvement of diverse employees in both developing and adapting CSR initiatives. Overall, embracing a dynamic, data-driven approach to CSR is essential for meaningful social and environmental impact.

By now, almost all large companies are engaged in corporate social responsibility (CSR): they have CSR policies, employ CSR staff, engage in activities that aim to have a positive impact on the environment and society, and write CSR reports. However, the evolution of CSR has brought forth new challenges. A stark contrast to two decades ago, when the primary concern was the sheer neglect of CSR, the current issue lies in the ineffective execution of these practices. Why do some companies implement CSR in ways that create a positive impact on the environment and society, while others fail to do so? Our research reveals that experimentation is critical for impactful CSR, which has implications for both companies that implement CSR and companies that externally monitor these CSR activities, such as sustainable investors and ESG rating agencies.

• EM Emilio Marti is an associate professor at the Rotterdam School of Management, Erasmus University. His research focuses on corporate sustainability with a specific focus on sustainable investing.
• DR David Risi is a professor at the Bern University of Applied Sciences and a habilitated lecturer at the University of St. Gallen. His research focuses on how companies organize CSR and sustainability.
• ES Eva Schlindwein is a professor at the Bern University of Applied Sciences and a postdoctoral fellow at the University of Oxford. Her research focuses on how organizations navigate tensions between business and society.
• AA Andromachi Athanasopoulou is an associate professor at Queen Mary University of London and an associate fellow at the University of Oxford. Her research focuses on how individuals manage their leadership careers and make ethically charged decisions.

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