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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods.

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Types of Research – Tips and Examples

Published by Carmen Troy at August 16th, 2021 , Revised On October 26, 2023

“Research is an investigation conducted to seek knowledge and find solutions to scientific and social problems.”

It includes the collection of information from various sources. New research can contribute to existing knowledge.

The types of research can be categorised from the following perspectives;

• Application of the study
• Aim of the research
• Mode of inquiry
• Research approach

Types of Research According to the Application Perspective

The different types of research, according to the application perspective, include the following.

Basic Research

Primary research is conducted to increase knowledge. It is also known as theoretical research, pure research, and fundamental research. It provides in-depth knowledge about the scientific and logical explanations and their conclusions.

The results of the primary research are used as the base of applied research. It is based on  experiments  and observation. The results of basic research are published in peer-reviewed journals.

• What is global warming?
• How did the Universe begin?
• What do humans get stress?

Applied Research

Applied research is conducted to find solutions for practical problems. It uses the outcomes of basic research as its base. The results of applied research are applied immediately. It includes case studies, experimental research.

Example: Finding the solution to control air pollution.

Descriptive Research

Descriptive research  is carried out to describe current issues, programs, and provides information about the issue through  surveys  and various fact-finding methods.

It includes co-relational and comparative methods of research. It follows the Ex post facto research, which predicts the possible reasons behind the situation that has already occurred.

A researcher cannot control its variables and can report only about the current situation and its occurring.

Example: The widespread contaminated diseases in a specific area of the town. Investigation reveals that there is no trash removal system in that area. A researcher can hypothesise the reason that the improper trash removal system leads to the widespread of contaminated disease.

Analytical Research

In analytical research, a researcher can use the existing data, facts, and knowledge and critically analyses and evaluates the sources and material. It attempts to describe why a specific situation exists.

Example: Impact of video games on teenagers.

Explanatory

Explanatory research is conducted to know why and how two or more variables are interrelated. Researchers usually conduct experiments to know the effect of specific changes among two or more variables.

Example: A study to identify the impact of a nutritious diet on pregnant women.

Exploratory

Exploratory research is conducted to understand the nature of the problem. It does not focus on finding evidence or a conclusion of the problem. It studies the problem to explore the research in-depth and covers such topics which have not been studied before.

Example:  An investigation about the growing crimes against women in India.

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Types of Research According to the Mode of Inquiry

Qualitative research.

Qualitative research  is based on quality, and it looks in-depth at non-numerical data. It enables us to understand the comprehensive details of the problem. The researcher prepares open-ended questions to gather as much information as possible.

• Stress level among men and women.
• The obesity rate among teenagers.

Quantitative Research

Quantitative research is associated with the aspects of measurement, quantity, and extent. It follows the statistical, mathematical, and computational techniques in the form of numerical data such as percentages and statistics. The research is conducted on a large group of population.

• Find out the weight of students of the fifth standard
• Studying in government schools.

Types of Research According to the Research Approach

Longitudinal research.

Researchers collect the information at multiple points in time. Usually, a specific group of participants is selected and examined numerous times at various periods.

Example: If a researcher experiments on a group of women to find out the impact of a low carb diet within six months. The women’s weight and a health check-up will be done multiple times to get the evidence of the study.

Cross-Sectional Research

Cross-sectional research  gathers and compares the information from various groups of the population at the same point. It may not provide the exact reason and relationship between the subjects but gives a broad picture to study multiple groups at the same time.

Example: If a researcher wants to know the number of students studying in a school, he will get to know about the age groups, height, weight, and gender of the students at the same time.

Conceptual Research

It is associated with the concept and theory that describes the hypothesis being studied. It is based on  the inductive  approach of reasoning. It does not follow practical experiments. Philosophers, thinkers, logicians, and theorists use such research to discover new concepts and understand the existing knowledge.

Example: discoveries of Sir Isaac Newton and Einstein.

Empirical Research

It is also known as experimental research, which depends on observation and experience. It is based on the  deductive  approach of reasoning . A researcher focuses on gathering information about the facts, their sources and investigating the existing knowledge. Example: Is intermittent fasting the healthy weight loss option for women?

The researcher can come up with the result that a certain number of women lost their weight, and it improved their health. On the other hand, a certain number of women suffering from low blood pressure and diabetes didn’t lose weight, and they faced negative impacts of intermittent fasting on their health.

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Mixed Methods of Research

When you combine quantitative and qualitative methods of research, the resulting approach becomes mixed methods of research.

Over the last few decades, much of the research in the world of academia has been conducted using mixed methods. Due to its greater legitimacy, this particular technique has gained for several reasons, including the feeling that combining the two types of research can provide holistic and more dependable results.

Here is what mixed methods of research involve:

• Interpreting and investigating the information gathered through quantitative and qualitative techniques.
• There could be more than one stage of research. Depending on the topic of research, occasionally, it would be more appropriate to perform qualitative research in the first stage to figure out and investigate a problem to unveil key themes; and conduct quantitative research in stage two of the process for measuring relationships between the themes.

 Tips for Choosing the Right Type of Research

Choosing the right type of research is essential for producing relevant and actionable insights. The choice depends on your objectives, available resources, and the nature of the problem. Here are some tips to help you make the right decision:

Define your Research Objectives Clearly

• Descriptive Research: To describe the characteristics of certain phenomena.
• Exploratory Research: To explore a problem that hasn’t been studied in depth.
• Explanatory (or Causal) Research: To explain patterns of cause and effect.
• Predictive Research: To forecast future outcomes based on patterns.

Understand the Research Methods

• Quantitative Research: Employs structured data collection (e.g., surveys) to generate statistical data.
• Qualitative Research: Uses unstructured or semi-structured data collection methods (e.g., interviews, observations) to understand behaviour, motivations, etc.

Consider the Time Dimension

• Cross-sectional Studies: Capture data at a single point in time.
• Longitudinal Studies: Collect data over extended periods to observe changes.

Evaluate Available Resources

• Budget: Some research methods, like experimental research, may require more funding.
• Time: Exploratory or ethnographic studies may take longer than surveys.
• Expertise: Ensure you or your team possess the skills needed for your chosen research method.

Consider the Nature of the Problem

Complex problems may require mixed-methods research (a combination of qualitative and quantitative).

Review Existing Literature

Review existing literature before settling on a type to see what methodologies were previously employed for similar questions.

Think about Data Collection

Consider the best method to gather data: surveys, interviews, experiments, observations, etc. Your choice affects the research type.

Ethical Considerations:

Ensure your chosen method abides by ethical standards, especially when human subjects are involved.

Generalisability Vs. Depth

Quantitative methods often allow for generalizability, while qualitative methods provide depth and detail.

Pilot Testing

If unsure, run a pilot study to test your chosen method’s feasibility and utility.

Stay Open to Adaptation

Sometimes, initial research can lead to unforeseen insights or complexities. Be prepared to adjust your approach if needed.

Seek Feedback

Discuss your research approach with colleagues, mentors, or experts in the field. They might offer valuable insights or identify potential pitfalls.

Stay Updated

Research methods evolve. Stay updated with the latest techniques, tools, and best practices in your field.

Frequently Asked Questions

What is research.

Research is a systematic inquiry aimed at discovering, interpreting, and revising knowledge about specific phenomena. It involves formulating hypotheses, collecting data, and analysing results to generate new insights or validate existing theories. Conducted in various fields, research can be empirical, theoretical, or experimental and is fundamental for informed decision-making.

What are the different Types of Research?

Different types of research include:

• Descriptive: Describe and analyze phenomena.
• Experimental: Manipulate variables to establish causation.
• Correlational: Examine relationships between variables.
• Qualitative: Gather insights and understanding.
• Quantitative: Use numerical data for analysis.
• Case study, survey, ethnography, and more.

What is research design?

Research design is a structured blueprint for conducting a study, outlining how data will be collected, analysed, and interpreted. It determines the overall strategy and approach to obtain valid, accurate, and reliable results. Research design encompasses choices about type (e.g., experimental, observational), method (qualitative, quantitative), and data collection procedures.

What is survey?

A survey is a research method used to gather data from a predefined group by asking specific questions. Surveys can be conducted using various mediums, such as face-to-face interviews, phone calls, or online questionnaires. They are valuable for collecting descriptive, quantitative, or qualitative information and gauging public opinion or behaviours.

What is research method?

A research method is a systematic approach used by researchers to gather, analyse, and interpret data relevant to their study. It dictates how information is collected and evaluated to answer specific research questions. Methods can be qualitative, quantitative, or mixed and include techniques like surveys, experiments, case studies, and interviews.

What is exploratory research?

Exploratory research is an initial study designed to clarify and define the nature of a problem. It’s used when researchers have a limited understanding of the topic. Instead of seeking definitive answers, it aims to identify patterns, ideas, or hypotheses. Methods often include literature reviews, qualitative interviews, and observational studies.

What is the purpose of research?

The purpose of research is to discover, interpret, or revise knowledge on specific topics or phenomena. It seeks to answer questions, validate theories, or find solutions to problems. Research enhances understanding, informs decision-making, guides policies, drives innovation, and contributes to academic, scientific, and societal advancement. It’s fundamental for evidence-based practices.

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This post provides the key disadvantages of secondary research so you know the limitations of secondary research before making a decision.

Experimental research refers to the experiments conducted in the laboratory or under observation in controlled conditions. Here is all you need to know about experimental research.

A meta-analysis is a formal, epidemiological, quantitative study design that uses statistical methods to generalise the findings of the selected independent studies.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
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• The C.A.R.S. Model
• Background Information
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• Qualitative Methods
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• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Home » Research Methods – Types, Examples and Guide

Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

Definition:

Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.

Types of Research Methods

Types of Research Methods are as follows:

Qualitative research Method

Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.

Quantitative Research Method

Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.

Mixed Method Research

Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.

Key Differences Between Research Methods

The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods

Examples of Research Methods

Examples of Research Methods are as follows:

Qualitative Research Example:

A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.

Quantitative Research Example:

A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.

Mixed Research Example:

A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.

Applications of Research Methods

Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:

• Psychology : Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they respond to different stimuli, and how their brains process information.
• Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
• Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
• Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
• Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
• Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
• Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.

Purpose of Research Methods

Research methods serve several purposes, including:

• Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
• Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
• Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
• Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
• Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
• Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
• Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.

When to Use Research Methods

Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.

Here are some situations when research methods may be appropriate:

• To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
• To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
• To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
• To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
• To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.

Advantages of Research Methods

Research methods provide several advantages, including:

• Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
• Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
• Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
• Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
• Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
• Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Types of Research – Definition & Methods

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The methodology of academic writing encompasses various types of research, each with its unique approach and purpose. From qualitative to quantitative, experimental to correlational, understanding these different types is crucial to select the right approach for your study. Our guide will elucidate these methods. Good research skills are a must for students, so you need to be familiar with the different types of research you can use.

Inhaltsverzeichnis

• 1 Types of Research – In a Nutshell
• 2 Definition: Types of research
• 3 Types of research – Purpose
• 4 Types of research – Depth of scope
• 5 Types of research – Type of data
• 6 Types of research – Manipulation of variables
• 7 Types of research – Type of inference
• 8 Types of research – Time of execution
• 9 Types of research – Sources of information
• 10 Types of research – State of data

Types of Research – In a Nutshell

• Research skills are essential for college and university students.
• There are different types of research you can use, depending on your topic, course, and research question.
• You can use multiple types of research in the same study, as long as they’re suited to your topic and research question.

Definition: Types of research

There are many different types of research based on their purpose, the type of data involved, the sources used, etc.

Which types of research you use depends on your area of study and the scope of your project.

Someone studying anthropology would use qualitative research , like different types of interviews , whereas someone studying physics would rely on quantitative research .

Types of research – Purpose

Theoretical research.

This type of research deepens or expands existing knowledge, but it doesn’t try to solve specific problems.

A study of 17th century literature in southern Italy.

Applied Research

Applied research focuses on real-world issues and its conclusions have practical applications.

A psychology student investigating how to improve memory in school-age children.

Types of research – Depth of scope

Exploratory research.

This type of research aims to provide a general framework around a particular topic, without going into too much detail.

A Ph.D. student would carry out exploratory research at the beginning of their studies, to determine the best data collection method or to find out what others have written about the topic.

Descriptive research

Descriptive research gathers extensive data about a topic or about research subjects, so it has a deeper scope than exploratory research .

A marketing student researching changes in smartphone buyer behavior.

Explanatory research

This type of research focuses on answering “why”-questions. This requires a detailed cause-and-effect assessment.

A linguistics student investigating why second language acquisition is delayed in learners over 30 years old.

Correlational research

This research compares two facts or data sets, to find meaningful connections between them.

A sports medicine student examining whether sleep patterns affect an athlete’s performance.

Types of research – Type of data

Qualitative research.

Data consists of words or concepts, which can be assessed and interpreted but not objectively measured.

A focus group on retired people’s attitude to exercise.

Quantitative research

This research relies on any data units that can be objectively measured.

A statistical analysis of economic data.

Types of research – Manipulation of variables

Experimental research.

Independent variables are applied to dependent variables to determine their impact and to test a hypothesis.

Observing the effects of independent variable A (two groups of 70+ patients, one group taking X medication and the other taking a placebo) on dependent variable B (lower blood pressure).

Non-Experimental research

Non-experimental research doesn’t manipulate the independent variable, and it’s not always necessary to find a correlation between variables.

A study of visual accuracy in people who’ve had laser eye surgery.

Quasi-experimental research

This is similar to experimental research, but the main difference is that in quasi-experimental research , research subjects aren’t randomly assigned to different groups.

Investigating why some office workers use productivity apps and some don’t.

Types of research – Type of inference

Deductive research.

In deductive research , inferences or conclusions are reached by testing a theory and/or a hypothesis. Data is collected and analyzed to either prove or disprove the hypothesis.

Investigating the hypothesis that airline delays are more frequent in summer.

Inductive research

The starting point isn’t a theory or hypothesis, but a set of observations that can later be turned into generalizations.

A number of flights are delayed during the summer months. Inductive research observes the pattern and concludes by stating a possible theory about why this happens.

Types of research – Time of execution

Diachronic research.

Diachronic research studies a fact or phenomenon over a period of time.

A study of how English slang changed between 1950 and 1980.

Synchronous research

Synchronous research examines a fact or phenomenon as it happens during a specific point in time.

How second-generation Indian immigrants use English slang.

Types of research – Sources of information

Primary research.

This means that your data comes from your own research.

Interviewing shoppers to find out what are their purchasing criteria.

Secondary research

This means that your research data comes from existing sources.

A study of census data between 1900-1930.

Types of research – State of data

Documentary.

Data consists of existing official documents, photos, personal communications, or audio files.

Field data is created and collected in real-world settings, for example in the form of interviews, surveys, etc.

Lab data is usually collected in lab settings under controlled conditions.

Mixed-Method: Documentary, field, and/or laboratory

This refers to the combination of all three types of research in a single study.

What are the two main types of research?

In terms of purpose, the 2 main types of research are theoretical and applied. In terms of the data used, research can be classified into qualitative and quantitative.

Can I use different types of research in my work?

Yes, using several types of research is possible as long as they’re compatible and suited to your topic and research question.

What do I need to do research?

A research question, data, and a framework to analyze the data.

Do I need special software?

Not always. This is more common if you’re doing quantitative research.

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1.5: Type of Research Designs

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• Foster et al.
• University of Missouri-St. Louis, Rice University, & University of Houston, Downtown Campus via University of Missouri’s Affordable and Open Access Educational Resources Initiative

Research studies come in many forms, and, just like with the different types of data we have, different types of studies tell us different things. The choice of research design is determined by the research question and the logistics involved. Though a complete understanding of different research designs is the subject for at least one full class, if not more, a basic understanding of the principles is useful here. There are three types of research designs we will discuss: experimental, quasiexperimental, and non-experimental.

Experimental Designs

If we want to know if a change in one variable causes a change in another variable, we must use a true experiment. An experiment is defined by the use of random assignment to treatment conditions and manipulation of the independent variable. To understand what this means, let’s look at an example: A clinical researcher wants to know if a newly developed drug is effective in treating the flu. Working with collaborators at several local hospitals, she randomly samples 40 flu patients and randomly assigns each one to one of two conditions: Group A receives the new drug and Group B received a placebo. She measures the symptoms of all participants after 1 week to see if there is a difference in symptoms between the groups. In the example, the independent variable is the drug treatment; we manipulate it into 2 levels: new drug or placebo. Without the researcher administering the drug (i.e. manipulating the independent variable), there would be no difference between the groups. Each person, after being randomly sampled to be in the research, was then randomly assigned to one of the 2 groups. That is, random sampling and random assignment are not the same thing and cannot be used interchangeably. For research to be a true experiment, random assignment must be used. For research to be representative of the population, random sampling must be used. The use of both techniques helps ensure that there are no systematic differences between the groups, thus eliminating the potential for sampling bias. The dependent variable in the example is flu symptoms. Barring any other intervention, we would assume that people in both groups, on average, get better at roughly the same rate. Because there are no systematic differences between the 2 groups, if the researcher does find a difference in symptoms, she can confidently attribute it to the effectiveness of the new drug.

Quasi-Experimental Designs

Quasi-experimental research involves getting as close as possible to the conditions of a true experiment when we cannot meet all requirements. Specifically, a quasiexperiment involves manipulating the independent variable but not randomly assigning people to groups. There are several reasons this might be used. First, it may be unethical to deny potential treatment to someone if there is good reason to believe it will be effective and that the person would unduly suffer if they did not receive it. Alternatively, it may be impossible to randomly assign people to groups. Consider the following example: A professor wants to test out a new teaching method to see if it improves student learning. Because he is teaching two sections of the same course, he decides to teach one section the traditional way and the other section using the new method. At the end of the semester, he compares the grades on the final for each class to see if there is a difference. In this example, the professor has manipulated his teaching method, which is the independent variable, hoping to find a difference in student performance, the dependent variable. However, because students enroll in courses, he cannot randomly assign the students to a particular group, thus precluding using a true experiment to answer his research question. Because of this, we cannot know for sure that there are no systematic differences between the classes other than teaching style and therefore cannot determine causality.

Non-Experimental Designs

Finally, non-experimental research (sometimes called correlational research) involves observing things as they occur naturally and recording our observations as data. Consider this example: A data scientist wants to know if there is a relation between how conscientious a person is and whether that person is a good employee. She hopes to use this information to predict the job performance of future employees by measuring their personality when they are still job applicants. She randomly samples volunteer employees from several different companies, measuring their conscientiousness and having their bosses rate their performance on the job. She analyzes this data to find a relation. Here, it is not possible to manipulate conscientious, so the researcher must gather data from employees as they are in order to find a relation between her variables. Although this technique cannot establish causality, it can still be quite useful. If the relation between conscientiousness and job performance is consistent, then it doesn’t necessarily matter is conscientiousness causes good performance or if they are both caused by something else – she can still measure conscientiousness to predict future performance. Additionally, these studies have the benefit of reflecting reality as it actually exists since we as researchers do not change anything.

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1.9: Types of Research Studies and How To Interpret Them

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• Alice Callahan, Heather Leonard, & Tamberly Powell
• Lane Community College via OpenOregon

The field of nutrition is dynamic, and our understanding and practices are always evolving. Nutrition scientists are continuously conducting new research and publishing their findings in peer-reviewed journals. This adds to scientific knowledge, but it’s also of great interest to the public, so nutrition research often shows up in the news and other media sources. You might be interested in nutrition research to inform your own eating habits, or if you work in a health profession, so that you can give evidence-based advice to others. Making sense of science requires that you understand the types of research studies used and their limitations.

The Hierarchy of Nutrition Evidence

Researchers use many different types of study designs depending on the question they are trying to answer, as well as factors such as time, funding, and ethical considerations. The study design affects how we interpret the results and the strength of the evidence as it relates to real-life nutrition decisions. It can be helpful to think about the types of studies within a pyramid representing a hierarchy of evidence, where  the  studies at the bottom of the pyramid usually give us the weakest evidence with the least relevance to real-life nutrition decisions, and the studies at the top offer the strongest evidence, with the most relevance to real-life nutrition  decisions .

The pyramid also represents a few other general ideas. There tend to be more studies published using the methods at the bottom of the pyramid, because they require less time, money, and other resources. When researchers want to test a new hypothesis , they often start with the study designs at the bottom of the pyramid , such as in vitro, animal, or observational studies. Intervention studies are more expensive and resource-intensive, so there are fewer of these types of studies conducted. But they also give us higher quality evidence, so they’re an important next step if observational and non-human studies have shown promising results. Meta-analyses and systematic reviews combine the results of many studies already conducted, so they help researchers summarize scientific knowledge on a topic.

Non-Human Studies: In Vitro & Animal Studies

The simplest form of nutrition research is an in vitro study . In vitro means “within glass,” (although plastic is used more commonly today) and these experiments are conducted within flasks, dishes, plates, and test tubes. These studies are performed on isolated cells or tissue samples, so they’re less expensive and time-intensive than animal or human studies. In vitro studies are vital for zooming in on biological mechanisms, to see how things work at the cellular or molecular level. However, these studies shouldn’t be used to draw conclusions about how things work in humans (or even animals), because we can’t assume that the results will apply to a whole, living organism.

Animal studies are one form of in vivo research, which translates to “within the living.” Rats and mice are the most common animals used in nutrition research. Animals are often used in research that would be unethical to conduct in humans. Another advantage of animal dietary studies is that researchers can control exactly what the animals eat. In human studies, researchers can tell subjects what to eat and even provide them with the food, but they may not stick to the planned diet. People are also not very good at estimating, recording, or reporting what they eat and in what quantities. In addition, animal studies typically do not cost as much as human studies.

There are some important limitations of animal research. First, an animal’s metabolism and physiology are different from humans. Plus, animal models of disease (cancer, cardiovascular disease, etc.), although similar, are different from human diseases. Animal research is considered preliminary, and while it can be very important to the process of building scientific understanding and informing the types of studies that should be conducted in humans, animal studies shouldn’t be considered relevant to real-life decisions about how people eat.

Observational Studies

Observational studies  in human nutrition collect information on people’s dietary patterns or nutrient intake and look for associations with health outcomes. Observational studies do not give participants a treatment or intervention; instead, they look at what they’re already doing and see how it relates to their health. These types of study designs can only identify  correlations  (relationships) between nutrition and health; they can’t show that one factor  causes  another. (For that, we need intervention studies, which we’ll discuss in a moment.) Observational studies that describe factors correlated with human health are also called  epidemiological studies . 1

One example of a nutrition hypothesis that has been investigated using observational studies is that eating a Mediterranean diet reduces the risk of developing cardiovascular disease. (A Mediterranean diet focuses on whole grains, fruits and vegetables, beans and other legumes, nuts, olive oil, herbs, and spices. It includes small amounts of animal protein (mostly fish), dairy, and red wine. 2 ) There are three main types of observational studies, all of which could be used to test hypotheses about the Mediterranean diet:

• Cohort studies follow a group of people (a cohort) over time, measuring factors such as diet and health outcomes. A cohort study of the Mediterranean diet would ask a group of people to describe their diet, and then researchers would track them over time to see if those eating a Mediterranean diet had a lower incidence of cardiovascular disease.
• Case-control studies compare a group of cases and controls, looking for differences between the two groups that might explain their different health outcomes. For example, researchers might compare a group of people with cardiovascular disease with a group of healthy controls to see whether there were more controls or cases that followed a Mediterranean diet.
• Cross-sectional studies collect information about a population of people at one point in time. For example, a cross-sectional study might compare the dietary patterns of people from different countries to see if diet correlates with the prevalence of cardiovascular disease in the different countries.

Prospective cohort studies, which enroll a cohort and follow them into the future, are usually considered the strongest type of observational study design. Retrospective studies look at what happened in the past, and they’re considered weaker because they rely on people’s memory of what they ate or how they felt in the past. There are several well-known examples of prospective cohort studies that have described important correlations between diet and disease:

• Framingham Heart Study : Beginning in 1948, this study has followed the residents of Framingham, Massachusetts to identify risk factors for heart disease.
• Health Professionals Follow-Up Study : This study started in 1986 and enrolled 51,529 male health professionals (dentists, pharmacists, optometrists, osteopathic physicians, podiatrists, and veterinarians), who complete diet questionnaires every 2 years.
• Nurses Health Studies : Beginning in 1976, these studies have enrolled three large cohorts of nurses with a total of 280,000 participants. Participants have completed detailed questionnaires about diet, other lifestyle factors (smoking and exercise, for example), and health outcomes.

Observational studies have the advantage of allowing researchers to study large groups of people in the real world, looking at the frequency and pattern of health outcomes and identifying factors that correlate with them. But even very large observational studies may not apply to the population as a whole. For example, the Health Professionals Follow-Up Study and the Nurses Health Studies include people with above-average knowledge of health. In many ways, this makes them ideal study subjects, because they may be more motivated to be part of the study and to fill out detailed questionnaires for years. However, the findings of these studies may not apply to people with less baseline knowledge of health.

We’ve already mentioned another important limitation of observational studies—that they can only determine correlation, not causation. A prospective cohort study that finds that people eating a Mediterranean diet have a lower incidence of heart disease can only show that the Mediterranean diet is correlated with lowered risk of heart disease. It can’t show that the Mediterranean diet directly prevents heart disease. Why? There are a huge number of factors that determine health outcomes such as heart disease, and other factors might explain a correlation found in an observational study. For example, people who eat a Mediterranean diet might also be the same kind of people who exercise more, sleep more, have higher income (fish and nuts can be expensive!), or be less stressed. These are called confounding factors ; they’re factors that can affect the outcome in question (i.e., heart disease) and also vary with the factor being studied (i.e., Mediterranean diet).

Intervention Studies

Intervention studies , also sometimes called experimental studies or clinical trials, include some type of treatment or change imposed by the researcher. Examples of interventions in nutrition research include asking participants to change their diet, take a supplement, or change the time of day that they eat. Unlike observational studies, intervention studies can provide evidence of cause and effect , so they are higher in the hierarchy of evidence pyramid.

The gold standard for intervention studies is the randomized controlled trial (RCT) . In an RCT, study subjects are recruited to participate in the study. They are then randomly assigned into one of at least two groups, one of which is a control group (this is what makes the study controlled ). In an RCT to study the effects of the Mediterranean diet on cardiovascular disease development, researchers might ask the control group to follow a low-fat diet (typically recommended for heart disease prevention) and the intervention group to eat a Mediterrean diet. The study would continue for a defined period of time (usually years to study an outcome like heart disease), at which point the researchers would analyze their data to see if more people in the control or Mediterranean diet had heart attacks or strokes. Because the treatment and control groups were randomly assigned, they should be alike in every other way except for diet, so differences in heart disease could be attributed to the diet. This eliminates the problem of confounding factors found in observational research, and it’s why RCTs can provide evidence of causation, not just correlation.

Imagine for a moment what would happen if the two groups weren’t randomly assigned. What if the researchers let study participants choose which diet they’d like to adopt for the study? They might, for whatever reason, end up with more overweight people who smoke and have high blood pressure in the low-fat diet group, and more people who exercised regularly and had already been eating lots of olive oil and nuts for years in the Mediterranean diet group. If they found that the Mediterranean diet group had fewer heart attacks by the end of the study, they would have no way of knowing if this was because of the diet or because of the underlying differences in the groups. In other words, without randomization, their results would be compromised by confounding factors, with many of the same limitations as observational studies.

In an RCT of a supplement, the control group would receive a placebo—a  “fake” treatment that contains no active ingredients, such as a sugar pill. The use of a placebo is necessary in medical research because of a phenomenon known as the placebo effect. The placebo effect results in a beneficial effect because of a subject’s belief in the treatment, even though there is no treatment actually being administered.

Blinding is a technique to prevent bias in intervention studies. In a study without blinding, the subject and the researchers both know what treatment the subject is receiving. This can lead to bias if the subject or researcher have expectations about the treatment working, so these types of trials are used less frequently. It’s best if a study is double-blind , meaning that neither the researcher nor the subject know what treatment the subject is receiving. It’s relatively simple to double-blind a study where subjects are receiving a placebo or treatment pill, because they could be formulated to look and taste the same. In a single-blind study , either the researcher or the subject knows what treatment they’re receiving, but not both. Studies of diets—such as the Mediterranean diet example—often can’t be double-blinded because the study subjects know whether or not they’re eating a lot of olive oil and nuts. However, the researchers who are checking participants’ blood pressure or evaluating their medical records could be blinded to their treatment group, reducing the chance of bias.

Like all studies, RCTs and other intervention studies do have some limitations. They can be difficult to carry on for long periods of time and require that participants remain compliant with the intervention. They’re also costly and often have smaller sample sizes. Furthermore, it is unethical to study certain interventions. (An example of an unethical intervention would be to advise one group of pregnant mothers to drink alcohol to determine its effects on pregnancy outcomes, because we know that alcohol consumption during pregnancy damages the developing fetus.)

VIDEO: “ Not all scientific studies are created equal ” by David H. Schwartz, YouTube (April 28, 2014), 4:26.

Meta-Analyses and Systematic Reviews

At the top of the hierarchy of evidence pyramid are systematic reviews and meta-analyses .  You can think of these as “studies of studies.” They attempt to combine all of the relevant studies that have been conducted on a research question and summarize their overall conclusions. Researchers conducting a  systematic review  formulate a research question and then systematically and independently identify, select, evaluate, and synthesize all high-quality evidence that relates to the research question. Since systematic reviews combine the results of many studies, they help researchers produce more reliable findings. A  meta-analysis  is a type of systematic review that goes one step further, combining the data from multiple studies and using statistics to summarize it, as if creating a mega-study from many smaller studies . 4

However, even systematic reviews and meta-analyses aren’t the final word on scientific questions. For one thing, they’re only as good as the studies that they include. The  Cochrane Collaboration  is an international consortium of researchers who conduct systematic reviews in order to inform evidence-based healthcare, including nutrition, and their reviews are among the most well-regarded and rigorous in science. For the most recent Cochrane review of the Mediterranean diet and cardiovascular disease, two authors independently reviewed studies published on this question. Based on their inclusion criteria, 30 RCTs with a total of 12,461 participants were included in the final analysis. However, after evaluating and combining the data, the authors concluded that “despite the large number of included trials, there is still uncertainty regarding the effects of a Mediterranean‐style diet on cardiovascular disease occurrence and risk factors in people both with and without cardiovascular disease already.” Part of the reason for this uncertainty is that different trials found different results, and the quality of the studies was low to moderate. Some had problems with their randomization procedures, for example, and others were judged to have unreliable data. That doesn’t make them useless, but it adds to the uncertainty about this question, and uncertainty pushes the field forward towards more and better studies. The Cochrane review authors noted that they found seven ongoing trials of the Mediterranean diet, so we can hope that they’ll add more clarity to this question in the future. 5

Science is an ongoing process. It’s often a slow process, and it contains a lot of uncertainty, but it’s our best method of building knowledge of how the world and human life works. Many different types of studies can contribute to scientific knowledge. None are perfect—all have limitations—and a single study is never the final word on a scientific question. Part of what advances science is that researchers are constantly checking each other’s work, asking how it can be improved and what new questions it raises.

Attributions:

• “Chapter 1: The Basics” from Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR , CC BY-NC-SA 4.0
• “ The Broad Role of Nutritional Science ,” section 1.3 from the book An Introduction to Nutrition (v. 1.0), CC BY-NC-SA 3.0

References:

• 1 Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica , 24 (2), 199–210. https://doi.org/10.11613/BM.2014.022
• 2 Harvard T.H. Chan School of Public Health. (2018, January 16). Diet Review: Mediterranean Diet . The Nutrition Source. https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/mediterranean-diet/
• 3 Ross, R., Gray, C. M., & Gill, J. M. R. (2015). Effects of an Injected Placebo on Endurance Running Performance. Medicine and Science in Sports and Exercise , 47 (8), 1672–1681. https://doi.org/10.1249/MSS.0000000000000584
• 4 Hooper, A. (n.d.). LibGuides: Systematic Review Resources: Systematic Reviews vs Other Types of Reviews . Retrieved February 7, 2020, from //libguides.sph.uth.tmc.edu/c.php?g=543382&p=5370369
• 5 Rees, K., Takeda, A., Martin, N., Ellis, L., Wijesekara, D., Vepa, A., Das, A., Hartley, L., & Stranges, S. (2019). Mediterranean‐style diet for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews , 3 . doi.org/10.1002/14651858.CD009825.pub3
• Figure 2.3. The hierarchy of evidence by Alice Callahan, is licensed under CC BY 4.0
• Research lab photo by National Cancer Institute on Unsplas h ; mouse photo by vaun0815 on Unsplash
• Figure 2.4. “Placebo effect example” by Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR

The Two Main Research Types – A Brief Overview

In this module, we will have a short overview of the two main types of research before we dive further into learning more about each in detail.

Content includes:

• Qualitative methods brief introductory overview
• Quantitative methods brief introductory overview

Objectives:

• Identify the three main types of qualitative research.
• Describe the processes of inductive versus deductive reasoning, and the types of research for both.
• Distinguish quantitative experimental and nonexperimental research.

Here we go! We are now going to start learning a bit about research. Remember, research is the underpinning for EBP. Research provides the evidence, and EBP takes that evidence and embeds it into practice to improve clinical outcomes.

The two major classes of research are:

• Qualitative Research – subjective, seeks a human’s experience as a narrative
• Quantitative Research – objective, seeks to statistically make inferences about a sample to generalize to the larger population

We need to have a solid understanding of the difference between the two main types of research before we study the nuances of each.

Three Main Types of Qualitative Research

Qualitative Research : Qualitative research is rooted in research that originated in anthropology, sociology, and psychology. Qualitative research is not experimental, it seeks to understand the lived experiences in humans and seeks to understanding meaning, and it is subjective in nature. The overarching goal of qualitative research is theory-generating. It is an inductive process (inductive reasoning). Most often, qualitative research features an interview style. This allows the researcher to ask open-ended questions and the participants share their experiences and/or explanation of particular meanings in life.

Qualitative research differs from quantitative research in that:

• It is completely subjective .
• It utilizes an inductive   (versus deductive) approach.
• It does not utilize a hypothesis.
• It generates a theory from the data to explain the social phenomenon that the researchers were interested in.
• The researcher is involved with the participants for data collection.
• The data is analyzed with a thematic nature. That is, themes from the collected narratives are analyzed to see trends or themes in what the participants shared.
• The results are not generalizable to the population.

There are three types of qualitative research designs:

Inductive versus Deductive Reasoning

The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory.

Think of inductive (theory producing) as to qualitative research and deductive (theory testing) as to quantitative research.

Inductive reasoning moves from specific observations to broad generalizations, and deductive reasoning the other way around.

Both approaches are used in various types of research, and it’s not uncommon to combine them in one large study.

Here is a qualitative study in which the researchers conducted interviews in order to obtain the subjective perspectives of the participants.

Quantitative Experimental and Nonexperimental Research.

Quantitative Research: In quantitative research, the goal is to utilize the statistical data to generalize results to the population studied. Some key features include utilizing the statistics to help answer the clinical question and determine whether the hypothesis is indeed statistically supported.

There are two main types of quantitative research:

• Experimental : In experimental research, the researcher introduces  an intervention or treatment.
• Non-Experimental : In non-experimental research, the researcher does not introduce an intervention or treatment, but instead acts as a bystander. Meaning, they collect data without introducing a treatment.

We will explore those two types in much detail in the next module.

Quantitative research differs from qualitative research in that:

• It is completely objective .
• It utilizes a deductive   (versus inductive) approach.
• It utilizes a hypothesis(es).
• It tests a theory.
• The researcher is usually not directly involved with the participants for data collection in order to minimize bias.
• The data is analyzed statistically in order to generalize results to the larger population.

Experimental Research : In the following article, the researchers introduced an intervention, which was a “Program for Enhancing the Positive Aspects of Caregiving” (a particular education program).

Non-experimental Research : In the following article, the researchers did not introduce an intervention or treatment. They handed out surveys for the participants to complete about their activity and depression levels.

Video: Qualitative Types and Experimental/Nonexperimental Research

In summary, there are two main approaches to research designs: Quantitative and qualitative. They each seek to answer questions, but quantitative research is meant to generalize its findings to the population whereas qualitative research seeks to understand phenomenon and develop theories about the human lived experiences.

References & Attribution

“ Light bulb doodle ” by rawpixel licensed CC0

“ Orange flame ” by rawpixel licensed CC0 .

Chen, P., Nunez-Smith, M., Bernheim, S… (2010). Professional experiences of international medical graduates practicing primary care in the United States. Journal of General Internal Medicine, 25 (9), 947-53. 

Haedtke, C., Smith, M., VanBuren, J., Kein, D., Turvey, C. (2017). The relationships among pain, depression, and physical activity in patients with heart failure. Journal of Cardiovascular Nursing, 32 (5), E21-E25.

Pankong, O., Pothiban, L., Sucamvang, K., Khampolsiri, T. (2018). A randomized controlled trial of enhancing positive aspects of caregiving in Thai dementia caregivers for dementia. Pacific Rim Internal Journal of Nursing Res, 22 (2), 131-143.

Evidence-Based Practice & Research Methodologies Copyright © by Tracy Fawns is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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7.1: Types of research

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• Page ID 25638

• Matthew DeCarlo
• Radford University via Open Social Work Education

Learning Objectives

• Differentiate among exploratory, descriptive, and explanatory research studies

A recent news story about college students’ addictions to electronic gadgets (Lisk, 2011) [1] describes findings from some current research by Professor Susan Moeller and colleagues from the University of Maryland ( http://withoutmedia.wordpress.com ). The story raises a number of interesting questions. Just what sorts of gadgets are students addicted to? How do these addictions work? Why do they exist, and who is most likely to experience them?

Social science research is great for answering just these sorts of questions. But in order to answer our questions well, we must take care in designing our research projects. In this chapter, we’ll consider what aspects of a research project should be considered at the beginning, including specifying the goals of the research, the components that are common across most research projects, and a few other considerations.

One of the first things to think about when designing a research project is what you hope to accomplish, in very general terms, by conducting the research. What do you hope to be able to say about your topic? Do you hope to gain a deep understanding of whatever phenomenon it is that you’re studying, or would you rather have a broad, but perhaps less deep, understanding? Do you want your research to be used by policymakers or others to shape social life, or is this project more about exploring your curiosities? Your answers to each of these questions will shape your research design.

Exploration, description, and explanation

You’ll need to decide in the beginning phases whether your research will be exploratory, descriptive, or explanatory. Each has a different purpose, so how you design your research project will be determined in part by this decision.

Researchers conducting exploratory research are typically at the early stages of examining their topics. These sorts of projects are usually conducted when a researcher wants to test the feasibility of conducting a more extensive study and to figure out the “lay of the land” with respect to the particular topic. Perhaps very little prior research has been conducted on this subject. If this is the case, a researcher may wish to do some exploratory work to learn what method to use in collecting data, how best to approach research subjects, or even what sorts of questions are reasonable to ask. A researcher wanting to simply satisfy her own curiosity about a topic could also conduct exploratory research. In the case of the study of college students’ addictions to their electronic gadgets, a researcher conducting exploratory research on this topic may simply wish to learn more about students’ use of these gadgets. Because these addictions seem to be a relatively new phenomenon, an exploratory study of the topic might make sense as an initial first step toward understanding it.

It is important to note that exploratory designs do not make sense for topic areas with a lot of existing research. For example, the question “What are common interventions for parents who neglect their children?” would not make much sense as a research question. One could simply look at journal articles and textbooks to see what interventions are commonly used with this population. Exploratory questions are best suited to topics that have not been studied. Students may sometimes say there is not much literature on their chosen topic, when there is in fact a large body of literature on that topic. However, that said, there are a few students each semester who pick a topic for which there is little existing research. Perhaps, if you were looking at child neglect interventions for parents who identify as transgender or parents who are refugees from the Syrian civil war, less would be known about child neglect for those specific populations. In that case, an exploratory design would make sense as there is less literature to guide your study.

Another purpose of research is to describe or define a particular phenomenon, termed descriptive research . For example, a social work researcher may want to understand what it means to be a first-generation college student or a resident in a psychiatric group home. In this case, descriptive research would be an appropriate strategy. A descriptive study of college students’ addictions to their electronic gadgets, for example, might aim to describe patterns in how many hours students use gadgets or which sorts of gadgets students tend to use most regularly.

Researchers at the Princeton Review conduct descriptive research each year when they set out to provide students and their parents with information about colleges and universities around the United States. They describe the social life at a school, the cost of admission, and student-to-faculty ratios (to name just a few of the categories reported). Although students and parents may be able to obtain much of this information on their own, having access to the data gathered by a team of researchers is much more convenient and less time consuming.

Social workers often rely on descriptive research to tell them about their service area. Keeping track of the number of children receiving foster care services, their demographic makeup (e.g., race, gender), and length of time in care are excellent examples of descriptive research. On a more macro-level, the Centers for Disease Control provides a remarkable amount of descriptive research on mental and physical health conditions. In fact, descriptive research has many useful applications, and you probably rely on findings from descriptive research without even being aware that that is what you are doing.

Finally, social work researchers often aim to explain why particular phenomena work in the way that they do. Research that answers “why” questions is referred to as explanatory research. In this case, the researcher is trying to identify the causes and effects of whatever phenomenon she is studying. An explanatory study of college students’ addictions to their electronic gadgets might aim to understand why students become addicted. Does it have anything to do with their family histories? With their other extracurricular hobbies and activities? With whom they spend their time? An explanatory study could answer these kinds of questions.

There are numerous examples of explanatory social scientific investigations. For example, in a recent study, Dominique Simons and Sandy Wurtele (2010) [2] sought to discover whether receiving corporal punishment from parents led children to turn to violence in solving their interpersonal conflicts with other children. In their study of 102 families with children between the ages of 3 and 7, the researchers found that experiencing frequent spanking did, in fact, result in children being more likely to accept aggressive problem-solving techniques. Another example of explanatory research can be seen in Robert Faris and Diane Felmlee’s (2011) [3] research study on the connections between popularity and bullying. From their study of 8th, 9th, and 10th graders in 19 North Carolina schools, they found that aggression increased as adolescents’ popularity increased. [4]

The choice between descriptive, exploratory, and explanatory research should be made with your research question in mind. What does your question ask? Are you trying to learn the basics about a new area, establish a clear “why” relationship, or define or describe an activity or concept? In the next section, we will explore how each type of research is associated with different methods, paradigms, and forms of logic.

Key Takeaways

• Exploratory research is usually conducted when a researcher has just begun an investigation and wishes to understand the topic generally.
• Descriptive research is research that aims to describe or define the topic at hand.
• Explanatory research is research that aims to explain why particular phenomena work in the way that they do.
• Descriptive research- research that describes or define a particular phenomenon
• Explanatory research- explains why particular phenomena work in the way that they do, answers “why” questions
• Exploratory research- conducted during the early stages of a project, usually when a researcher wants to test the feasibility of conducting a more extensive study

Image attributions

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Two men and one woman in a photo by Rawpixel.com CC-0

• Lisk, J. (2011). Addiction to our electronic gadgets. Retrieved from: https://www.youtube.com/watch?v=9lVHZZG5qvw ↵
• Simons, D. A., & Wurtele, S. K. (2010). Relationships between parents’ use of corporal punishment and their children’s endorsement of spanking and hitting other children. Child Abuse & Neglect, 34 , 639–646. ↵
• Faris, R., & Felmlee, D. (2011). Status struggles: Network centrality and gender segregation in same- and cross-gender aggression. American Sociological Review, 76 , 48–73. The study has also been covered by several media outlets: Pappas, S. (2011). Popularity increases aggression in kids, study finds. Retrieved from: http://www.livescience.com/11737-popularity-increases-aggression-kids-study-finds.html ↵
• This pattern was found until adolescents reached the top 2% in the popularity ranks. After that, aggression declines. ↵

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Types of Research – Methods Explained with Examples

In the ever-evolving world of academia and professional inquiry, understanding the various types of research is crucial for anyone looking to delve into a new study or project. Research, a systematic investigation aimed at discovering and interpreting facts, plays a pivotal role in expanding our knowledge across various fields.

From qualitative research , known for its in-depth analysis of non-numerical data, to quantitative research , which focuses on numerical data and statistical approaches, the spectrum of research types is broad and diverse. We also explore descriptive research , which aims to accurately and systematically describe a population, situation, or phenomenon, and analytical research, which goes a step further to understand the ‘why’ and ‘how’ of a subject.

What is Research?

Research is the process of studying a subject in detail to discover new information or understand it better. This can be anything from studying plants or animals, to learning how people think and behave, to finding new ways to cure diseases. People do research by asking questions, collecting information, and then looking at that information to find answers or learn new things.

Types of Researches Glance

This table provides a quick reference to understand the key aspects of each research type.

Types of Researches Methodology

1. qualitative.

Qualitative research is a methodological approach primarily used in fields like social sciences, anthropology, and psychology. It’s aimed at understanding human behavior and the motivations behind it. Unlike quantitative research that focuses on numbers and statistics, qualitative research delves into the nature of phenomena through detailed, in-depth exploration. Here’s a more detailed explanation:

Definition and Approach: Qualitative research focuses on understanding human behavior and the reasons that govern such behavior. It involves in-depth analysis of non-numerical data like texts, videos, or audio recordings.

Key Features:

• Emphasis on exploring complex phenomena
• Involves interviews, focus groups , and observations
• Generates rich, detailed data that are often subjective

Applications: Widely used in social sciences, marketing, and user experience research.

2. Quantitative Research

Quantitative research is a systematic approach used in various scientific fields to quantify data and generalize findings from a sample to a larger population. This type of research is fundamentally different from qualitative research in several ways:

Definition and Approach: Quantitative research is centered around quantifying data and generalizing results from a sample to the population of interest. It involves statistical analysis and numerical data .

• Relies on structured data collection instruments
• Large sample sizes for generalizability
• Statistical methods to establish relationships between variables

Applications: Common in natural sciences, economics, and market research.

3. Descriptive Research

Definition and Approach: This research type aims to accurately describe characteristics of a particular phenomenon or population.

• Provides detailed insights without explaining why or how something happens
• Involves surveys and observations
• Often used as a preliminary research method

Applications: Used in demographic studies, census, and organizational reporting.

4. Analytical Research

Definition and Approach: Analytical research goes beyond description to understand the underlying reasons or causes.

• Involves comparing data and facts to make evaluations
• Critical thinking is a key component
• Often hypothesis-driven

Applications: Useful in scientific research, policy analysis, and business strategy.

5. Applied Research

Definition and Approach: Applied research focuses on finding solutions to practical problems.

• Direct practical application
• Often collaborative , involving stakeholders
• Results are immediately applicable

Applications: Used in healthcare, engineering, and technology development.

6. Fundamental Research

Definition and Approach: Also known as basic or pure research, it aims to expand knowledge without a direct application in mind.

• Theoretical framework
• Focus on understanding fundamental principles
• Long-term in nature

Applications: Foundational in fields like physics, mathematics, and social sciences.

7. Exploratory Research

Definition and Approach: This type of research is conducted for a problem that has not been clearly defined.

• Flexible and unstructured
• Used to identify potential hypotheses
• Relies on secondary research like reviewing available literature

Applications: Often the first step in social science research and product development.

8. Conclusive Research

Definition and Approach: Conclusive research is designed to provide information that is useful in decision-making.

• Structured and methodical
• Aims to test hypotheses
• Involves experiments, surveys, and testing

Applications: Used in market research, clinical trials, and policy evaluations.

Difference between Qualitative And Quantitative Research

Here is detailed difference between the qualitative and quantitative research –

Understanding the different types of research is crucial for anyone embarking on a research project. Each type has its unique approach, methodology, and application area, making it essential to choose the right type for your specific research question or problem. This guide serves as a starting point for researchers to explore and select the most suitable research method for their needs, ensuring effective and reliable outcomes.

Types of Research – FAQs

What are the 4 main types of research.

There are four main types of Quantitative research:  Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research . attempts to establish cause- effect relationships among the variables. These types of design are very similar to true experiments, but with some key differences.

What are the types of research PDF?

APPLIED RESEARCH BASIC RESEARCH CORRELATIONAL RESEARCH DESCRIPTIVE RESEARCH ETHNOGRAPHIC RESEARCH EXPERIMENTAL RESEARCH. EXPLORATORY RESEARCH

What are the 5 main purpose of research?

The primary purposes of basic research (as opposed to applied research) are  documentation, discovery, interpretation, and the research and development (R&D) of methods and systems for the advancement of human knowledge .

Can I be sure that my assignment paper will be plagiarism-free?

You can be 100% sure about the content of your assignment when done by a professional and genuine assignment maker. Most companies focus on providing the best and unique content so that they can attract more returning customers.

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1.7 types of research.

Research is a formal, evidence-based approach to learning or creating new information. Broadly speaking, the two main types of research are qualitative and quantitative research.

These two types of research are different and complementary (Table 1.1 , which is a gross generalisation !).

Both methods have advantages and disadvantages, and both can be, and often are, used together: using a combination of qualitative and quantitative research is called mixed methods research.

The decision to use qualitative, quantitative or mixed methods approaches should depend on the research problem, not the skills of the researchers.

Broadly, qualitative research leads to a deeper understanding of what is being studied, usually about a very narrowly-defined group. Meanings, motivations, opinions or themes often emerge from qualitative research.

Broadly, quantitative research summarises and analyses data using numerical methods, such as averages and percentages. Typically, information from a subset of the population (a sample ) is used to infer information about a larger group (a population ) in quantitative research.

... quantitative data gets you the numbers to... [support] the broad general points of your research. Qualitative data brings you the details and the depth to understand their full implications. --- SurveyMonkey website, October 2019.

Approaches to qualitative research ( Robson 2002 ) include grounded theory, action research, and ethnography. Specific examples of quantitative research approaches include observational studies and experimental studies.

Example 1.1 (Types of research) Suppose we wish to learn about why people do or do not buy electric cars (for example, see Egbue and Long ( 2012 ) ).

A qualitative research study might:

• Interview a small group of people who have bought electric cars,
• Interview another small group of people who have bought non-electric cars.

The researchers ask about their reasons for their car purchase.

A quantitative research study might survey a large number of buyers of electric and non-electric cars, and ask the buyers' age, sex, and type of car purchased.

The survey may include questions such as 'Which of the following is your biggest concern about buying an electric car?' and then list five reasons from which the respondents can select.

The survey responses could be analysed by numerically summarising the ages and sex of car buyers, looking for relationships between age and whether an electric car was purchased, and reporting the percentage of respondents who select each of the five options of concerns about buying electric cars.

A mixed methods study may initially use a qualitative approach using small groups (as described above). From this study, any common themes that emerge could be used to create a survey, with these themes as options that respondents can choose between.

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Types of Research Papers: Overview

A research paper is simply a piece of writing that uses outside sources. There are different types of research papers with varying purposes and expectations for sourcing.

While this guide explains those differences broadly, ask your professor about specific disciplinary conventions.

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This paper is in the following e-collection/theme issue:

Published on 10.4.2024 in Vol 26 (2024)

Effectiveness of a Web-Based Individual Coping and Alcohol Intervention Program for Children of Parents With Alcohol Use Problems: Randomized Controlled Trial

Authors of this article:

Original Paper

• Håkan Wall 1 , PhD   ; 
• Helena Hansson 2 , PhD   ; 
• Ulla Zetterlind 3 , PhD   ; 
• Pia Kvillemo 1 , PhD   ; 
• Tobias H Elgán 1 , PhD  

1 Stockholm Prevents Alcohol and Drug Problems, Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm, Sweden

2 School of Social Work, Faculty of Social Sciences, Lund University, Lund, Sweden

3 Clinical Health Promotion Centre, Department of Health Sciences, Lund University, Lund, Sweden

Corresponding Author:

Tobias H Elgán, PhD

Stockholm Prevents Alcohol and Drug Problems, Centre for Psychiatry Research

Department of Clinical Neuroscience

Karolinska Institutet, & Stockholm Health Care Services

Norra Stationsgatan 69

Stockholm, 11364

Phone: 46 700011003

Email: [email protected]

Background: Children whose parents have alcohol use problems are at an increased risk of several negative consequences, such as poor school performance, an earlier onset of substance use, and poor mental health. Many would benefit from support programs, but the figures reveal that only a small proportion is reached by existing support. Digital interventions can provide readily accessible support and potentially reach a large number of children. Research on digital interventions aimed at this target group is scarce. We have developed a novel digital therapist-assisted self-management intervention targeting adolescents whose parents had alcohol use problems. This program aims to strengthen coping behaviors, improve mental health, and decrease alcohol consumption in adolescents.

Objective: This study aims to examine the effectiveness of a novel web-based therapist-assisted self-management intervention for adolescents whose parents have alcohol use problems.

Methods: Participants were recruited on the internet from social media and websites containing health-related information about adolescents. Possible participants were screened using the short version of the Children of Alcoholics Screening Test-6. Eligible participants were randomly allocated to either the intervention group (n=101) or the waitlist control group (n=103), and they were unblinded to the condition. The assessments, all self-assessed, consisted of a baseline and 2 follow-ups after 2 and 6 months. The primary outcome was the Coping With Parents Abuse Questionnaire (CPAQ), and secondary outcomes were the Center for Epidemiological Studies Depression Scale, Alcohol Use Disorders Identification Test (AUDIT-C), and Ladder of Life (LoL).

Results: For the primary outcome, CPAQ, a small but inconclusive treatment effect was observed (Cohen d =–0.05 at both follow-up time points). The intervention group scored 38% and 46% lower than the control group on the continuous part of the AUDIT-C at the 2- and 6-month follow-up, respectively. All other between-group comparisons were inconclusive at either follow-up time point. Adherence was low, as only 24% (24/101) of the participants in the intervention group completed the intervention.

Conclusions: The findings were inconclusive for the primary outcome but demonstrate that a digital therapist-assisted self-management intervention may contribute to a reduction in alcohol consumption. These results highlight the potential for digital interventions to reach a vulnerable, hard-to-reach group of adolescents but underscore the need to develop more engaging support interventions to increase adherence.

Trial Registration: ISRCTN Registry ISRCTN41545712; https://www.isrctn.com/ISRCTN41545712?q=ISRCTN41545712

International Registered Report Identifier (IRRID): RR2-10.1186/1471-2458-12-35

Introduction

Children who grow up with parents who have substance use problems or disorders face extraordinary challenges. Approximately 20% of all children have parents with alcohol problems [ 1 - 5 ], while approximately 5% have parents with alcohol use disorders [ 4 , 6 , 7 ]. Children growing up with parental substance abuse are at an increased risk of several negative outcomes, such as psychiatric morbidity [ 8 - 12 ]; poor intellectual, cognitive, and academic achievement [ 13 - 15 ]; domestic physical abuse [ 16 ]; and early drinking onset and the development of substance use problems [ 9 , 17 , 18 ]. Thus, children exposed to parental substance abuse comprise a target group for selective interventions and prevention strategies [ 19 - 22 ].

In Sweden, municipalities account for most of the support offered to these children. An annual survey by the junior association of the Swedish branch of Movendi International (ie, an international temperance movement) reported that 97% of all municipalities provided support resources [ 23 ]. However, estimates from the same survey showed that approximately 2% of the children in the target group received support. Hence, an overwhelming majority never receives support, mainly because of difficulties in identifying and attracting them to intervention programs [ 22 , 24 ].

The internet has become an appealing way to reach and support a large number of people [ 25 , 26 ]. Web-based interventions seem particularly attractive to adolescents, as they generally use digital technology and social media. Furthermore, research has shown that adolescents regard the internet as inviting because it is a readily accessible, anonymous way of seeking help [ 27 ]. Web-based interventions can reduce the stigma associated with face-to-face consultations in health care settings [ 28 ], and young people appreciate the flexibility of completing web-based sessions to fit their own schedules [ 29 ]. The positive effects of web-based interventions have been detected across a broad range of conditions. A recent review by Hedman-Lagerlöf et al [ 30 ] concluded that therapist-supported internet-based cognitive behavioral therapy for adults yielded similar effects as face-to-face therapy. To date, most web-based interventions have been designed for adults. Although the number of web-based interventions targeting children or adolescents is increasing [ 25 , 31 - 33 ], the number of digital interventions aimed at children of substance-abusing parents is still scarce [ 22 , 34 - 38 ]. Those described in the literature, however, all have in common that they are quite extensive, with a duration over several weeks, and a brief digital intervention could complement these more extended interventions. For instance, our research group initiated a study on a web-based group chat for 15- to 25-year-old individuals who have parents with mental illness or substance use problems [ 35 ]. The duration of the program is 8 weeks, and it is a translated version of a program from the Netherlands [ 34 ], which has been shown to have inconclusive treatment effects [ 39 ]. In Sweden, 2 other programs with inconclusive treatment effects have been tested that target significant others and their children [ 37 , 38 ]. Finally, a digital intervention developed in Australia for 18- to 25-year-old individuals with parents with mental illness or substance use disorder [ 36 ] was tested in a pilot study demonstrating positive findings [ 40 ].

To meet the need for a brief, web-based intervention that targets adolescents having parents with alcohol problems and build on the evidence base of digital interventions targeting this vulnerable group, we developed a novel internet-delivered therapist-assisted self-management intervention called “Alcohol and Coping.” Our program originated from a manual-based face-to-face intervention called the “Individual Coping and Alcohol Intervention Program” (ICAIP) [ 41 , 42 ]. Previous studies on both the ICAIP, which aimed at college students having parents with alcohol problems, and a coping skills intervention program, which aimed at spouses of partners with alcohol dependency [ 43 ], have demonstrated positive effects regarding decreased alcohol consumption and improved mental health and coping behaviors [ 41 - 44 ]. Furthermore, the results from these studies underscore the importance of improving coping skills [ 42 , 44 ]. Among college students, those who received a combination of coping skills and an alcohol intervention program had better long-term outcomes [ 42 ].

The aim of this study was to test the effectiveness of Alcohol and Coping among a sample of adolescents aged 15-19 years with at least 1 parent with alcohol use problems. We hypothesized that the intervention group would be superior to the control group in improving coping skills. Secondary research questions concerned the participants’ improvement in (1) depression, (2) alcohol consumption, and (3) quality of life.

This study was a parallel-group randomized controlled trial in which participants were randomized to either the intervention or waitlist control group in a 1:1 allocation ratio. The trial design is illustrated in Figure 1 .

Recruitment and Screening

The participants were recruited from August 2012 to December 2013 through advertisements on social media (Facebook). The advertisements targeted individuals aged 15-19 years with Facebook accounts. Participants were recruited on the internet through advertisements on websites containing health-related information about adolescents. The advertisements included the text, “Do your parents drink too much? Participate in a study.” The advertisement contained an invitation to perform a web-based, self-assessed screening procedure. In addition to questions about age and sex, participants were screened for having parents with alcohol problems using the short version of the Children of Alcoholics Screening Test-6 (CAST-6), developed from a 30-item original version [ 45 ]. The CAST-6 is a 6-item true-false measure designed to assess whether participants perceive their parents’ alcohol consumption to be problematic. The CAST-6 has demonstrated high internal consistency ( r =0.92-0.94), test-retest reliability ( r =0.94), and high validity as compared to the 30-item version ( r =0.93) using the recommended threshold score of 3 or higher [ 45 , 46 ]. We previously translated the CAST-6 into Swedish and validated the translated version among 1450 adolescents, showing good internal consistency (α=.88), excellent test-retest reliability (intraclass correlation coefficient=0.93), and loading into 1 latent factor [ 47 ]. Additional inclusion criteria included having access to a computer and the internet and being sufficiently fluent in Swedish. Participants were excluded from the study and were referred to appropriate care if there were indications of either suicidal or self-inflicted harmful behaviors. Individuals eligible for inclusion received further information about the study and were asked to provide consent to participate by providing an email address.

Data Collection and Measures

All assessments were administered through email invitations containing a hyperlink to the web-based self-reported assessments. Up to 3 reminders were sent through email at 5, 10, and 15 days after the first invitation. A baseline assessment (t 0 ) was collected before randomization, and follow-up assessments were conducted at 2 and 6 months (t 1 and t 2 , respectively) after the initial assessment.

Participants were asked for age, sex, whether they lived with a parent (mother and father, mother or father, mother or father and stepparent, or alternate between mother and father), where their parents were born (Sweden or a Nordic country excluding Sweden or outside of the Nordic countries), parental status (employed, student, on parental leave, or unemployed), and any previous or present participation in support activities for children having parents with alcohol use problems. The primary outcome was coping, measured using the Coping With Parents Abuse Questionnaire (CPAQ) based on the Coping Behavior Scale developed by Orford et al [ 48 ]. Secondary outcomes were the Center for Epidemiological Studies Depression Scale (CES-DC) [ 49 ], the 3-question Alcohol Use Disorders Identification Test (AUDIT-C) [ 50 ], and the Ladder of Life (LoL), which measures the overall quality of life by asking about the participants’ past, present, and future ratings of their overall life satisfaction [ 50 ]. CPAQ has been shown to be reliable [ 41 , 42 ]. For this study, this scale was factor-analyzed to reduce the number of questions from 37 to 20. The resulting scale measures 6 coping typologies (discord, emotion, control, relationship, avoidance, and taking specific action) using a 4-point Likert scale, with a threshold score above 50 points (out of 80) indicating dysfunctional coping behavior. The CES-DC measures depressive symptoms during the past week using a 4-point Likert scale, where a higher total score indicates more depressive symptoms [ 49 ]. A cutoff score of ≥16 indicates symptoms of moderate depression, while a score of ≥30 indicates symptoms of severe depression [ 51 , 52 ]. The scale measures 4 dimensions of depression: depressed mood, tiredness, inability to concentrate, and feelings of being outside and lonely, and has positively stated items [ 52 ]. Additionally, this scale is a general measure of childhood psychopathology [ 53 ] and has been demonstrated to be reliable and valid among Swedish adolescents [ 52 ]. Alcohol consumption was measured using a modified AUDIT-C, which assesses the frequency of drinking, quantity consumed on a typical occasion, and frequency of heavy episodic drinking (ie, binge drinking) [ 50 ] using a 30-day perspective (as opposed to the original 12-month perspective). These questions have previously been translated into Swedish [ 54 ], and a score of ≥4 and ≥5 points for women and men, respectively, was used as a cutoff for risky drinking. This scale has been demonstrated to be reliable and valid for Swedish adolescents [ 55 ]. Furthermore, 2 questions were added concerning whether the participants had ever consumed alcohol to the point of intoxication and their age at the onset of drinking and intoxication. The original version of the LoL was designed for adults and asked the respondents to reflect on their, present, and future life status from a 5-year perspective on a 10-point Visual Analogue Scale representing life status from “worst” to “best” possible life imaginable [ 56 ]. A modified version for children, using a time frame of 1 year, has been used previously in Sweden [ 57 ] and was used in this study.

Randomization

After completing the baseline assessment, each participant was allocated to either the intervention or the control group. An external researcher generated an unrestricted random allocation sequence using random allocation software [ 58 ]. Neither the participants nor the researchers involved in the study were blinded to group allocation.

Based on the order in which participants were included in the study, they were allocated to 1 of the 2 study groups and informed of their allocation by email. Additionally, those who were randomized to the intervention group received a hyperlink to the Alcohol and Coping program, whereas the control group participants received information that they would gain access to Alcohol and Coping after the last follow-up assessment (ie, the waitlist control group). All participants were informed about other information and support available through web pages, notably drugsmart [ 59 ], which contains general information and facts about alcohol and drugs, in addition to more specific information about having substance-abusing parents. Telephone numbers and contact information for other organizations and primary health care facilities were also provided.

The Intervention

As noted previously, Alcohol and Coping is derived from the aforementioned manual-based face-to-face ICAIP intervention program [ 41 , 42 ]. The ICAIP consists of a combination of an alcohol intervention program, which is based on the short version of the Brief Alcohol Screening and Intervention for College Students program [ 60 ], and a coping intervention program developed for the purpose of the ICAIP [ 41 , 42 ]. Like the original ICAIP intervention, Alcohol and Coping builds on psychoeducational principles and includes components such as film-based lectures, various exercises, and both automated and therapist-assisted feedback. Briefly, once the participants logged into the Alcohol and Coping platform, they were introduced to the program, which followed the pattern of a board game ( Figure 2 ). Following the introduction, participants took part in 3 film-based lectures (between 8 and 15 minutes each, Figure 3 ) concerning alcohol problems within the family. The respective lectures included information about (1) dependency in general as well as the genetic and environmental risks for developing dependency, (2) family patterns and how the family adapts to the one having alcohol problems, and (3) attitudes toward alcohol and how they influence drinking and the physiological effects of alcohol. After completing the lectures, the participants were asked to answer 2 questions about their own alcohol consumption (ie, how often they drink and how often they drink to intoxication), followed by an automatic feedback message that depended on their answers. It was then suggested that the participants log out of the intervention for a 1- to 2-day break. The reason for this break was to give the participants a chance to digest all information and impressions. When they logged back into the intervention, they were asked to answer 20 questions about their coping strategies, which were also followed by automatic feedback. This feedback comprised a library covering all the prewritten feedback messages, each of which was tailored to the participants’ specific answers. The participants then participated in a 5-minute–long film-based lecture on emotion and problem-focused coping in relation to family alcohol problems ( Figure 3 ). This was followed by 4 exercises where the participants read through vignette-like stories from 4 fictional persons describing their everyday lives related to coping and alcohol problems in the family. The stories are presented by film-based introductions that are each 1-2 minutes long. Participants were then requested to respond to each story by describing how the fictive person could have coped with their situation. As a final exercise, participants were asked to reflect on their own family situation and how they cope with situations. The participants then had to take a break for a few days.

During the break, a therapist composed individual feedback that covered reflections and confirmation of the participant’s exercises and answers to questions and included suggestions on well-suited coping strategies. Additionally, the therapist encouraged the participants to talk to others in their surroundings, such as friends, teachers, or coaches, and seek further support elsewhere, such as from municipal social services, youth health care centers, or other organizations. Finally, the therapist reflected on the participants’ alcohol consumption patterns and reminded them of increased genetic and environmental risks. Those who revealed patterns of risky alcohol use were encouraged to look at 2 additional film-based lectures with more information about alcohol and intoxication (4 minutes) and alcohol use and dependency (5 minutes). Participants received this feedback once they logged back into the program, but they also had the opportunity to receive feedback through email. The total estimated effective time for completing the program was about 1 hour, but as described above, there was 1 required break when the individualized feedback was written. To keep track of the dose each participant received, each of the 15 components in the program ( Figure 1 ) is equal to completing 6.7% (1/15) of the program in total.

Sample Size

The trial was designed to detect a medium or large effect size corresponding to a standardized mean difference (Cohen d >0.5) [ 61 ]. An a priori calculation of the estimated sample size, using the software G*Power (G*Power Team) [ 62 ], revealed that a total of 128 participants (64 in each group) were required to enroll in the trial (power=0.80; α=.05; 2-tailed). However, to account for an estimated attrition rate of approximately 30% [ 34 ], it was necessary to enroll a minimum of 128/(1 – 0.3) = 183 participants in the trial. After a total of 204 individuals had been recruited and randomized into 2 study arms, recruitment was ended.

Statistical Analysis

Data were analyzed according to the intention-to-treat (ITT) principle, and all randomized participants were included, irrespective of whether they participated in the trial. The 4 research variables were depression (CES-DC), coping (CPAQ), alcohol use (AUDIT-C), and life status (LoL).

Data analysis consisted of comparing outcome measurements at t 1 and t 2 . The baseline measurement t 0 value was added as an adjustment variable in all models. The resulting data from CPAQ, CES-DC, and LoL were normally distributed and analyzed using linear mixed models. The resulting AUDIT-C scores were nonnormally distributed, with an excess of 0 values, and were analyzed using a 2-part model for longitudinal data. This model is sufficiently flexible to account for numerous 0 reports. This was achieved by combining a logistic generalized linear mixed model (GLMM) for the 0 parts and a skewed continuous GLMM for the non-0 alcohol consumption parts. R-package brms (Bayesian regression models using Stan; R Foundation for Statistical Computing) [ 63 ], a higher-level interface for the probabilistic programming language Stan [ 64 ], and a custom brms family for a marginalized 2-part lognormal distribution were used to fit the model [ 65 ]. The logistic part of the model represents the subject-specific effects on the odds of reporting no drinking. The continuous part was modeled using a gamma GLMM with a log link. The exponentiated treatment effect represents the subject-specific ratio of the total AUDIT-C scores between the treatment and waitlist control groups for those who reported drinking during the specific follow-up period.

Handling of Missing Data

GLMMs include all available data and provide unbiased ITT estimates under the assumption that data are missing at random, meaning that the missing data can be explained by existing data. However, it is impossible to determine whether the data are missing at random or whether the missing data are due to unobserved factors [ 66 ]. Therefore, we also assumed that data were not missing at random, and subsequent sensitivity analyses were performed [ 66 ]. We used the pattern mixture method, which assumes not missing at random, to compare those who completed the follow-up at 6 months (t 2 ) with those who did not (but completed the 2-month follow-up). The overall effect of this model is a combination of the effects of each subgroup. We also tested the robustness of the results by performing ANCOVAs at the 2-month follow-up, both using complete cases and with missing values imputed using multilevel multiple imputation.

The effect of the program was estimated using Cohen d , where a value of approximately 0.2 indicates a small effect size and values of approximately 0.5 and 0.8 indicate medium and large effect sizes, respectively [ 61 ].

Ethical Considerations

All procedures were performed in accordance with the ethical standards of the institutional or national research committees, the 1964 Helsinki Declaration and its later amendments, and comparable ethical standards. Informed consent was obtained from all the participants included in the study. This study was approved by the Swedish Ethical Review Authority (formerly the Regional Ethical Review Board in Stockholm, No. 2011/1648-31/5).

To enhance the response rates, participants received a cinema gift certificate corresponding to approximately EUR 11 (US $12) as compensation for completing each assessment. If a participant completed all assessments, an additional gift certificate was provided. The participants could subsequently receive 4 cinema gift certificates totaling EUR 44 (US $48).

The trial profile is depicted in Figure 1 and reveals that 2722 individuals who were aged between 15 and 19 years performed the screening procedure. A total of 1448 individuals did not fulfill the inclusion criteria and were excluded, leaving 1274 eligible participants. Another 1070 individuals were excluded because they did not provide informed consent or complete the baseline assessment, leaving 204 participants who were allocated to 1 of the 2 study groups. A total of 140 (69%) and 131 (64%) participants completed t 1 and t 2 assessments, respectively. Of the participants in the intervention group (n=101), 63% (n=64) registered an account on the Alcohol and Coping website, 35% (n=35) completed the alcohol intervention section, and 24% (n=24) completed both the alcohol and coping intervention sections.

Sample Characteristics

The mean age of the sample was 17.0 (SD 1.23) years, and the vast majority were female, with both parents born in Sweden and currently working ( Table 1 ). Approximately one-third of the participants reported living with both parents. The mean score on the CAST-6 was 5.33 (SD 0.87) out of a total of 6, and the majority of the sample (147/204, 72.1%) perceived their father to have alcohol problems. Approximately 12% (25/204) had never consumed alcohol, whereas approximately 70% (144/204) had consumed alcohol at a level of intoxication. The mean age at onset was 13.7 (SD 2.07) years and the age at first intoxication was 14.8 (SD 1.56) years. The proportion of participants with symptoms of at least moderate depression was 77.5% (158/204), of whom 55.1% (87/158) had symptoms of severe depression and 42.6% (87/204) had symptoms of dysfunctional coping behaviors. The percentage of participants who consumed alcohol at a risky level was 39.7% (81/204). Table 1 provides complete information regarding the study sample.

a Significance levels calculated by Pearson chi-square statistics for categorical variables and 2-tailed t tests for continuous variables.

Treatment Effects

For the primary outcome, coping behavior (CPAQ), we found a small but inconclusive treatment effect in favor of treatment at both 2 (t 1 ) and 6 (t 2 ) months (Cohen d =–0.05 at both t 1 and t 2 ). For the secondary outcome, alcohol use (AUDIT-C), we found a treatment effect in that the intervention group scored 38% less than the control group on the continuous part (ie, drinking when it occurred) at t 1 and 46% less at t 2 . Regarding depression (CES-DC) and life status (LoL), all between-group comparisons of treatment effects were inconclusive at both follow-up time points ( Table 2 ).

a CPAQ: Coping With Parents Abuse Questionnaire.

b CES-DC: Center for Epidemiological Studies Depression Scale.

c LoL: Ladder of Life.

d AUDIT-C: Alcohol Use Disorders Identification Test.

e N/A: not applicable.

Missing Data

In contrast to the ITT analyses, the sensitivity analyses showed that the treatment group, averaged over the levels of dropout, scored higher (ie, a negative effect) on the main outcome, coping behavior (CPAQ), at t 1 (2.44; P =.20). However, the results remain inconclusive.

Dose-Response Effects

We did not find any evidence for greater involvement in the program being linked to improved outcomes with regard to coping behavior.

We did not find any support for the primary hypothesis: the intervention was not superior to the control condition with regard to coping behavior. Inconclusive results with small effect sizes were observed at both follow-up time points. However, for the secondary outcomes, we found that those in the intervention group who drank alcohol drank approximately 40%-50% less than those in the control group at both follow-ups. These results corroborate previous findings on the precursor face-to-face ICAIP intervention program, demonstrating that participants who received a combined alcohol and coping intervention reported superior outcomes with regard to alcohol-related outcomes compared to participants in the other 2 study arms, who received only a coping or alcohol intervention [ 41 , 42 ]. In contrast to this study, Hansson et al [ 42 ] found that all groups improved their coping skills, although the between-group comparisons were inconclusive and the improvements were maintained over time. These differences could be explained by the different settings in which the precursor program was provided (ie, face-to-face to young adults in a university setting), whereas this study targeted young people (15-19 years of age) through a web-based digital intervention. Additionally, the poor adherence in this study may explain the absence of primary results favoring the intervention group. In a recent study, parents without alcohol problems were recruited to participate in a randomized trial evaluating the web-based SPARE (Supportive Parenting and Reinforcement) program to improve children’s mental health and reduce coparents’ alcohol use. In line with our study, the authors did not find the primary outcome of the SPARE program to be superior to that of the active control group (which received written psychoeducation); however, both groups reported decreased coparental alcohol consumption [ 38 ].

Considering that approximately 3600 children in 2022 participated in various forms of support provided by Swedish municipalities [ 23 ], our recruitment activities reached a large number of eligible individuals, pointing to the potential of finding these children on these platforms. There were unexpectedly high levels of depression among the participants in this study. Although the intervention did not target depressive symptoms per se , there was a trend for the intervention group to have decreased depression levels compared to the control group. A large proportion of participants had symptoms of severe depression, which may have aggravated their capacity for improvement at follow-up [ 28 , 67 ]. Targeting dysfunctional coping patterns could affect an individual’s perceived mental health, and studies have shown that healthy coping strategies positively affect depression and anxiety in a positive way [ 68 ]. Using dysfunctional coping strategies, such as negative self-talk and alcohol consumption, can lead to depressive symptoms [ 69 ]. Targeting these symptoms in the context of healthy and unhealthy coping strategies may be a viable route to fostering appropriate coping strategies that work in the long run. Given that the young people who were reached by the intervention in this study displayed high levels of depression, future interventions for this group should include programs targeting depressive symptoms.

Almost 37% (37/101) of the intervention group did not log into the intervention at all, and only 24% (24/101) of the intervention group participants completed all parts of the program. The fact that a high proportion of the participants had symptoms of severe depression could explain the low adherence. Another reason could be that the initial film-based lectures were too long to maintain the participants’ attention, as the lectures ranged from 8-15 minutes. Yet a final reason could be that we had a 1- to 2-day break built into the intervention, and for unknown reasons, some participants did not log back into the intervention. However, we did not find a dose-response relationship indicating favorable outcomes for those who completed more of the program content. High levels of attrition are not uncommon in self-directed programs such as the one in this study; for example, in a study on a smoking cessation intervention, 37% of the participants never logged into the platform [ 70 ], and in a self-directed intervention for problem gamblers, a majority dropped out after 1 week and none completed the entire program [ 71 ]. Increased intervention adherence is a priority when developing new digital interventions, particularly for young people. One method is to use more persuasive technologies, such as primary tasks, dialogue, and social support [ 72 ]. Considering children whose parents have mental disorders, Grové and Reupert [ 73 ] suggested that digital interventions should include components such as providing information about parental mental illness, access to health care, genetic risk, and suggestions for how children might initiate conversations with parents who have the illness. These suggestions should be considered in future studies on interventions for youths whose parents have substance use problems. Representatives of the target group and other relevant stakeholders should also be involved in coproducing new interventions to increase the probability of developing more engaging programs [ 74 ]. Moreover, one cannot expect study participants to return to the program more than once, and for the sake of adherence, briefer interventions should not encourage participants to log-out for a break. To keep adherence at an acceptable level, similar future interventions for this target group should also consider having symptoms of severe depression as an exclusion criterion [ 28 , 67 ]. Further, to improve adherence, strategies of coproduction could be used where all stakeholders, including the target group, are involved in intervention development [ 75 ]. Other important factors identified to improve adherence to digital interventions are to make the content relatable, useful, and even more interactive [ 76 ]. Those participants who have symptoms of severe depression should be referred to other appropriate health care. Finally, it is probably beneficial to develop shorter psychoeducative film-based lectures than ours, lasting up to 15 minutes. Future self-directed digital interventions targeting this population should, therefore, focus on a very brief and focused intervention, which, based on theory, has the potential to foster healthy coping behaviors that can lead to an increased quality of life and improved mental health for this group of young people.

Another concern for future projects would be to use a data-driven approach during the program development phase, where A/B testing can be used to test different setups of the program to highlight which setup works best. Another aspect that must be considered is the fast-changing world of technology, where young people are exposed to an infinite number of different apps that grab their attention, which also calls for interventions to be short and to the point. Furthermore, if the program is to spread and become generally available, one must consider that keeping the program alive for a longer period will require funding and staffing for both product management and technical support.

Strengths and Limitations

This study had several strengths. First, Alcohol and Coping is a web-based intervention program, and it appears as if the internet is a particularly promising way to provide support to adolescents growing up with parents with alcohol problems because it offers an anonymous means of communicating and makes intervention programs readily accessible [ 25 ]. Our recruitment strategies reached a considerable number of interested and eligible individuals, demonstrating the potential for recruiting through social media and other web platforms. Additionally, this program is one of the first brief web-based interventions aimed at adolescents with parents with alcohol-related problems. We used the CAST-6, which has been validated among Swedish adolescents [ 47 ], to screen eligible participants. Another strength is that the intervention program involved personalized, tailored feedback in the form of prewritten automatic messages and therapist-written personalized feedback, both of which have proven to be important components of web-based interventions aimed at adolescents [ 77 , 78 ]. Finally, this study evaluated the effectiveness of the Alcohol and Coping program using a randomized controlled trial design, which is considered the strongest experimental design with regard to allocation bias.

This study had some limitations. First, the design with a passive waitlist control group and an active intervention group, both unblinded to study allocation, may have resulted in biased estimates of treatment effects. Intervention adherence was low, and most of the study participants had symptoms of depression, where 55% (87/158) had symptoms of severe depression. This may have contributed to the small and overall inconclusive effects on the primary outcomes of this study. Many digital interventions have problems with low adherence, and in a review by Välimäki et al [ 79 ], some studies reported adherence rates as low as 10%. A vast proportion of the study participants were women, making the findings difficult to generalize to men. However, another limitation concerns selection bias and external validity. We recruited study participants through social media and other relevant websites containing health-related information, including information about parents with alcohol-related problems. It is, therefore, possible that the study population can be classified as “information-seeking” adolescents, who may have different personality traits relative to other adolescents in the same home situation. Additionally, as an inclusion criterion was having ready access to computers and the internet, it is possible that participants belonging to a lower socioeconomic class were underrepresented in the study. It should also be noted that the data presented here were collected approximately 10 years ago. However, we believe our findings make an important contribution to the field since, like our intervention, many recent web-based interventions use strategies of psychoeducation, films, exercises, questions, and feedback. Further, the number of web-based interventions for this target group remains scarce in the literature, which underscores the need for future research. Finally, the study was powered to detect a medium effect size. However, given the small effect sizes detected in this study, it is plausible that too few participants were recruited to detect differences between the groups.

Implications for Practice

Although growing up with parents who have alcohol problems per se is not sufficient for developing psychosocial disorders, many children need support to manage their situation. Therefore, it is difficult to recruit children to support these groups. In Sweden, not even 2% of all children growing up with parental alcohol problems attend face-to-face support groups provided by municipalities.

Offering support through web-based intervention programs seems particularly attractive to adolescents whose parents have alcohol-related problems. To date, evidence for such programs is scarce, and there is an urgent need to develop and evaluate digital interventions targeting this group of adolescents. This study makes important contributions to this novel field of research. The results provide insight into effective strategies for delivering intervention programs to children of parents with substance abuse issues, highlighting the potential for digital interventions to reach a vulnerable, hard-to-reach group of adolescents. Our findings underscore the need to develop more engaging interventions in coproduction with the target group.

Conclusions

We found that a digital therapist-assisted self-management intervention for adolescents whose parents have alcohol use problems contributed to a reduction in the adolescents’ own alcohol consumption. This result highlights the potential for digital interventions to reach a large, vulnerable, and hard-to-reach group of adolescents with support efforts. Findings were inconclusive for all other outcomes, which may be attributable to low adherence. This points to the need for future research on developing more engaging digital interventions to increase adherence among adolescents.

Acknowledgments

This work was undertaken on behalf of the Swedish Council for Information on Alcohol and Other Drugs (CAN) and was supported by grants from the Swedish National Institute of Public Health and the Swedish Council for Working Life and Social Research.

Conflicts of Interest

HH and UZ developed the study interventions. However, the parties did not derive direct financial income from these interventions. HW, PK, and THE declare no conflicts of interest.

CONSORT-eHEALTH checklist (V 1.6.1).

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Abbreviations

Edited by YH Lin; submitted 24.08.23; peer-reviewed by X Zhang, C Asuzu, D Liu; comments to author 28.01.24; revised version received 08.02.24; accepted 27.02.24; published 10.04.24.

©Håkan Wall, Helena Hansson, Ulla Zetterlind, Pia Kvillemo, Tobias H Elgán. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Types and grades of womb cancer

The most common type of womb cancer is endometrial cancer. Endometrial means that the cancer starts in the lining of the womb. This lining is called the endometrium.

Most womb cancers start in specific cells in the womb lining and are adenocarcinomas.

The grade of a cancer tells you how much the cancer cells look like normal cells. Cancer cells are divided into 3 grades.

Endometrial cancer

Most womb cancers start in specific cells in the womb lining, the endometrium. There are several types of endometrial cancer. Three of the more common types are:

Endometrioid adenocarcinoma

Endometrioid is pronounced endo-mee-tree-oyd. Adeno means that the cancerous cells are in the glandular tissue of the womb lining. Carcinoma means the cancer has started in tissues lining or covering internal organs.

Most endometrial cancers are adenocarcinomas. There are several types of adenocarcinomas:

• not otherwise specified
• adenocarcinoma with squamous differentiation
• secretory adenocarcinoma
• ciliated carcinoma
• villoglandular

These cancers are often diagnosed at an early stage and so are usually treated successfully.

Serous carcinoma

This type is much less common than endometrioid cancers. It also develops from the lining of the womb. This is called a non endometrioid cancer. It is a type 2 endometrial cancer. 

Clear cell carcinoma

Clear cell carcinoma of the womb lining is very rare. These cells are also in the lining of the womb and get their name from looking clear under a microscope. 

This is also called a non endometrioid cancer and is a type 2 endometrial cancer.

Other types of endometrial cancers include:

undifferentiated carcinoma

mixed carcinoma

carcinosarcoma - has features of both endometrial cancer and sarcoma when looked at under a microscope. This is different form sarcoma of the womb

mesonephric-like carcinoma

gastrointestinal mucinous carcinoma

Type 1 and type 2 endometrial cancer

Doctors sometimes divide endometrial cancers into 2 types.

Type 1 cancers are the most common type. They are usually endometrioid adenocarcinomas, and are linked to excess oestrogen in the body. They are generally slow growing and less likely to spread.

Type 2 cancers are not linked to excess oestrogen. They are generally faster growing and more likely to spread. They include uterine serous carcinomas and clear cell carcinomas. 

Sarcoma of the womb (uterine sarcoma)

Sarcoma of the womb is different from carcinosarcoma. Carcinosarcoma is when there is a combination of sarcoma and carcinoma cells under the microscope. They are often type 2 cancers and more aggressive. Carcinosarcomas are treated as carcinomas and not sarcomas.

Sarcoma of the womb develop from cells in the muscle layer of the womb, rather than the lining. They are a type of soft tissue sarcoma and are much less common than endometrial cancer. 

Soft tissue sarcomas are cancers of the supporting tissues of the body. There are many subtypes and they can develop from any type of soft tissue such as muscle or blood vessels. They tend to spread in the bloodstream to other parts of the body especially the lungs. The treatment you need for this type of womb cancer is not the same as other types of womb cancer.

Leiomyosarcoma is the most common sarcoma of the womb. It is a cancer of the muscle wall.

Because sarcomas are different and need different treatments, we have separate information about them.

• Read about soft tissue sarcoma

Cancer of the neck of the womb (cervix)

Although the cervix is part of the womb, cervical cancer is very different from womb cancer. 

• Find out about cervical cancer

Grading of your cancer

Grading is a way of dividing cancer cells into groups depending on how much the cells look like normal cells. This gives your doctor an idea of how quickly or slowly the cancer might grow and whether it is likely to spread.

The cells look very like normal cells. They are also called low grade or well differentiated. They tend to be slow growing and are less likely to spread than higher grade cancer cells.

The cells look more abnormal and are more likely to spread. This grade is also called moderately differentiated or moderate grade.

The cells look very abnormal and not like normal cells. They tend to grow quickly and are more likely to spread. They are called poorly differentiated or high grade.

Risk groups for endometrial cancer

Type of womb cancer.

When your doctor takes a sample of cells, a biopsy, it is sent to the laboratory. They also test the cancer they remove during surgery. A specialist doctor does various tests on the cells. This can help diagnose cancer and show which type of womb cancer it is.

Gene changes

Some womb cancers have changes in genes (mutations) that make the cancer grow and divide. Your doctor may test the cancer for changes in these genes to further classify your cancer. It also helps them decide on the best treatment for your type of womb cancer. This is also called molecular classification and includes the following groups:

• POLE mutations

Hormone receptors

They will also look for hormone receptors. The number of hormone receptors is linked to the effectiveness of hormone therapy.

When you have surgery, they look at the cancer to find out: 

• how fast growing your cancer is, also called the grade
• how far it has grown into the womb's muscle layer or the cervix
• whether it has spread into any lymph or blood vessels, also called lymphovascular space invasion (LVSI)

This information might help to describe the risk of your womb cancer spreading or coming back after treatment. It will also help your doctor decide about the best treatment for you. The risk can be:

• intermediate
• high-intermediate
• advanced metastatic

More information

To decide about what treatment you need your doctor looks at your type, grade and stage of your cancer. 

• Find out about staging of womb cancer

Related links

Stages of womb cancer.

The stage of a cancer tells you how big it is and whether it has spread. This helps your doctor decide which treatment you need.

Treatment for womb cancer

Your treatment depends on several factors. These include what type of womb cancer you have, how big it is, whether it has spread (the stage) and the grade. It also depends on your general health.

Living with womb cancer

There is support available during and after treatment to help you cope. This includes support from your clinical nurse specialist, cancer charities, community services, and family and friends.

Getting diagnosed with womb cancer

You usually start by seeing your GP if you have symptoms that could be due to womb cancer. Your doctor will then decide whether to do tests or refer you to a specialist.

Symptoms of womb cancer

The most common symptom of womb cancer is abnormal bleeding from the vagina. This is especially in women who have stopped having periods (post menopausal women).

Womb cancer main page

The womb is the pear shaped muscular bag that holds a baby during pregnancy. Most womb cancers start in the lining of the womb. They are also called uterine or endometrial cancer.

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6 Common Leadership Styles — and How to Decide Which to Use When

• Rebecca Knight

Being a great leader means recognizing that different circumstances call for different approaches.

Research suggests that the most effective leaders adapt their style to different circumstances — be it a change in setting, a shift in organizational dynamics, or a turn in the business cycle. But what if you feel like you’re not equipped to take on a new and different leadership style — let alone more than one? In this article, the author outlines the six leadership styles Daniel Goleman first introduced in his 2000 HBR article, “Leadership That Gets Results,” and explains when to use each one. The good news is that personality is not destiny. Even if you’re naturally introverted or you tend to be driven by data and analysis rather than emotion, you can still learn how to adapt different leadership styles to organize, motivate, and direct your team.

Much has been written about common leadership styles and how to identify the right style for you, whether it’s transactional or transformational, bureaucratic or laissez-faire. But according to Daniel Goleman, a psychologist best known for his work on emotional intelligence, “Being a great leader means recognizing that different circumstances may call for different approaches.”

• RK Rebecca Knight is a journalist who writes about all things related to the changing nature of careers and the workplace. Her essays and reported stories have been featured in The Boston Globe, Business Insider, The New York Times, BBC, and The Christian Science Monitor. She was shortlisted as a Reuters Institute Fellow at Oxford University in 2023. Earlier in her career, she spent a decade as an editor and reporter at the Financial Times in New York, London, and Boston.

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QS World University Rankings rates MIT No. 1 in 11 subjects for 2024

QS World University Rankings has placed MIT in the No. 1 spot in 11 subject areas for 2024, the organization announced today. The Institute received a No. 1 ranking in the following QS subject areas: Chemical Engineering; Civil and Structural Engineering; Computer Science and Information Systems; Data Science and Artificial Intelligence; Electrical and Electronic Engineering; Linguistics; Materials Science; Mechanical, Aeronautical, and Manufacturing Engineering; Mathematics; Physics and Astronomy; and Statistics and Operational Research. MIT also placed second in five subject areas: Accounting and Finance; Architecture/Built Environment; Biological Sciences; Chemistry; and Economics and Econometrics. For 2024, universities were evaluated in 55 specific subjects and five broader subject areas. MIT was ranked No. 1 in the broader subject area of Engineering and Technology and No. 2 in Natural Sciences. Quacquarelli Symonds Limited subject rankings, published annually, are designed to help prospective students find the leading schools in their field of interest. Rankings are based on research quality and accomplishments, academic reputation, and graduate employment. MIT has been ranked as the No. 1 university in the world by QS World University Rankings for 12 straight years.

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Stanford Medicine study flags unexpected cells in lung as suspected source of severe COVID

A previously overlooked type of immune cell allows SARS-CoV-2 to proliferate, Stanford Medicine scientists have found. The discovery has important implications for preventing severe COVID-19.

April 10, 2024 - By Bruce Goldman

In an uninfected interstitial macrophage, the nucleus (purple) and outer cell membrane (blue) are intact. In an infected interstitial macrophage, the nucleus is shattered, copious newly made viral components (red) clump together, and the cell broadcasts inflammatory and scar-tissue-inducing chemical signals (yellow). Emily Moskal

The lung-cell type that’s most susceptible to infection by SARS-CoV-2, the virus that causes COVID-19, is not the one previously assumed to be most vulnerable. What’s more, the virus enters this susceptible cell via an unexpected route. The medical consequences may be significant.

Stanford Medicine investigators have implicated a type of immune cell known as an interstitial macrophage in the critical transition from a merely bothersome COVID-19 case to a potentially deadly one. Interstitial macrophages are situated deep in the lungs, ordinarily protecting that precious organby, among other things, engorging viruses, bacteria, fungi and dust particles that make their way down our airways. But it’s these very cells, the researchers have shown in a study published online April 10 in the  Journal of Experimental Medicine , that of all known types of cells composing lung tissue are most susceptible to infection by SARS-CoV-2.

SARS-CoV-2-infected interstitial macrophages, the scientists have learned, morph into virus producersand squirt out inflammatory and scar-tissue-inducing chemical signals, potentially paving the road to pneumonia and damaging the lungs to the point where the virus, along with those potent secreted substances, can break out of the lungs and wreak havoc throughout the body.

The surprising findings point to new approaches in preventing a SARS-CoV-2 infection from becoming a life-threatening disease. Indeed, they may explain why monoclonal antibodies meant to combat severe COVID didn’t work well, if at all — and when they did work, it was only when they were administered early in the course of infection, when the virus was infecting cells in the upper airways leading to the lungs but hadn’t yet ensconced itself in lung tissue.

The virus surprises

“We’ve overturned a number of false assumptions about how the virus actually replicates in the human lung,” said  Catherine Blish , MD, PhD, a professor of infectious diseases and of microbiology and immunology and the George E. and Lucy Becker Professor in Medicine and associate dean for basic and translational research.

Blish is the co-senior author of the study, along with  Mark Krasnow , MD, PhD, the Paul and Mildred Berg Professor of biochemistry and the Executive Director of the Vera Moulton Wall Center for pulmonary vascular disease.

“The critical step, we think, is when the virus infects interstitial macrophages, triggering a massive inflammatory reaction that can flood the lungs and spread infection and inflammation to other organs,” Krasnow said. Blocking that step, he said, could prove to be a major therapeutic advance. But there’s a plot twist: The virus has an unusual way of getting inside these cells — a route drug developers have not yet learned how to block effectively — necessitating a new focus on that alternative mechanism, he added.

Catherine Blish

In a  paper  published in  Nature  in early 2020, Krasnow and his colleagues including then-graduate student Kyle Travaglini, PhD — who is also one of the new study’s co-lead authors along with MD-PhD student  Timothy Wu  — described a technique they’d worked out for isolating fresh human lungs; dissociating the cells from one another; and characterizing them, one by one, on the basis of which genes within each cell were active and how much so. Using that technique, the Krasnow lab and collaborators were able to discern more than 50 distinct cell types, assembling an atlas of healthy lung cells.

“We’d just compiled this atlas when the COVID-19 pandemic hit,” Krasnow said. Soon afterward, he learned that Blish and  Arjun Rustagi , MD, PhD, instructor of infectious diseases and another lead co-author of the study, were building an ultra-safe facility where they could safely grow SARS-CoV-2 and infect cells with it.

A collaboration ensued. Krasnow and Blish and their associates obtained fresh healthy lung tissue excised from seven surgical patients and five deceased lung donors whose lungs were virus-free but for one reason or another not used in transplants. After infecting the lung tissue with SARS-CoV-2 and waiting one to three days for the infection to spread, they separated and typed the cells to generate an infected-lung-cell atlas, analogous to the one Krasnow’s team had created with healthy lung cells. They saw most of the cell types that Krasnow’s team had identified in healthy lung tissue.

Now the scientists could compare pristine versus SARS-CoV-2-infected lungs cells of the same cell type and see how they differed: They wanted to know which cells the virus infected, how easily SARS-CoV-2 replicated in infected cells, and which genes the infected cells cranked up or dialed down compared with their healthy counterparts’ activity levels. They were able to do this for each of the dozens of different cell types they’d identified in both healthy and infected lungs.

“It was a straightforward experiment, and the questions we were asking were obvious,” Krasnow said. “It was the  answers  we weren’t prepared for.”

It’s been assumed that the cells in the lungs that are most vulnerable to SARS-CoV-2 infection are those known as alveolar type 2 cells. That’s because the surfaces of these cells, along with those of numerous other cell types in the heart, gut and other organs, sport many copies of a molecule known as ACE2. SARS-CoV-2 has been shown to be able to grab onto ACE2 and manipulate it in a way that allows the virus to maneuver its way into cells.

Alveolar type 2 cells are somewhat vulnerable to SARS-CoV-2, the scientists found. But the cell types that were by far the most frequently infected turned out to be two varieties of a cell type called a macrophage.

Virus factories

The word “macrophage” comes from two Greek terms meaning, roughly, “big eater.” This name is not unearned. The air we inhale carries not only oxygen but, unfortunately, tiny airborne dirt particles, fungal spores, bacteria and viruses. A macrophage earns its keep by, among other things, gobbling up these foreign bodies.

Mark Krasnow

The airways leading to our lungs culminate in myriad alveoli, minuscule one-cell-thick air sacs, whichare abutted by abundant capillaries. This interface, called the interstitium, is where oxygen in the air we breathe enters the bloodstream and is then distributed to the rest of the body by the circulatory system.

The two kinds of SARS-CoV-2-susceptible lung-associated macrophages are positioned in two different places. So-called alveolar macrophages hang out in the air spaces within the alveoli. Once infected, these cells smolder, producing and dribbling out some viral progeny at a casual pace but more or less keeping a stiff upper lip and maintaining their normal function. This behavior may allow them to feed SARS-CoV-2’s progression by incubating and generating a steady supply of new viral particles that escape by stealth and penetrate the layer of cells enclosing the alveoli.

Interstitial macrophages, the other cell type revealed to be easily and profoundly infected by SARS-CoV-2, patrol the far side of the alveoli, where the rubber of oxygen meets the road of red blood cells. If an invading viral particle or other microbe manages to evade alveolar macrophages’ vigilance, infect and punch through the layer of cells enclosing the alveoli, jeopardizing not only the lungs but the rest of the body, interstitial macrophages are ready to jump in and protect the neighborhood.

At least, usually. But when an interstitial macrophage meets SARS-CoV-2, it’s a different story. Rather than get eaten by the omnivorous immune cell, the virus infects it.

And an infected interstitial macrophage doesn’t just smolder; it catches on fire. All hell breaks loose as the virus literally seizes the controls and takes over, hijacking a cell’s protein- and nucleic-acid-making machinery. In the course of producing massive numbers of copies of itself, SARS-CoV-2 destroys the boundaries separating the cell nucleus from the rest of the cell like a spatula shattering and scattering the yolk of a raw egg. The viral progeny exit the spent macrophage and move on to infect other cells.

But that’s not all. In contrast to alveolar macrophages, infected interstitial macrophages pump out substances that signal other immune cells elsewhere in the body to head for the lungs. In a patient, Krasnow suggested, this would trigger an inflammatory influx of such cells. As the lungs fill with cells and fluid that comes with them, oxygen exchange becomes impossible. The barrier maintaining alveolar integrity grows progressively damaged. Leakage of infected fluids from damaged alveoli propels viral progeny into the bloodstream, blasting the infection and inflammation to distant organs.

Yet other substances released by SARS-CoV-2-infected interstitial macrophages stimulate the production of fibrous material in connective tissue, resulting in scarring of the lungs. In a living patient, the replacement of oxygen-permeable cells with scar tissue would further render the lungs incapable of executing oxygen exchange.

“We can’t say that a lung cell sitting in a dish is going to get COVID,” Blish said. “But we suspect this may be the point where, in an actual patient, the infection transitions from manageable to severe.”

Another point of entry

Compounding this unexpected finding is the discovery that SARS-CoV-2 uses a different route to infect interstitial macrophages than the one it uses to infect the other types.

Unlike alveolar type 2 cells and alveolar macrophages, to which the virus gains access by clinging to ACE2 on their surfaces, SARS-CoV-2 breaks into interstitial macrophages using a different receptor these cells display. In the study, blocking SARS-CoV-2’s binding to ACE2 protected the former cells but failed to dent the latter cells’ susceptibility to SARS-CoV-2 infection.

“SARS-CoV-2 was not using ACE2 to get into interstitial macrophages,” Krasnow said. “It enters via another receptor called CD209.”

That would seem to explain why monoclonal antibodies developed specifically to block SARS-CoV-2/ACE2 interaction failed to mitigate or prevent severe COVID-19 cases.

It’s time to find a whole new set of drugs that can impede SARS-CoV-2/CD209 binding. Now, Krasnow said.

The study was funded by the National Institutes of Health (grants K08AI163369, T32AI007502 and T32DK007217), the Bill & Melinda Gates Foundation, Chan Zuckerberg Biohub, the Burroughs Wellcome Fund, Stanford Chem-H, the Stanford Innovative Medicine Accelerator, and the Howard Hughes Medical Institute.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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Why is looking at a solar eclipse dangerous without special glasses? Eye doctors explain.

By Sara Moniuszko

Edited By Allison Elyse Gualtieri

Updated on: April 8, 2024 / 8:54 AM EDT / CBS News

The solar eclipse will be visible for millions of Americans on April 8, 2024, making many excited to see it — but how you watch it matters, since it can be dangerous for your eyes. 

A  solar eclipse occurs when the moon passes between the sun and Earth, blocking the sun's light . When the moon blocks some of the sun, it's a partial solar eclipse, but when moon lines up with the sun, blocking all of its light, a total solar eclipse occurs,  NASA explains . Either way, you need eye protection when viewing.

"The solar eclipse will be beautiful, so I hope that everyone experiences it — but they need to experience it in the right way," said Dr. Jason P. Brinton, an ophthalmologist and medical director at Brinton Vision in St. Louis.

Here's what to know to stay safe.

Why is looking at a solar eclipse dangerous?

Looking at the sun — even when it's partially covered like during an eclipse — can cause eye damage.

There is no safe dose of solar ultraviolet rays or infrared radiation, said  Dr. Yehia Hashad , an ophthalmologist, retinal specialist and the chief medical officer at eye health company Bausch + Lomb.

"A very small dose could cause harm to some people," he said. "That's why we say the partial eclipse could also be damaging. And that's why we protect our eyes with the partial as well as with the full sun."

Some say that during a total eclipse, it's safe to view the brief period time when the moon completely blocks the sun without eye protection. But experts warn against it. 

"Totality of the eclipse lasts only about 1 to 3 minutes based on geographic location, and bright sunlight suddenly can appear as the moon continues to move," notes an eclipse viewing guide published in JAMA , adding, "even a few seconds of viewing the sun during an eclipse" can temporarily or permanently damage your vision. 

Do I need special glasses for eclipse viewing?

Yes.  Eclipse glasses are needed to protect your eyes if you want to look at the eclipse.

Regular sunglasses aren't protective enough for eclipse viewing — even if you stack more than one. 

"There's no amount of sunglasses that people can put on that will make up for the filtering that the ISO standard filters and the eclipse glasses provide," Brinton said.

You also shouldn't look at the eclipse through a camera lens, phone, binoculars or telescope, according to NASA, even while wearing eclipse glasses. The solar rays can burn through the lens and cause serious eye injury.

Eclipse glasses must comply with the  ISO 12312-2 international safety standard , according to NASA, and should have an "ISO" label printed on them to show they comply. The American Astronomical Society  has a list  of approved solar viewers.

Can't find these, or they're sold out near you? You can also  make homemade viewers ,   which allow you to observe the eclipse indirectly — just don't accidentally look at the sun while using one.

How to keep kids safe during the solar eclipse

Since this eclipse is expected to occur around the time of dismissal for many schools across the country, it may be tempting for students to view it without the proper safety precautions while getting to and from their buses. That's why some school districts are  canceling classes early so kids can enjoy the event safely with their families.

Dr. Avnish Deobhakta, vitreoretinal surgeon at New York Eye and Ear Infirmary at Mount Sinai, said parents should also be careful because it can be difficult for children to listen or keep solar eclipse glasses on. 

"You want to actually, in my opinion, kind of avoid them even looking at the eclipse, if possible," he said. "Never look directly at the sun, always wear the right eclipse sunglasses if you are going to look at the sun and make sure that those are coming from a reliable source."

Brinton recommends everyone starts their eclipse "viewing" early, by looking at professional photos and videos of an eclipse online or visiting a local planetarium. 

That way, you "have an idea of what to expect," he said. 

He also recommends the foundation  Prevent Blindness , which has resources for families about eclipse safety.

What happens if you look at a solar eclipse without eclipse glasses?

While your eyes likely won't hurt in the moment if you look at the eclipse without protection, due to lowered brightness and where damage occurs in the eye, beware: The rays can still cause damage .

The harm may not be apparent immediately. Sometimes trouble starts to appear one to a few days following the event. It could affect just one or both eyes.

And while some will regain normal visual function, sometimes the damage is permanent. 

"Often there will be some recovery of the vision in the first few months after it, but sometimes there is no recovery and sometimes there's a degree to which it is permanent," Brinton said. 

How long do you have to look at the eclipse to damage your eyes?

Any amount of time looking at the eclipse without protection is too long, experts say. 

"If someone briefly looks at the eclipse, if it's extremely brief, in some cases there won't be damage. But damage can happen even within a fraction of a second in some cases," Brinton said. He said he's had patients who have suffered from solar retinopathy, the official name for the condition.

Deobhakta treated a patient who watched the 2017 solar eclipse for 20 seconds without proper eye protection. She now has permanent damage in the shape of a crescent that interferes with her vision. 

"The crescent that is burned into the retina, the patient sees as black in her visual field," he said. "The visual deficit that she has will never go away."

How to know if you've damaged your eyes from looking at the eclipse

Signs and symptoms of eye damage following an eclipse viewing include headaches, blurred vision, dark spots, changes to how you see color, lines and shapes. 

Unfortunately, there isn't a treatment for solar retinopathy.

"Seeing an eye care professional to solidify the diagnosis and for education I think is reasonable," Brinton said, but added, "right now there is nothing that we do for this. Just wait and give it time and the body does tend to heal up a measure of it."

Sara Moniuszko is a health and lifestyle reporter at CBSNews.com. Previously, she wrote for USA Today, where she was selected to help launch the newspaper's wellness vertical. She now covers breaking and trending news for CBS News' HealthWatch.

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