descriptive research case study examples

18 Descriptive Research Examples

Descriptive research involves gathering data to provide a detailed account or depiction of a phenomenon without manipulating variables or conducting experiments.

A scholarly definition is:

“Descriptive research is defined as a research approach that describes the characteristics of the population, sample or phenomenon studied. This method focuses more on the “what” rather than the “why” of the research subject.” (Matanda, 2022, p. 63)

The key feature of descriptive research is that it merely describes phenomena and does not attempt to manipulate variables nor determine cause and effect .

To determine cause and effect , a researcher would need to use an alternate methodology, such as experimental research design .

Common approaches to descriptive research include:

• Cross-sectional research : A cross-sectional study gathers data on a population at a specific time to get descriptive data that could include categories (e.g. age or income brackets) to get a better understanding of the makeup of a population.
• Longitudinal research : Longitudinal studies return to a population to collect data at several different points in time, allowing for description of changes in categories over time. However, as it’s descriptive, it cannot infer cause and effect (Erickson, 2017).

Methods that could be used include:

• Surveys: For example, sending out a census survey to be completed at the exact same date and time by everyone in a population.
• Case Study : For example, an in-depth description of a specific person or group of people to gain in-depth qualitative information that can describe a phenomenon but cannot be generalized to other cases.
• Observational Method : For example, a researcher taking field notes in an ethnographic study. (Siedlecki, 2020)

Descriptive Research Examples

1. Understanding Autism Spectrum Disorder (Psychology): Researchers analyze various behavior patterns, cognitive skills, and social interaction abilities specific to children with Autism Spectrum Disorder to comprehensively describe the disorder’s symptom spectrum. This detailed description classifies it as descriptive research, rather than analytical or experimental, as it merely records what is observed without altering any variables or trying to establish causality.

2. Consumer Purchase Decision Process in E-commerce Marketplaces (Marketing): By documenting and describing all the factors that influence consumer decisions on online marketplaces, researchers don’t attempt to predict future behavior or establish causes—just describe observed behavior—making it descriptive research.

3. Impacts of Climate Change on Agricultural Practices (Environmental Studies): Descriptive research is seen as scientists outline how climate changes influence various agricultural practices by observing and then meticulously categorizing the impacts on crop variability, farming seasons, and pest infestations without manipulating any variables in real-time.

4. Work Environment and Employee Performance (Human Resources Management): A study of this nature, describing the correlation between various workplace elements and employee performance, falls under descriptive research as it merely narrates the observed patterns without altering any conditions or testing hypotheses.

5. Factors Influencing Student Performance (Education): Researchers describe various factors affecting students’ academic performance, such as studying techniques, parental involvement, and peer influence. The study is categorized as descriptive research because its principal aim is to depict facts as they stand without trying to infer causal relationships.

6. Technological Advances in Healthcare (Healthcare): This research describes and categorizes different technological advances (such as telemedicine, AI-enabled tools, digital collaboration) in healthcare without testing or modifying any parameters, making it an example of descriptive research.

7. Urbanization and Biodiversity Loss (Ecology): By describing the impact of rapid urban expansion on biodiversity loss, this study serves as a descriptive research example. It observes the ongoing situation without manipulating it, offering a comprehensive depiction of the existing scenario rather than investigating the cause-effect relationship.

8. Architectural Styles across Centuries (Art History): A study documenting and describing various architectural styles throughout centuries essentially represents descriptive research. It aims to narrate and categorize facts without exploring the underlying reasons or predicting future trends.

9. Media Usage Patterns among Teenagers (Sociology): When researchers document and describe the media consumption habits among teenagers, they are performing a descriptive research study. Their main intention is to observe and report the prevailing trends rather than establish causes or predict future behaviors.

10. Dietary Habits and Lifestyle Diseases (Nutrition Science): By describing the dietary patterns of different population groups and correlating them with the prevalence of lifestyle diseases, researchers perform descriptive research. They merely describe observed connections without altering any diet plans or lifestyles.

11. Shifts in Global Energy Consumption (Environmental Economics): When researchers describe the global patterns of energy consumption and how they’ve shifted over the years, they conduct descriptive research. The focus is on recording and portraying the current state without attempting to infer causes or predict the future.

12. Literacy and Employment Rates in Rural Areas (Sociology): A study aims at describing the literacy rates in rural areas and correlating it with employment levels. It falls under descriptive research because it maps the scenario without manipulating parameters or proving a hypothesis.

13. Women Representation in Tech Industry (Gender Studies): A detailed description of the presence and roles of women across various sectors of the tech industry is a typical case of descriptive research. It merely observes and records the status quo without establishing causality or making predictions.

14. Impact of Urban Green Spaces on Mental Health (Environmental Psychology): When researchers document and describe the influence of green urban spaces on residents’ mental health, they are undertaking descriptive research. They seek purely to understand the current state rather than exploring cause-effect relationships.

15. Trends in Smartphone usage among Elderly (Gerontology): Research describing how the elderly population utilizes smartphones, including popular features and challenges encountered, serves as descriptive research. Researcher’s aim is merely to capture what is happening without manipulating variables or posing predictions.

16. Shifts in Voter Preferences (Political Science): A study describing the shift in voter preferences during a particular electoral cycle is descriptive research. It simply records the preferences revealed without drawing causal inferences or suggesting future voting patterns.

17. Understanding Trust in Autonomous Vehicles (Transportation Psychology): This comprises research describing public attitudes and trust levels when it comes to autonomous vehicles. By merely depicting observed sentiments, without engineering any situations or offering predictions, it’s considered descriptive research.

18. The Impact of Social Media on Body Image (Psychology): Descriptive research to outline the experiences and perceptions of individuals relating to body image in the era of social media. Observing these elements without altering any variables qualifies it as descriptive research.

Descriptive vs Experimental Research

Descriptive research merely observes, records, and presents the actual state of affairs without manipulating any variables, while experimental research involves deliberately changing one or more variables to determine their effect on a particular outcome.

De Vaus (2001) succinctly explains that descriptive studies find out what is going on , but experimental research finds out why it’s going on /

Simple definitions are below:

• Descriptive research is primarily about describing the characteristics or behaviors in a population, often through surveys or observational methods. It provides rich detail about a specific phenomenon but does not allow for conclusive causal statements; however, it can offer essential leads or ideas for further experimental research (Ivey, 2016).
• Experimental research , often conducted in controlled environments, aims to establish causal relationships by manipulating one or more independent variables and observing the effects on dependent variables (Devi, 2017; Mukherjee, 2019).

Experimental designs often involve a control group and random assignment . While it can provide compelling evidence for cause and effect, its artificial setting might not perfectly mirror real-worldly conditions, potentially affecting the generalizability of its findings.

These two types of research are complementary, with descriptive studies often leading to hypotheses that are then tested experimentally (Devi, 2017; Zhao et al., 2021).

Benefits and Limitations of Descriptive Research

Descriptive research offers several benefits: it allows researchers to gather a vast amount of data and present a complete picture of the situation or phenomenon under study, even within large groups or over long time periods.

It’s also flexible in terms of the variety of methods used, such as surveys, observations, and case studies, and it can be instrumental in identifying patterns or trends and generating hypotheses (Erickson, 2017).

However, it also has its limitations.

The primary drawback is that it can’t establish cause-effect relationships, as no variables are manipulated. This lack of control over variables also opens up possibilities for bias, as researchers might inadvertently influence responses during data collection (De Vaus, 2001).

Additionally, the findings of descriptive research are often not generalizable since they are heavily reliant on the chosen sample’s characteristics.

See More Types of Research Design Here

De Vaus, D. A. (2001). Research Design in Social Research . SAGE Publications.

Devi, P. S. (2017). Research Methodology: A Handbook for Beginners . Notion Press.

Erickson, G. S. (2017). Descriptive research design. In  New Methods of Market Research and Analysis  (pp. 51-77). Edward Elgar Publishing.

Gresham, B. B. (2016). Concepts of Evidence-based Practice for the Physical Therapist Assistant . F.A. Davis Company.

Ivey, J. (2016). Is descriptive research worth doing?.  Pediatric nursing ,  42 (4), 189. ( Source )

Krishnaswamy, K. N., Sivakumar, A. I., & Mathirajan, M. (2009). Management Research Methodology: Integration of Principles, Methods and Techniques . Pearson Education.

Matanda, E. (2022). Research Methods and Statistics for Cross-Cutting Research: Handbook for Multidisciplinary Research . Langaa RPCIG.

Monsen, E. R., & Van Horn, L. (2007). Research: Successful Approaches . American Dietetic Association.

Mukherjee, S. P. (2019). A Guide to Research Methodology: An Overview of Research Problems, Tasks and Methods . CRC Press.

Siedlecki, S. L. (2020). Understanding descriptive research designs and methods.  Clinical Nurse Specialist ,  34 (1), 8-12. ( Source )

Zhao, P., Ross, K., Li, P., & Dennis, B. (2021). Making Sense of Social Research Methodology: A Student and Practitioner Centered Approach . SAGE Publications.

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Positive Punishment Examples
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• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Zone of Proximal Development Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ Perception Checking: 15 Examples and Definition

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

• Chris Drew (PhD) #molongui-disabled-link 25 Positive Punishment Examples
• Chris Drew (PhD) #molongui-disabled-link 25 Dissociation Examples (Psychology)
• Chris Drew (PhD) #molongui-disabled-link 15 Zone of Proximal Development Examples
• Chris Drew (PhD) #molongui-disabled-link Perception Checking: 15 Examples and Definition

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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Muhammad Hassan

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• Descriptive Research Designs: Types, Examples & Methods

One of the components of research is getting enough information about the research problem—the what, how, when and where answers, which is why descriptive research is an important type of research. It is very useful when conducting research whose aim is to identify characteristics, frequencies, trends, correlations, and categories.

This research method takes a problem with little to no relevant information and gives it a befitting description using qualitative and quantitative research method s. Descriptive research aims to accurately describe a research problem.

In the subsequent sections, we will be explaining what descriptive research means, its types, examples, and data collection methods.

What is Descriptive Research?

Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why.

This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place. 

For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in.

What Are The Types of Descriptive Research?

Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:

• Descriptive-survey

Descriptive survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.

For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument , and each item on the survey related to qualifications is subjected to a Yes/No answer. 

This way, the researcher can describe the qualifications possessed by the employed demographics of this community. 

• Descriptive-normative survey

This is an extension of the descriptive survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.

For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.

If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.

• Descriptive-status

This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.

A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.

• Descriptive-analysis

The descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.

A questionnaire is devised to analyze the job role of employees with similar salaries and who work in similar positions.

• Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.

• Descriptive-comparative

In descriptive-comparative research, the researcher considers 2 variables that are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.

A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.

• Correlative Survey

Correlative surveys are used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables say X and Y are directly proportional, inversely proportional or are not related to each other.

Examples of Descriptive Research

There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article.

• Comparing Student Performance:

An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field.

Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify students’ weak points and work on ways to assist them.

• Scientific Classification

During the major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified.

For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, invertebrae, etc. 

All these classifications are made a result of descriptive research which describes what they are.

• Human Behavior

When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use it to conclude. A company willing to sell to its target market needs to first study the behaviour of the market.

This may be done by observing how its target reacts to a competitor’s product, then use it to determine their behaviour.

What are the Characteristics of Descriptive Research?  

The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are:

• Quantitativeness

Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences.

• Qualitativeness

It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratory or experimental.

• Uncontrolled variables

In descriptive research, researchers cannot control the variables like they do in experimental research.

• The basis for further research

The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used.

This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is.

Why Use Descriptive Research Design?  

Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience.

• Define subject characteristics :

It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects.

For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community.

• Measure Data Trends

It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision.

Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions.

Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.

This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do?

Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales.

• Validate existing conditions

When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions.

• Conducted Overtime

Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be.

What are the Disadvantages of Descriptive Research?  

• Response and Non-response Bias

Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched.

• The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results.
• A case-study or sample taken from a large population is not representative of the whole population.
• Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.

What are the Data Collection Methods in Descriptive Research?  

There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research.

1. Observational Method

The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour.

It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research . This process can be said to be either quantitative or qualitative.

Quantitative observation involved the objective collection of numerical data , whose results can be analyzed using numerical and statistical methods. 

Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions.

2. Case Study Method

A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group.

This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization.

3. Survey Research

This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers.

Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies.

Survey research can be carried out both online and offline using the following methods

• Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Formplus, an online survey builder. Formplus has amazing tools and features that will help increase response rates.
• Offline Surveys: This includes paper forms, mobile offline forms , and SMS-based forms.

What Are The Differences Between Descriptive and Correlational Research?  

Before going into the differences between descriptive and correlation research, we need to have a proper understanding of what correlation research is about. Therefore, we will be giving a summary of the correlation research below.

Correlational research is a type of descriptive research, which is used to measure the relationship between 2 variables, with the researcher having no control over them. It aims to find whether there is; positive correlation (both variables change in the same direction), negative correlation (the variables change in the opposite direction), or zero correlation (there is no relationship between the variables).

Correlational research may be used in 2 situations;

(i) when trying to find out if there is a relationship between two variables, and

(ii) when a causal relationship is suspected between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables. 

Below are some of the differences between correlational and descriptive research:

• Definitions :

Descriptive research aims is a type of research that provides an in-depth understanding of the study population, while correlational research is the type of research that measures the relationship between 2 variables. 

• Characteristics :

Descriptive research provides descriptive data explaining what the research subject is about, while correlation research explores the relationship between data and not their description.

• Predictions :

 Predictions cannot be made in descriptive research while correlation research accommodates the possibility of making predictions.

Descriptive Research vs. Causal Research

Descriptive research and causal research are both research methodologies, however, one focuses on a subject’s behaviors while the latter focuses on a relationship’s cause-and-effect. To buttress the above point, descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular or specific population or situation. 

It focuses on providing an accurate and detailed account of an already existing state of affairs between variables. Descriptive research answers the questions of “what,” “where,” “when,” and “how” without attempting to establish any causal relationships or explain any underlying factors that might have caused the behavior.

Causal research, on the other hand, seeks to determine cause-and-effect relationships between variables. It aims to point out the factors that influence or cause a particular result or behavior. Causal research involves manipulating variables, controlling conditions or a subgroup, and observing the resulting effects. The primary objective of causal research is to establish a cause-effect relationship and provide insights into why certain phenomena happen the way they do.

Descriptive Research vs. Analytical Research

Descriptive research provides a detailed and comprehensive account of a specific situation or phenomenon. It focuses on describing and summarizing data without making inferences or attempting to explain underlying factors or the cause of the factor. 

It is primarily concerned with providing an accurate and objective representation of the subject of research. While analytical research goes beyond the description of the phenomena and seeks to analyze and interpret data to discover if there are patterns, relationships, or any underlying factors. 

It examines the data critically, applies statistical techniques or other analytical methods, and draws conclusions based on the discovery. Analytical research also aims to explore the relationships between variables and understand the underlying mechanisms or processes involved.

Descriptive Research vs. Exploratory Research

Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause. 

Descriptive research typically involves collecting and analyzing quantitative or qualitative data to generate descriptive statistics or narratives. Exploratory research differs from descriptive research because it aims to explore and gain firsthand insights or knowledge into a relatively unexplored or poorly understood topic. 

It focuses on generating ideas, hypotheses, or theories rather than providing definitive answers. Exploratory research is often conducted at the early stages of a research project to gather preliminary information and identify key variables or factors for further investigation. It involves open-ended interviews, observations, or small-scale surveys to gather qualitative data.

Read More – Exploratory Research: What are its Method & Examples?

Descriptive Research vs. Experimental Research

Descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular population or situation. It focuses on providing an accurate and detailed account of the existing state of affairs. 

Descriptive research typically involves collecting data through surveys, observations, or existing records and analyzing the data to generate descriptive statistics or narratives. It does not involve manipulating variables or establishing cause-and-effect relationships.

Experimental research, on the other hand, involves manipulating variables and controlling conditions to investigate cause-and-effect relationships. It aims to establish causal relationships by introducing an intervention or treatment and observing the resulting effects. 

Experimental research typically involves randomly assigning participants to different groups, such as control and experimental groups, and measuring the outcomes. It allows researchers to control for confounding variables and draw causal conclusions.

Related – Experimental vs Non-Experimental Research: 15 Key Differences

Descriptive Research vs. Explanatory Research

Descriptive research focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. It aims to describe the characteristics, behaviors, or relationships within the given context. 

Descriptive research is primarily concerned with providing an objective representation of the subject of study without explaining underlying causes or mechanisms. Explanatory research seeks to explain the relationships between variables and uncover the underlying causes or mechanisms. 

It goes beyond description and aims to understand the reasons or factors that influence a particular outcome or behavior. Explanatory research involves analyzing data, conducting statistical analyses, and developing theories or models to explain the observed relationships.

Descriptive Research vs. Inferential Research

Descriptive research focuses on describing and summarizing data without making inferences or generalizations beyond the specific sample or population being studied. It aims to provide an accurate and objective representation of the subject of study. 

Descriptive research typically involves analyzing data to generate descriptive statistics, such as means, frequencies, or percentages, to describe the characteristics or behaviors observed.

Inferential research, however, involves making inferences or generalizations about a larger population based on a smaller sample. 

It aims to draw conclusions about the population characteristics or relationships by analyzing the sample data. Inferential research uses statistical techniques to estimate population parameters, test hypotheses, and determine the level of confidence or significance in the findings.

Related – Inferential Statistics: Definition, Types + Examples

Conclusion  

The uniqueness of descriptive research partly lies in its ability to explore both quantitative and qualitative research methods. Therefore, when conducting descriptive research, researchers have the opportunity to use a wide variety of techniques that aids the research process.

Descriptive research explores research problems in-depth, beyond the surface level thereby giving a detailed description of the research subject. That way, it can aid further research in the field, including other research methods .

It is also very useful in solving real-life problems in various fields of social science, physical science, and education.

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Descriptive Research 101: Definition, Methods and Examples

Parvathi vijayamohan.

8 April 2024

Table Of Contents

• Descriptive Research 101: The Definitive Guide

What is Descriptive Research?

Key characteristics of descriptive research.

• Descriptive Research Methods: The 3 You Need to Know!

Observation

Case studies, 7 types of descriptive research, descriptive research: examples to build your next study, tips to excel at descriptive research.

Imagine you are a detective called to a crime scene. Your job is to study the scene and report whatever you find: whether that’s the half-smoked cigarette on the table or the large “RACHE” written in blood on the wall. That, in a nutshell, is  descriptive research .

Researchers often need to do descriptive research on a problem before they attempt to solve it. So in this guide, we’ll take you through:

• What is descriptive research + characteristics
• Descriptive research methods
• Types of descriptive research
• Descriptive research examples
• Tips to excel at the descriptive method

Click to jump to the section that interests you.

Definition: As its name says, descriptive research  describes  the characteristics of the problem, phenomenon, situation, or group under study.

So the goal of all descriptive studies is to  explore  the background, details, and existing patterns in the problem to fully understand it. In other words, preliminary research.

However, descriptive research can be both  preliminary and conclusive . You can use the data from a descriptive study to make reports and get insights for further planning.

What descriptive research isn’t: Descriptive research finds the  what/when/where  of a problem, not the  why/how .

Because of this, we can’t use the descriptive method to explore cause-and-effect relationships where one variable (like a person’s job role) affects another variable (like their monthly income).

• Answers the “what,” “when,” and “where”  of a research problem. For this reason, it is popularly used in  market research ,  awareness surveys , and  opinion polls .
• Sets the stage  for a research problem. As an early part of the research process, descriptive studies help you dive deeper into the topic.
• Opens the door  for further research. You can use descriptive data as the basis for more profound research, analysis and studies.
• Qualitative and quantitative . It is possible to get a balanced mix of numerical responses and open-ended answers from the descriptive method.
• No control or interference with the variables . The researcher simply observes and reports on them. However, specific research software has filters that allow her to zoom in on one variable.
• Done in natural settings . You can get the best results from descriptive research by talking to people, surveying them, or observing them in a suitable environment. For example, suppose you are a website beta testing an app feature. In that case, descriptive research invites users to try the feature, tracking their behavior and then asking their opinions .
• Can be applied to many research methods and areas. Examples include healthcare, SaaS, psychology, political studies, education, and pop culture.

Descriptive Research Methods: The Top Three You Need to Know!

In short, survey research is a brief interview or conversation with a set of prepared questions about a topic.

So you create a questionnaire, share it, and analyze the data you collect for further action. Learn about the differences between surveys and questionnaires  here .

You can access free survey templates , over 20+ question types, and pass data to 1,500+ applications with survey software, like SurveySparrow . It enables you to create surveys, share them and capture data with very little effort.

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• Surveys can be hyper-local, regional, or global, depending on your objectives.
• Share surveys in-person, offline, via SMS, email, or QR codes – so many options!
• Easy to automate if you want to conduct many surveys over a period.

The observational method is a type of descriptive research in which you, the researcher, observe ongoing behavior.

Now, there are several (non-creepy) ways you can observe someone. In fact, observational research has three main approaches:

• Covert observation: In true spy fashion, the researcher mixes in with the group undetected or observes from a distance.
• Overt observation : The researcher identifies himself as a researcher – “The name’s Bond. J. Bond.” – and explains the purpose of the study.
• Participatory observation : The researcher participates in what he is observing to understand his topic better.
• Observation is one of the most accurate ways to get data on a subject’s behavior in a natural setting.
• You don’t need to rely on people’s willingness to share information.
• Observation is a universal method that can be applied to any area of research.

In the case study method, you do a detailed study of a specific group, person, or event over a period.

This brings us to a frequently asked question: “What’s the difference between case studies and longitudinal studies?”

A case study will go  very in-depth into the subject with one-on-one interviews, observations, and archival research. They are also qualitative, though sometimes they will use numbers and stats.

An example of longitudinal research would be a study of the health of night shift employees vs. general shift employees over a decade. An example of a case study would involve in-depth interviews with Casey, an assistant director of nursing who’s handled the night shift at the hospital for ten years now.

• Due to the focus on a few people, case studies can give you a tremendous amount of information.
• Because of the time and effort involved, a case study engages both researchers and participants.
• Case studies are helpful for ethically investigating unusual, complex, or challenging subjects. An example would be a study of the habits of long-term cocaine users.

1. Case Study: Airbnb’s Growth Strategy

In an excellent case study, Tam Al Saad, Principal Consultant, Strategy + Growth at Webprofits, deep dives into how Airbnb attracted and retained 150 million users .

“What Airbnb offers isn’t a cheap place to sleep when you’re on holiday; it’s the opportunity to experience your destination as a local would. It’s the chance to meet the locals, experience the markets, and find non-touristy places.

Sure, you can visit the Louvre, see Buckingham Palace, and climb the Empire State Building, but you can do it as if it were your hometown while staying in a place that has character and feels like a home.” – Tam al Saad, Principal Consultant, Strategy + Growth at Webprofits

2. Observation – Better Tech Experiences for the Elderly

We often think that our elders are so hopeless with technology. But we’re not getting any younger either, and tech is changing at a hair trigger! This article by Annemieke Hendricks shares a wonderful example where researchers compare the levels of technological familiarity between age groups and how that influences usage.

“It is generally assumed that older adults have difficulty using modern electronic devices, such as mobile telephones or computers. Because this age group is growing in most countries, changing products and processes to adapt to their needs is increasingly more important. “ – Annemieke Hendricks, Marketing Communication Specialist, Noldus

3. Surveys – Decoding Sleep with SurveySparrow

SRI International (formerly Stanford Research Institute) – an independent, non-profit research center – wanted to investigate the impact of stress on an adolescent’s sleep. To get those insights, two actions were essential: tracking sleep patterns through wearable devices and sending surveys at a pre-set time –  the pre-sleep period.

“With SurveySparrow’s recurring surveys feature, SRI was able to share engaging surveys with their participants exactly at the time they wanted and at the frequency they preferred.”

Read more about this project : How SRI International decoded sleep patterns with SurveySparrow

1: Answer the six Ws –

• Who should we consider?
• What information do we need?
• When should we collect the information?
• Where should we collect the information?
• Why are we obtaining the information?
• Way to collect the information

#2: Introduce and explain your methodological approach

#3: Describe your methods of data collection and/or selection.

#4: Describe your methods of analysis.

#5: Explain the reasoning behind your choices.

#6: Collect data.

#7: Analyze the data. Use software to speed up the process and reduce overthinking and human error.

#8: Report your conclusions and how you drew the results.

Wrapping Up

That’s all, folks!

Growth Marketer at SurveySparrow

Fledgling growth marketer. Cloud watcher. Aunty to a naughty beagle.

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• What is descriptive research?

Last updated

5 February 2023

Reviewed by

Cathy Heath

Descriptive research is a common investigatory model used by researchers in various fields, including social sciences, linguistics, and academia.

Read on to understand the characteristics of descriptive research and explore its underlying techniques, processes, and procedures.

Analyze your descriptive research

Dovetail streamlines analysis to help you uncover and share actionable insights

Descriptive research is an exploratory research method. It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.

As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses . This can be reported using surveys , observational studies, and case studies. You can use both quantitative and qualitative methods to compile the data.

Besides making observations and then comparing and analyzing them, descriptive studies often develop knowledge concepts and provide solutions to critical issues. It always aims to answer how the event occurred, when it occurred, where it occurred, and what the problem or phenomenon is.

• Characteristics of descriptive research

The following are some of the characteristics of descriptive research:

Quantitativeness

Descriptive research can be quantitative as it gathers quantifiable data to statistically analyze a population sample. These numbers can show patterns, connections, and trends over time and can be discovered using surveys, polls, and experiments.

Qualitativeness

Descriptive research can also be qualitative. It gives meaning and context to the numbers supplied by quantitative descriptive research .

Researchers can use tools like interviews, focus groups, and ethnographic studies to illustrate why things are what they are and help characterize the research problem. This is because it’s more explanatory than exploratory or experimental research.

Uncontrolled variables

Descriptive research differs from experimental research in that researchers cannot manipulate the variables. They are recognized, scrutinized, and quantified instead. This is one of its most prominent features.

Cross-sectional studies

Descriptive research is a cross-sectional study because it examines several areas of the same group. It involves obtaining data on multiple variables at the personal level during a certain period. It’s helpful when trying to understand a larger community’s habits or preferences.

Carried out in a natural environment

Descriptive studies are usually carried out in the participants’ everyday environment, which allows researchers to avoid influencing responders by collecting data in a natural setting. You can use online surveys or survey questions to collect data or observe.

Basis for further research

You can further dissect descriptive research’s outcomes and use them for different types of investigation. The outcomes also serve as a foundation for subsequent investigations and can guide future studies. For example, you can use the data obtained in descriptive research to help determine future research designs.

• Descriptive research methods

There are three basic approaches for gathering data in descriptive research: observational, case study, and survey.

You can use surveys to gather data in descriptive research. This involves gathering information from many people using a questionnaire and interview .

Surveys remain the dominant research tool for descriptive research design. Researchers can conduct various investigations and collect multiple types of data (quantitative and qualitative) using surveys with diverse designs.

You can conduct surveys over the phone, online, or in person. Your survey might be a brief interview or conversation with a set of prepared questions intended to obtain quick information from the primary source.

Observation

This descriptive research method involves observing and gathering data on a population or phenomena without manipulating variables. It is employed in psychology, market research , and other social science studies to track and understand human behavior.

Observation is an essential component of descriptive research. It entails gathering data and analyzing it to see whether there is a relationship between the two variables in the study. This strategy usually allows for both qualitative and quantitative data analysis.

Case studies

A case study can outline a specific topic’s traits. The topic might be a person, group, event, or organization.

It involves using a subset of a larger group as a sample to characterize the features of that larger group.

You can generalize knowledge gained from studying a case study to benefit a broader audience.

This approach entails carefully examining a particular group, person, or event over time. You can learn something new about the study topic by using a small group to better understand the dynamics of the entire group.

• Types of descriptive research

There are several types of descriptive study. The most well-known include cross-sectional studies, census surveys, sample surveys, case reports, and comparison studies.

Case reports and case series

In the healthcare and medical fields, a case report is used to explain a patient’s circumstances when suffering from an uncommon illness or displaying certain symptoms. Case reports and case series are both collections of related cases. They have aided the advancement of medical knowledge on countless occasions.

The normative component is an addition to the descriptive survey. In the descriptive–normative survey, you compare the study’s results to the norm.

Descriptive survey

This descriptive type of research employs surveys to collect information on various topics. This data aims to determine the degree to which certain conditions may be attained.

You can extrapolate or generalize the information you obtain from sample surveys to the larger group being researched.

Correlative survey

Correlative surveys help establish if there is a positive, negative, or neutral connection between two variables.

Performing census surveys involves gathering relevant data on several aspects of a given population. These units include individuals, families, organizations, objects, characteristics, and properties.

During descriptive research, you gather different degrees of interest over time from a specific population. Cross-sectional studies provide a glimpse of a phenomenon’s prevalence and features in a population. There are no ethical challenges with them and they are quite simple and inexpensive to carry out.

Comparative studies

These surveys compare the two subjects’ conditions or characteristics. The subjects may include research variables, organizations, plans, and people.

Comparison points, assumption of similarities, and criteria of comparison are three important variables that affect how well and accurately comparative studies are conducted.

For instance, descriptive research can help determine how many CEOs hold a bachelor’s degree and what proportion of low-income households receive government help.

• Pros and cons

The primary advantage of descriptive research designs is that researchers can create a reliable and beneficial database for additional study. To conduct any inquiry, you need access to reliable information sources that can give you a firm understanding of a situation.

Quantitative studies are time- and resource-intensive, so knowing the hypotheses viable for testing is crucial. The basic overview of descriptive research provides helpful hints as to which variables are worth quantitatively examining. This is why it’s employed as a precursor to quantitative research designs.

Some experts view this research as untrustworthy and unscientific. However, there is no way to assess the findings because you don’t manipulate any variables statistically.

Cause-and-effect correlations also can’t be established through descriptive investigations. Additionally, observational study findings cannot be replicated, which prevents a review of the findings and their replication.

The absence of statistical and in-depth analysis and the rather superficial character of the investigative procedure are drawbacks of this research approach.

• Descriptive research examples and applications

Several descriptive research examples are emphasized based on their types, purposes, and applications. Research questions often begin with “What is …” These studies help find solutions to practical issues in social science, physical science, and education.

Here are some examples and applications of descriptive research:

Determining consumer perception and behavior

Organizations use descriptive research designs to determine how various demographic groups react to a certain product or service.

For example, a business looking to sell to its target market should research the market’s behavior first. When researching human behavior in response to a cause or event, the researcher pays attention to the traits, actions, and responses before drawing a conclusion.

Scientific classification

Scientific descriptive research enables the classification of organisms and their traits and constituents.

Measuring data trends

A descriptive study design’s statistical capabilities allow researchers to track data trends over time. It’s frequently used to determine the study target’s current circumstances and underlying patterns.

Conduct comparison

Organizations can use a descriptive research approach to learn how various demographics react to a certain product or service. For example, you can study how the target market responds to a competitor’s product and use that information to infer their behavior.

• Bottom line

A descriptive research design is suitable for exploring certain topics and serving as a prelude to larger quantitative investigations. It provides a comprehensive understanding of the “what” of the group or thing you’re investigating.

This research type acts as the cornerstone of other research methodologies . It is distinctive because it can use quantitative and qualitative research approaches at the same time.

What is descriptive research design?

Descriptive research design aims to systematically obtain information to describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem rather than the why.

How does descriptive research compare to qualitative research?

Despite certain parallels, descriptive research concentrates on describing phenomena, while qualitative research aims to understand people better.

How do you analyze descriptive research data?

Data analysis involves using various methodologies, enabling the researcher to evaluate and provide results regarding validity and reliability.

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Study designs: Part 2 – Descriptive studies

Rakesh aggarwal.

Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Priya Ranganathan

1 Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

One of the first steps in planning a research study is the choice of study design. The available study designs are divided broadly into two types – observational and interventional. Of the various observational study designs, the descriptive design is the simplest. It allows the researcher to study and describe the distribution of one or more variables, without regard to any causal or other hypotheses. This article discusses the subtypes of descriptive study design, and their strengths and limitations.

INTRODUCTION

In our previous article in this series,[ 1 ] we introduced the concept of “study designs”– as “the set of methods and procedures used to collect and analyze data on variables specified in a particular research question.” Study designs are primarily of two types – observational and interventional, with the former being loosely divided into “descriptive” and “analytical.” In this article, we discuss the descriptive study designs.

WHAT IS A DESCRIPTIVE STUDY?

A descriptive study is one that is designed to describe the distribution of one or more variables, without regard to any causal or other hypothesis.

TYPES OF DESCRIPTIVE STUDIES

Descriptive studies can be of several types, namely, case reports, case series, cross-sectional studies, and ecological studies. In the first three of these, data are collected on individuals, whereas the last one uses aggregated data for groups.

Case reports and case series

A case report refers to the description of a patient with an unusual disease or with simultaneous occurrence of more than one condition. A case series is similar, except that it is an aggregation of multiple (often only a few) similar cases. Many case reports and case series are anecdotal and of limited value. However, some of these bring to the fore a hitherto unrecognized disease and play an important role in advancing medical science. For instance, HIV/AIDS was first recognized through a case report of disseminated Kaposi's sarcoma in a young homosexual man,[ 2 ] and a case series of such men with Pneumocystis carinii pneumonia.[ 3 ]

In other cases, description of a chance observation may open an entirely new line of investigation. Some examples include: fatal disseminated Bacillus Calmette–Guérin infection in a baby born to a mother taking infliximab for Crohn's disease suggesting that adminstration of infliximab may bring about reactivation of tuberculosis,[ 4 ] progressive multifocal leukoencephalopathy following natalizumab treatment – describing a new adverse effect of drugs that target cell adhesion molecule α4-integrin,[ 5 ] and demonstration of a tumor caused by invasive transformed cancer cells from a colonizing tapeworm in an HIV-infected person.[ 6 ]

Cross-sectional studies

Studies with a cross-sectional study design involve the collection of information on the presence or level of one or more variables of interest (health-related characteristic), whether exposure (e.g., a risk factor) or outcome (e.g., a disease) as they exist in a defined population at one particular time. If these data are analyzed only to determine the distribution of one or more variables, these are “descriptive.” However, often, in a cross-sectional study, the investigator also assesses the relationship between the presence of an exposure and that of an outcome. Such cross-sectional studies are referred to as “analytical” and will be discussed in the next article in this series.

Cross-sectional studies can be thought of as providing a “snapshot” of the frequency and characteristics of a disease in a population at a particular point in time. These are very good for measuring the prevalence of a disease or of a risk factor in a population. Thus, these are very helpful in assessing the disease burden and healthcare needs.

Let us look at a study that was aimed to assess the prevalence of myopia among Indian children.[ 7 ] In this study, trained health workers visited schools in Delhi and tested visual acuity in all children studying in classes 1–9. Of the 9884 children screened, 1297 (13.1%) had myopia (defined as spherical refractive error of −0.50 diopters (D) or worse in either or both eyes), and the mean myopic error was −1.86 ± 1.4 D. Furthermore, overall, 322 (3.3%), 247 (2.5%) and 3 children had mild, moderate, and severe visual impairment, respectively. These parts of the study looked at the prevalence and degree of myopia or of visual impairment, and did not assess the relationship of one variable with another or test a causative hypothesis – these qualify as a descriptive cross-sectional study. These data would be helpful to a health planner to assess the need for a school eye health program, and to know the proportion of children in her jurisdiction who would need corrective glasses.

The authors did, subsequently in the paper, look at the relationship of myopia (an outcome) with children's age, gender, socioeconomic status, type of school, mother's education, etc. (each of which qualifies as an exposure). Those parts of the paper look at the relationship between different variables and thus qualify as having “analytical” cross-sectional design.

Sometimes, cross-sectional studies are repeated after a time interval in the same population (using the same subjects as were included in the initial study, or a fresh sample) to identify temporal trends in the occurrence of one or more variables, and to determine the incidence of a disease (i.e., number of new cases) or its natural history. Indeed, the investigators in the myopia study above visited the same children and reassessed them a year later. This separate follow-up study[ 8 ] showed that “new” myopia had developed in 3.4% of children (incidence rate), with a mean change of −1.09 ± 0.55 D. Among those with myopia at the time of the initial survey, 49.2% showed progression of myopia with a mean change of −0.27 ± 0.42 D.

Cross-sectional studies are usually simple to do and inexpensive. Furthermore, these usually do not pose much of a challenge from an ethics viewpoint.

However, this design does carry a risk of bias, i.e., the results of the study may not represent the true situation in the population. This could arise from either selection bias or measurement bias. The former relates to differences between the population and the sample studied. The myopia study included only those children who attended school, and the prevalence of myopia could have been different in those did not attend school (e.g., those with severe myopia may not be able to see the blackboard and hence may have been more likely to drop out of school). The measurement bias in this study would relate to the accuracy of measurement and the cutoff used. If the investigators had used a cutoff of −0.25 D (instead of −0.50 D) to define myopia, the prevalence would have been higher. Furthermore, if the measurements were not done accurately, some cases with myopia could have been missed, or vice versa, affecting the study results.

Ecological studies

Ecological (also sometimes called as correlational) study design involves looking for association between an exposure and an outcome across populations rather than in individuals. For instance, a study in the United States found a relation between household firearm ownership in various states and the firearm death rates during the period 2007–2010.[ 9 ] Thus, in this study, the unit of assessment was a state and not an individual.

These studies are convenient to do since the data have often already been collected and are available from a reliable source. This design is particularly useful when the differences in exposure between individuals within a group are much smaller than the differences in exposure between groups. For instance, the intake of particular food items is likely to vary less between people in a particular group but can vary widely across groups, for example, people living in different countries.

However, the ecological study design has some important limitations.First, an association between exposure and outcome at the group level may not be true at the individual level (a phenomenon also referred to as “ecological fallacy”).[ 10 ] Second, the association may be related to a third factor which in turn is related to both the exposure and the outcome, the so-called “confounding”. For instance, an ecological association between higher income level and greater cardiovascular mortality across countries may be related to a higher prevalence of obesity. Third, migration of people between regions with different exposure levels may also introduce an error. A fourth consideration may be the use of differing definitions for exposure, outcome or both in different populations.

Descriptive studies, irrespective of the subtype, are often very easy to conduct. For case reports, case series, and ecological studies, the data are already available. For cross-sectional studies, these can be easily collected (usually in one encounter). Thus, these study designs are often inexpensive, quick and do not need too much effort. Furthermore, these studies often do not face serious ethics scrutiny, except if the information sought to be collected is of confidential nature (e.g., sexual practices, substance use, etc.).

Descriptive studies are useful for estimating the burden of disease (e.g., prevalence or incidence) in a population. This information is useful for resource planning. For instance, information on prevalence of cataract in a city may help the government decide on the appropriate number of ophthalmologic facilities. Data from descriptive studies done in different populations or done at different times in the same population may help identify geographic variation and temporal change in the frequency of disease. This may help generate hypotheses regarding the cause of the disease, which can then be verified using another, more complex design.

DISADVANTAGES

As with other study designs, descriptive studies have their own pitfalls. Case reports and case-series refer to a solitary patient or to only a few cases, who may represent a chance occurrence. Hence, conclusions based on these run the risk of being non-representative, and hence unreliable. In cross-sectional studies, the validity of results is highly dependent on whether the study sample is well representative of the population proposed to be studied, and whether all the individual measurements were made using an accurate and identical tool, or not. If the information on a variable cannot be obtained accurately, for instance in a study where the participants are asked about socially unacceptable (e.g., promiscuity) or illegal (e.g., substance use) behavior, the results are unlikely to be reliable.

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Descriptive research: what it is and how to use it.

8 min read Understanding the who, what and where of a situation or target group is an essential part of effective research and making informed business decisions.

For example you might want to understand what percentage of CEOs have a bachelor’s degree or higher. Or you might want to understand what percentage of low income families receive government support – or what kind of support they receive.

Descriptive research is what will be used in these types of studies.

In this guide we’ll look through the main issues relating to descriptive research to give you a better understanding of what it is, and how and why you can use it.

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What is descriptive research?

Descriptive research is a research method used to try and determine the characteristics of a population or particular phenomenon.

Using descriptive research you can identify patterns in the characteristics of a group to essentially establish everything you need to understand apart from why something has happened.

Market researchers use descriptive research for a range of commercial purposes to guide key decisions.

For example you could use descriptive research to understand fashion trends in a given city when planning your clothing collection for the year. Using descriptive research you can conduct in depth analysis on the demographic makeup of your target area and use the data analysis to establish buying patterns.

Conducting descriptive research wouldn’t, however, tell you why shoppers are buying a particular type of fashion item.

Descriptive research design

Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis.

As a survey method, descriptive research designs will help researchers identify characteristics in their target market or particular population.

These characteristics in the population sample can be identified, observed and measured to guide decisions.

Descriptive research characteristics

While there are a number of descriptive research methods you can deploy for data collection, descriptive research does have a number of predictable characteristics.

Here are a few of the things to consider:

Measure data trends with statistical outcomes

Descriptive research is often popular for survey research because it generates answers in a statistical form, which makes it easy for researchers to carry out a simple statistical analysis to interpret what the data is saying.

Descriptive research design is ideal for further research

Because the data collection for descriptive research produces statistical outcomes, it can also be used as secondary data for another research study.

Plus, the data collected from descriptive research can be subjected to other types of data analysis .

Uncontrolled variables

A key component of the descriptive research method is that it uses random variables that are not controlled by the researchers. This is because descriptive research aims to understand the natural behavior of the research subject.

It’s carried out in a natural environment

Descriptive research is often carried out in a natural environment. This is because researchers aim to gather data in a natural setting to avoid swaying respondents.

Data can be gathered using survey questions or online surveys.

For example, if you want to understand the fashion trends we mentioned earlier, you would set up a study in which a researcher observes people in the respondent’s natural environment to understand their habits and preferences.

Descriptive research allows for cross sectional study

Because of the nature of descriptive research design and the randomness of the sample group being observed, descriptive research is ideal for cross sectional studies – essentially the demographics of the group can vary widely and your aim is to gain insights from within the group.

This can be highly beneficial when you’re looking to understand the behaviors or preferences of a wider population.

Descriptive research advantages

There are many advantages to using descriptive research, some of them include:

Cost effectiveness

Because the elements needed for descriptive research design are not specific or highly targeted (and occur within the respondent’s natural environment) this type of study is relatively cheap to carry out.

Multiple types of data can be collected

A big advantage of this research type, is that you can use it to collect both quantitative and qualitative data. This means you can use the stats gathered to easily identify underlying patterns in your respondents’ behavior.

Descriptive research disadvantages

Potential reliability issues.

When conducting descriptive research it’s important that the initial survey questions are properly formulated.

If not, it could make the answers unreliable and risk the credibility of your study.

Potential limitations

As we’ve mentioned, descriptive research design is ideal for understanding the what, who or where of a situation or phenomenon.

However, it can’t help you understand the cause or effect of the behavior. This means you’ll need to conduct further research to get a more complete picture of a situation.

Descriptive research methods

Because descriptive research methods include a range of quantitative and qualitative research, there are several research methods you can use.

Use case studies

Case studies in descriptive research involve conducting in-depth and detailed studies in which researchers get a specific person or case to answer questions.

Case studies shouldn’t be used to generate results, rather it should be used to build or establish hypothesis that you can expand into further market research .

For example you could gather detailed data about a specific business phenomenon, and then use this deeper understanding of that specific case.

Use observational methods

This type of study uses qualitative observations to understand human behavior within a particular group.

By understanding how the different demographics respond within your sample you can identify patterns and trends.

As an observational method, descriptive research will not tell you the cause of any particular behaviors, but that could be established with further research.

Use survey research

Surveys are one of the most cost effective ways to gather descriptive data.

An online survey or questionnaire can be used in descriptive studies to gather quantitative information about a particular problem.

Survey research is ideal if you’re using descriptive research as your primary research.

Descriptive research examples

Descriptive research is used for a number of commercial purposes or when organizations need to understand the behaviors or opinions of a population.

One of the biggest examples of descriptive research that is used in every democratic country, is during elections.

Using descriptive research, researchers will use surveys to understand who voters are more likely to choose out of the parties or candidates available.

Using the data provided, researchers can analyze the data to understand what the election result will be.

In a commercial setting, retailers often use descriptive research to figure out trends in shopping and buying decisions.

By gathering information on the habits of shoppers, retailers can get a better understanding of the purchases being made.

Another example that is widely used around the world, is the national census that takes place to understand the population.

The research will provide a more accurate picture of a population’s demographic makeup and help to understand changes over time in areas like population age, health and education level.

Where Qualtrics helps with descriptive research

Whatever type of research you want to carry out, there’s a survey type that will work.

Qualtrics can help you determine the appropriate method and ensure you design a study that will deliver the insights you need.

Our experts can help you with your market research needs , ensuring you get the most out of Qualtrics market research software to design, launch and analyze your data to guide better, more accurate decisions for your organization.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 15 April 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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Descriptive Research: Definition, Characteristics, Methods + Examples

Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.

The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.

What is descriptive research?

Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.

The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.

Characteristics of descriptive research

The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Applications of descriptive research with examples

A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:

• Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
• Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
• Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
• Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
• Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

• Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
• Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
• Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
• Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.

Descriptive research methods

There are three distinctive methods to conduct descriptive research. They are:

Observational method

The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.

A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .

Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.

Case study method

Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.

Survey research

In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.

Examples of descriptive research

Some examples of descriptive research are:

• A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
• Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.

Some other research problems and research questions that can lead to descriptive research are:

• Market researchers want to observe the habits of consumers.
• A company wants to evaluate the morale of its staff.
• A school district wants to understand if students will access online lessons rather than textbooks.
• To understand if its wellness questionnaire programs enhance the overall health of the employees.

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Module 2: Research and Ethics in Abnormal Psychology

Descriptive research and case studies, learning objectives.

• Explain the importance and uses of descriptive research, especially case studies, in studying abnormal behavior

Types of Research Methods

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions; to extensive, in-depth interviews; to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research, it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While surveys allow results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While existing records can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later, there is a tremendous amount of control over variables of interest. While performing an experiment is a powerful approach, experiments are often conducted in very artificial settings, which calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

Clinical or Case Studies

Psychologists can use a detailed description of one person or a small group based on careful observation.  Case studies  are intensive studies of individuals and have commonly been seen as a fruitful way to come up with hypotheses and generate theories. Case studies add descriptive richness. Case studies are also useful for formulating concepts, which are an important aspect of theory construction. Through fine-grained knowledge and description, case studies can fully specify the causal mechanisms in a way that may be harder in a large study.

Sigmund Freud   developed  many theories from case studies (Anna O., Little Hans, Wolf Man, Dora, etc.). F or example, he conducted a case study of a man, nicknamed “Rat Man,”  in which he claimed that this patient had been cured by psychoanalysis.  T he nickname derives from the fact that among the patient’s many compulsions, he had an obsession with nightmarish fantasies about rats. 

Today, more commonly, case studies reflect an up-close, in-depth, and detailed examination of an individual’s course of treatment. Case studies typically include a complete history of the subject’s background and response to treatment. From the particular client’s experience in therapy, the therapist’s goal is to provide information that may help other therapists who treat similar clients.

Case studies are generally a single-case design, but can also be a multiple-case design, where replication instead of sampling is the criterion for inclusion. Like other research methodologies within psychology, the case study must produce valid and reliable results in order to be useful for the development of future research. Distinct advantages and disadvantages are associated with the case study in psychology.

A commonly described limit of case studies is that they do not lend themselves to generalizability . The other issue is that the case study is subject to the bias of the researcher in terms of how the case is written, and that cases are chosen because they are consistent with the researcher’s preconceived notions, resulting in biased research. Another common problem in case study research is that of reconciling conflicting interpretations of the same case history.

Despite these limitations, there are advantages to using case studies. One major advantage of the case study in psychology is the potential for the development of novel hypotheses of the  cause of abnormal behavior   for later testing. Second, the case study can provide detailed descriptions of specific and rare cases and help us study unusual conditions that occur too infrequently to study with large sample sizes. The major disadvantage is that case studies cannot be used to determine causation, as is the case in experimental research, where the factors or variables hypothesized to play a causal role are manipulated or controlled by the researcher. 

Single-Case Experimental Designs

The lack of control available in the traditional case study research strategy led researchers to develop more sophisticated methods, such as single-subject research, which provides the statistical framework for making inferences from quantitative case-study data.

Figure 1 . Antipsychotics are the treatment of choice in managing schizophrenia and other psychotic disorders. Several major trials have been conducted examining the clinical difference between typical antipsychotics and atypical antipsychotics and how the selection may affect the quality of life.

The single-case experimental design  (sometimes called  single-participant research designs ), is particularly useful for studies of treatment effectiveness.  In  single-case experimental designs ,  the same  research participant  serves as the subject in both the experimental and control conditions.  One of the most common forms of the single-case experimental design is the A-B-A-B design, or  reversal design ,  reflecting the alternation between conditions, or phases A and B. The  AB design is a two-part or phase design composed of a baseline (“A” phase) with no changes, and a treatment or intervention (“B”) phase.  If there is a change, then the treatment may be said to have had an effect. However, it is subject to many possible competing hypotheses, making strong conclusions difficult. The A-B-A-B design, or reversal design, is a variant on the AB design. It introduces ways to control for the competing hypotheses and allows for stronger conclusions. T he reversal design (ABAB) is the most powerful of the single-subject research designs because it shows a strong reversal from baseline (“A”) to treatment (“B”) and back again. In an ABAB design, researchers observe behaviors in the “A” phase, institute treatment in the “B” phase, and then repeat the process. If the variable returns to baseline measure without treatment and then resumes its effects when reapplied, the researcher can have greater confidence in the efficacy of that treatment. However, many interventions cannot be reversed for ethical reasons (e.g., involving self-injurious behavior like smoking).  It may be unethical to end an experiment on a baseline measure if the treatment is self-sustaining and highly beneficial and/or related to health. Control condition participants may also deserve the benefits of research once all data has been collected. It is a researcher’s ethical duty to maximize benefits and to ensure that all participants have access to those benefits when possible.

Figure 2. The investigator looks for evidence that the change in the observed behavior occurred coincident with treatment. If the problem behavior declines whenever treatment is introduced (during the first and second treatment phases) but returns (is “reversed”) to baseline levels during the reversal phase, the experimenter can be reasonably confident the treatment had the intended effect.

Link to Learning: Famous Case Studies

Some well-known case studies that related to abnormal psychology include the following:

• Harlow— Phineas Gage
• Breuer & Freud (1895)— Anna O.
• Cleckley’s case studies: on psychopathy ( The Mask of Sanity ) (1941) and multiple personality disorder ( The Three Faces of Eve ) (1957)
• Freud and  Little Hans
• Freud and the  Rat Man
• John Money and the  John/Joan case
• Genie (feral child)
• Piaget’s studies
• Rosenthal’s book on the  murder of Kitty Genovese
• Washoe (sign language)
• Patient H.M.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about handwashing, we have other options available to us.

Suppose we send a researcher to a school playground to observe how aggressive or socially anxious children interact with peers. Will our observer blend into the playground environment by wearing a white lab coat, sitting with a clipboard, and staring at the swings? We want our researcher to be inconspicuous and unobtrusively positioned—perhaps pretending to be a school monitor while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

Figure 3 . In naturalistic observation, psychologists take their research into the streets, homes, restaurants, schools, and other settings where behavior can be directly observed.

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. For example, psychologists have spent weeks observing the behavior of homeless people on the streets, in train stations, and bus terminals. They try to ensure that their naturalistic observations are unobtrusive, so as to minimize interference with the behavior they observe. Nevertheless, the presence of the observer may distort the behavior that is observed, and this must be taken into consideration (Figure 1).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. Although something as simple as observation may seem like it would be a part of all research methods, participant observation is a distinct methodology that involves the researcher embedding themselves into a group in order to study its dynamics. For example, Festinger, Riecken, and Shacter (1956) were very interested in the psychology of a particular cult. However, this cult was very secretive and wouldn’t grant interviews to outside members. So, in order to study these people, Festinger and his colleagues pretended to be cult members, allowing them access to the behavior and psychology of the cult. Despite this example, it should be noted that the people being observed in a participant observation study usually know that the researcher is there to study them. [1]

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

Figure 4 . Surveys can be administered in a number of ways, including electronically administered research, like the survey shown here. (credit: Robert Nyman)

There is both strength and weakness in surveys when compared to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people do not always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the U.S. Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think iT Over

Research has shown that parental depressive symptoms are linked to a number of negative child outcomes. A classmate of yours is interested in  the associations between parental depressive symptoms and actual child behaviors in everyday life [2] because this associations remains largely unknown. After reading this section, what do you think is the best way to better understand such associations? Which method might result in the most valid data?

A-B-A-B design:  an experimental design in which the a person is given treatment, or experimental condition (B), to compare against the baseline (A), and this repeats in order to determine effectiveness

clinical or case study:  observational research study focusing on one or a few people

correlational research:  tests whether a relationship exists between two or more variables

descriptive research:  research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured

experimental research:  tests a hypothesis to determine cause-and-effect relationships

generalizability:  inferring that the results for a sample apply to the larger population

inter-rater reliability:  measure of agreement among observers on how they record and classify a particular event

naturalistic observation:  observation of behavior in its natural setting

observer bias:  when observations may be skewed to align with observer expectations

population:  overall group of individuals that the researchers are interested in

sample:  subset of individuals selected from the larger population

single-case experimental design:   when the same  research participant  serves as the subject in both the experimental and control conditions

survey:  list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

• Scollon, C. N. (2020). Research designs. In R. Biswas-Diener & E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http://noba.to/acxb2thy ↵
• Slatcher, R. B., & Trentacosta, C. J. (2011). A naturalistic observation study of the links between parental depressive symptoms and preschoolers' behaviors in everyday life. Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43), 25(3), 444–448. https://doi.org/10.1037/a0023728 ↵
• Modification and adaptation. Authored by : Sonja Ann Miller for Lumen Learning. Provided by : Lumen Learning. License : CC BY-SA: Attribution-ShareAlike
• Approaches to Research. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:iMyFZJzg@5/Approaches-to-Research . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]
• Descriptive Research. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/researching-psychology-2/types-of-research-studies-27/descriptive-research-124-12659/ . License : CC BY-SA: Attribution-ShareAlike
• Case Study. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Case_study . License : CC BY-SA: Attribution-ShareAlike
• Rat man. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Rat_Man#Legacy . License : CC BY-SA: Attribution-ShareAlike
• Case study in psychology. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Case_study_in_psychology . License : CC BY-SA: Attribution-ShareAlike
• Research Designs. Authored by : Christie Napa Scollon. Provided by : Singapore Management University. Located at : https://nobaproject.com/modules/research-designs#reference-6 . Project : The Noba Project. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Single subject design. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Single-subject_design . License : CC BY-SA: Attribution-ShareAlike
• Single subject research. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Single-subject_research#A-B-A-B . License : Public Domain: No Known Copyright
• Pills. Authored by : qimono. Provided by : Pixabay. Located at : https://pixabay.com/illustrations/pill-capsule-medicine-medical-1884775/ . License : CC0: No Rights Reserved
• ABAB Design. Authored by : Doc. Yu. Provided by : Wikimedia. Located at : https://commons.wikimedia.org/wiki/File:A-B-A-B_Design.png . License : CC BY-SA: Attribution-ShareAlike

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• URL: https://resources.nu.edu/researchtools

Child Care and Early Education Research Connections

Descriptive research studies.

Descriptive research is a type of research that is used to describe the characteristics of a population. It collects data that are used to answer a wide range of what, when, and how questions pertaining to a particular population or group. For example, descriptive studies might be used to answer questions such as: What percentage of Head Start teachers have a bachelor's degree or higher? What is the average reading ability of 5-year-olds when they first enter kindergarten? What kinds of math activities are used in early childhood programs? When do children first receive regular child care from someone other than their parents? When are children with developmental disabilities first diagnosed and when do they first receive services? What factors do programs consider when making decisions about the type of assessments that will be used to assess the skills of the children in their programs? How do the types of services children receive from their early childhood program change as children age?

Descriptive research does not answer questions about why a certain phenomenon occurs or what the causes are. Answers to such questions are best obtained from  randomized and quasi-experimental studies . However, data from descriptive studies can be used to examine the relationships (correlations) among variables. While the findings from correlational analyses are not evidence of causality, they can help to distinguish variables that may be important in explaining a phenomenon from those that are not. Thus, descriptive research is often used to generate hypotheses that should be tested using more rigorous designs.

A variety of data collection methods may be used alone or in combination to answer the types of questions guiding descriptive research. Some of the more common methods include surveys, interviews, observations, case studies, and portfolios. The data collected through these methods can be either quantitative or qualitative. Quantitative data are typically analyzed and presenting using  descriptive statistics . Using quantitative data, researchers may describe the characteristics of a sample or population in terms of percentages (e.g., percentage of population that belong to different racial/ethnic groups, percentage of low-income families that receive different government services) or averages (e.g., average household income, average scores of reading, mathematics and language assessments). Quantitative data, such as narrative data collected as part of a case study, may be used to organize, classify, and used to identify patterns of behaviors, attitudes, and other characteristics of groups.

Descriptive studies have an important role in early care and education research. Studies such as the  National Survey of Early Care and Education  and the  National Household Education Surveys Program  have greatly increased our knowledge of the supply of and demand for child care in the U.S. The  Head Start Family and Child Experiences Survey  and the  Early Childhood Longitudinal Study Program  have provided researchers, policy makers and practitioners with rich information about school readiness skills of children in the U.S.

Each of the methods used to collect descriptive data have their own strengths and limitations. The following are some of the strengths and limitations of descriptive research studies in general.

Study participants are questioned or observed in a natural setting (e.g., their homes, child care or educational settings).

Study data can be used to identify the prevalence of particular problems and the need for new or additional services to address these problems.

Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research."

Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.

Limitations:

Descriptive studies cannot be used to establish cause and effect relationships.

Respondents may not be truthful when answering survey questions or may give socially desirable responses.

The choice and wording of questions on a questionnaire may influence the descriptive findings.

Depending on the type and size of sample, the findings may not be generalizable or produce an accurate description of the population of interest.

• Open access
• Published: 17 April 2024

Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study

• Rawhia Salah Dogham 1 ,
• Heba Fakieh Mansy Ali 1 ,
• Asmaa Saber Ghaly 3 ,
• Nermine M. Elcokany 2 ,
• Mohamed Mahmoud Seweid 4 &
• Ayman Mohamed El-Ashry   ORCID: orcid.org/0000-0001-7718-4942 5  

BMC Nursing volume  23 , Article number:  249 ( 2024 ) Cite this article

49 Accesses

Metrics details

Nursing education presents unique challenges, including high levels of academic stress and varied learning approaches among students. Understanding the relationship between academic stress and learning approaches is crucial for enhancing nursing education effectiveness and student well-being.

This study aimed to investigate the prevalence of academic stress and its correlation with learning approaches among nursing students.

Design and Method

A cross-sectional descriptive correlation research design was employed. A convenient sample of 1010 nursing students participated, completing socio-demographic data, the Perceived Stress Scale (PSS), and the Revised Study Process Questionnaire (R-SPQ-2 F).

Most nursing students experienced moderate academic stress (56.3%) and exhibited moderate levels of deep learning approaches (55.0%). Stress from a lack of professional knowledge and skills negatively correlates with deep learning approaches (r = -0.392) and positively correlates with surface learning approaches (r = 0.365). Female students showed higher deep learning approach scores, while male students exhibited higher surface learning approach scores. Age, gender, educational level, and academic stress significantly influenced learning approaches.

Academic stress significantly impacts learning approaches among nursing students. Strategies addressing stressors and promoting healthy learning approaches are essential for enhancing nursing education and student well-being.

Nursing implication

Understanding academic stress’s impact on nursing students’ learning approaches enables tailored interventions. Recognizing stressors informs strategies for promoting adaptive coping, fostering deep learning, and creating supportive environments. Integrating stress management, mentorship, and counseling enhances student well-being and nursing education quality.

Peer Review reports

Introduction

Nursing education is a demanding field that requires students to acquire extensive knowledge and skills to provide competent and compassionate care. Nursing education curriculum involves high-stress environments that can significantly impact students’ learning approaches and academic performance [ 1 , 2 ]. Numerous studies have investigated learning approaches in nursing education, highlighting the importance of identifying individual students’ preferred approaches. The most studied learning approaches include deep, surface, and strategic approaches. Deep learning approaches involve students actively seeking meaning, making connections, and critically analyzing information. Surface learning approaches focus on memorization and reproducing information without a more profound understanding. Strategic learning approaches aim to achieve high grades by adopting specific strategies, such as memorization techniques or time management skills [ 3 , 4 , 5 ].

Nursing education stands out due to its focus on practical training, where the blend of academic and clinical coursework becomes a significant stressor for students, despite academic stress being shared among all university students [ 6 , 7 , 8 ]. Consequently, nursing students are recognized as prone to high-stress levels. Stress is the physiological and psychological response that occurs when a biological control system identifies a deviation between the desired (target) state and the actual state of a fitness-critical variable, whether that discrepancy arises internally or externally to the human [ 9 ]. Stress levels can vary from objective threats to subjective appraisals, making it a highly personalized response to circumstances. Failure to manage these demands leads to stress imbalance [ 10 ].

Nursing students face three primary stressors during their education: academic, clinical, and personal/social stress. Academic stress is caused by the fear of failure in exams, assessments, and training, as well as workload concerns [ 11 ]. Clinical stress, on the other hand, arises from work-related difficulties such as coping with death, fear of failure, and interpersonal dynamics within the organization. Personal and social stressors are caused by an imbalance between home and school, financial hardships, and other factors. Throughout their education, nursing students have to deal with heavy workloads, time constraints, clinical placements, and high academic expectations. Multiple studies have shown that nursing students experience higher stress levels compared to students in other fields [ 12 , 13 , 14 ].

Research has examined the relationship between academic stress and coping strategies among nursing students, but no studies focus specifically on the learning approach and academic stress. However, existing literature suggests that students interested in nursing tend to experience lower levels of academic stress [ 7 ]. Therefore, interest in nursing can lead to deep learning approaches, which promote a comprehensive understanding of the subject matter, allowing students to feel more confident and less overwhelmed by coursework and exams. Conversely, students employing surface learning approaches may experience higher stress levels due to the reliance on memorization [ 3 ].

Understanding the interplay between academic stress and learning approaches among nursing students is essential for designing effective educational interventions. Nursing educators can foster deep learning approaches by incorporating active learning strategies, critical thinking exercises, and reflection activities into the curriculum [ 15 ]. Creating supportive learning environments encouraging collaboration, self-care, and stress management techniques can help alleviate academic stress. Additionally, providing mentorship and counselling services tailored to nursing students’ unique challenges can contribute to their overall well-being and academic success [ 16 , 17 , 18 ].

Despite the scarcity of research focusing on the link between academic stress and learning methods in nursing students, it’s crucial to identify the unique stressors they encounter. The intensity of these stressors can be connected to the learning strategies employed by these students. Academic stress and learning approach are intertwined aspects of the student experience. While academic stress can influence learning approaches, the choice of learning approach can also impact the level of academic stress experienced. By understanding this relationship and implementing strategies to promote healthy learning approaches and manage academic stress, educators and institutions can foster an environment conducive to deep learning and student well-being.

Hence, this study aims to investigate the correlation between academic stress and learning approaches experienced by nursing students.

Study objectives

Assess the levels of academic stress among nursing students.

Assess the learning approaches among nursing students.

Identify the relationship between academic stress and learning approach among nursing students.

Identify the effect of academic stress and related factors on learning approach and among nursing students.

Materials and methods

Research design.

A cross-sectional descriptive correlation research design adhering to the STROBE guidelines was used for this study.

A research project was conducted at Alexandria Nursing College, situated in Egypt. The college adheres to the national standards for nursing education and functions under the jurisdiction of the Egyptian Ministry of Higher Education. Alexandria Nursing College comprises nine specialized nursing departments that offer various nursing specializations. These departments include Nursing Administration, Community Health Nursing, Gerontological Nursing, Medical-Surgical Nursing, Critical Care Nursing, Pediatric Nursing, Obstetric and Gynecological Nursing, Nursing Education, and Psychiatric Nursing and Mental Health. The credit hour system is the fundamental basis of both undergraduate and graduate programs. This framework guarantees a thorough evaluation of academic outcomes by providing an organized structure for tracking academic progress and conducting analyses.

Participants and sample size calculation

The researchers used the Epi Info 7 program to calculate the sample size. The calculations were based on specific parameters such as a population size of 9886 students for the academic year 2022–2023, an expected frequency of 50%, a maximum margin of error of 5%, and a confidence coefficient of 99.9%. Based on these parameters, the program indicated that a minimum sample size of 976 students was required. As a result, the researchers recruited a convenient sample of 1010 nursing students from different academic levels during the 2022–2023 academic year [ 19 ]. This sample size was larger than the minimum required, which could help to increase the accuracy and reliability of the study results. Participation in the study required enrollment in a nursing program and voluntary agreement to take part. The exclusion criteria included individuals with mental illnesses based on their response and those who failed to complete the questionnaires.

socio-demographic data that include students’ age, sex, educational level, hours of sleep at night, hours spent studying, and GPA from the previous semester.

Tool two: the perceived stress scale (PSS)

It was initially created by Sheu et al. (1997) to gauge the level and nature of stress perceived by nursing students attending Taiwanese universities [ 20 ]. It comprises 29 items rated on a 5-point Likert scale, where (0 = never, 1 = rarely, 2 = sometimes, 3 = reasonably often, and 4 = very often), with a total score ranging from 0 to 116. The cut-off points of levels of perceived stress scale according to score percentage were low < 33.33%, moderate 33.33–66.66%, and high more than 66.66%. Higher scores indicate higher stress levels. The items are categorized into six subscales reflecting different sources of stress. The first subscale assesses “stress stemming from lack of professional knowledge and skills” and includes 3 items. The second subscale evaluates “stress from caring for patients” with 8 items. The third subscale measures “stress from assignments and workload” with 5 items. The fourth subscale focuses on “stress from interactions with teachers and nursing staff” with 6 items. The fifth subscale gauges “stress from the clinical environment” with 3 items. The sixth subscale addresses “stress from peers and daily life” with 4 items. El-Ashry et al. (2022) reported an excellent internal consistency reliability of 0.83 [ 21 ]. Two bilingual translators translated the English version of the scale into Arabic and then back-translated it into English by two other independent translators to verify its accuracy. The suitability of the translated version was confirmed through a confirmatory factor analysis (CFA), which yielded goodness-of-fit indices such as a comparative fit index (CFI) of 0.712, a Tucker-Lewis index (TLI) of 0.812, and a root mean square error of approximation (RMSEA) of 0.100.

Tool three: revised study process questionnaire (R-SPQ-2 F)

It was developed by Biggs et al. (2001). It examines deep and surface learning approaches using only 20 questions; each subscale contains 10 questions [ 22 ]. On a 5-point Likert scale ranging from 0 (never or only rarely true of me) to 4 (always or almost always accurate of me). The total score ranged from 0 to 80, with a higher score reflecting more deep or surface learning approaches. The cut-off points of levels of revised study process questionnaire according to score percentage were low < 33%, moderate 33–66%, and high more than 66%. Biggs et al. (2001) found that Cronbach alpha value was 0.73 for deep learning approach and 0.64 for the surface learning approach, which was considered acceptable. Two translators fluent in English and Arabic initially translated a scale from English to Arabic. To ensure the accuracy of the translation, they translated it back into English. The translated version’s appropriateness was evaluated using a confirmatory factor analysis (CFA). The CFA produced several goodness-of-fit indices, including a Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100. Comparative Fit Index (CFI) of 0.790, a Tucker-Lewis Index (TLI) of 0.912, and a Root Mean Square Error of Approximation (RMSEA) of 0.100.

Ethical considerations

The Alexandria University College of Nursing’s Research Ethics Committee provided ethical permission before the study’s implementation. Furthermore, pertinent authorities acquired ethical approval at participating nursing institutions. The vice deans of the participating institutions provided written informed consent attesting to institutional support and authority. By giving written informed consent, participants confirmed they were taking part voluntarily. Strict protocols were followed to protect participants’ privacy during the whole investigation. The obtained personal data was kept private and available only to the study team. Ensuring participants’ privacy and anonymity was of utmost importance.

Tools validity

The researchers created tool one after reviewing pertinent literature. Two bilingual translators independently translated the English version into Arabic to evaluate the applicability of the academic stress and learning approach tools for Arabic-speaking populations. To assure accuracy, two additional impartial translators back-translated the translation into English. They were also assessed by a five-person jury of professionals from the education and psychiatric nursing departments. The scales were found to have sufficiently evaluated the intended structures by the jury.

Pilot study

A preliminary investigation involved 100 nursing student applicants, distinct from the final sample, to gauge the efficacy, clarity, and potential obstacles in utilizing the research instruments. The pilot findings indicated that the instruments were accurate, comprehensible, and suitable for the target demographic. Additionally, Cronbach’s Alpha was utilized to further assess the instruments’ reliability, demonstrating internal solid consistency for both the learning approaches and academic stress tools, with values of 0.91 and 0.85, respectively.

Data collection

The researchers convened with each qualified student in a relaxed, unoccupied classroom in their respective college settings. Following a briefing on the study’s objectives, the students filled out the datasheet. The interviews typically lasted 15 to 20 min.

Data analysis

The data collected were analyzed using IBM SPSS software version 26.0. Following data entry, a thorough examination and verification were undertaken to ensure accuracy. The normality of quantitative data distributions was assessed using Kolmogorov-Smirnov tests. Cronbach’s Alpha was employed to evaluate the reliability and internal consistency of the study instruments. Descriptive statistics, including means (M), standard deviations (SD), and frequencies/percentages, were computed to summarize academic stress and learning approaches for categorical data. Student’s t-tests compared scores between two groups for normally distributed variables, while One-way ANOVA compared scores across more than two categories of a categorical variable. Pearson’s correlation coefficient determined the strength and direction of associations between customarily distributed quantitative variables. Hierarchical regression analysis identified the primary independent factors influencing learning approaches. Statistical significance was determined at the 5% (p < 0.05).

Table  1 presents socio-demographic data for a group of 1010 nursing students. The age distribution shows that 38.8% of the students were between 18 and 21 years old, 32.9% were between 21 and 24 years old, and 28.3% were between 24 and 28 years old, with an average age of approximately 22.79. Regarding gender, most of the students were female (77%), while 23% were male. The students were distributed across different educational years, a majority of 34.4% in the second year, followed by 29.4% in the fourth year. The students’ hours spent studying were found to be approximately two-thirds (67%) of the students who studied between 3 and 6 h. Similarly, sleep patterns differ among the students; more than three-quarters (77.3%) of students sleep between 5- to more than 7 h, and only 2.4% sleep less than 2 h per night. Finally, the student’s Grade Point Average (GPA) from the previous semester was also provided. 21% of the students had a GPA between 2 and 2.5, 40.9% had a GPA between 2.5 and 3, and 38.1% had a GPA between 3 and 3.5.

Figure  1 provides the learning approach level among nursing students. In terms of learning approach, most students (55.0%) exhibited a moderate level of deep learning approach, followed by 25.9% with a high level and 19.1% with a low level. The surface learning approach was more prevalent, with 47.8% of students showing a moderate level, 41.7% showing a low level, and only 10.5% exhibiting a high level.

Nursing students? levels of learning approach (N=1010)

Figure  2 provides the types of academic stress levels among nursing students. Among nursing students, various stressors significantly impact their academic experiences. Foremost among these stressors are the pressure and demands associated with academic assignments and workload, with 30.8% of students attributing their high stress levels to these factors. Challenges within the clinical environment are closely behind, contributing significantly to high stress levels among 25.7% of nursing students. Interactions with peers and daily life stressors also weigh heavily on students, ranking third among sources of high stress, with 21.5% of students citing this as a significant factor. Similarly, interaction with teachers and nursing staff closely follow, contributing to high-stress levels for 20.3% of nursing students. While still significant, stress from taking care of patients ranks slightly lower, with 16.7% of students reporting it as a significant factor contributing to their academic stress. At the lowest end of the ranking, but still notable, is stress from a perceived lack of professional knowledge and skills, with 15.9% of students experiencing high stress in this area.

Nursing students? levels of academic stress subtypes (N=1010)

Figure  3 provides the total levels of academic stress among nursing students. The majority of students experienced moderate academic stress (56.3%), followed by those experiencing low academic stress (29.9%), and a minority experienced high academic stress (13.8%).

Nursing students? levels of total academic stress (N=1010)

Table  2 displays the correlation between academic stress subscales and deep and surface learning approaches among 1010 nursing students. All stress subscales exhibited a negative correlation regarding the deep learning approach, indicating that the inclination toward deep learning decreases with increasing stress levels. The most significant negative correlation was observed with stress stemming from the lack of professional knowledge and skills (r=-0.392, p < 0.001), followed by stress from the clinical environment (r=-0.109, p = 0.001), stress from assignments and workload (r=-0.103, p = 0.001), stress from peers and daily life (r=-0.095, p = 0.002), and stress from patient care responsibilities (r=-0.093, p = 0.003). The weakest negative correlation was found with stress from interactions with teachers and nursing staff (r=-0.083, p = 0.009). Conversely, concerning the surface learning approach, all stress subscales displayed a positive correlation, indicating that heightened stress levels corresponded with an increased tendency toward superficial learning. The most substantial positive correlation was observed with stress related to the lack of professional knowledge and skills (r = 0.365, p < 0.001), followed by stress from patient care responsibilities (r = 0.334, p < 0.001), overall stress (r = 0.355, p < 0.001), stress from interactions with teachers and nursing staff (r = 0.262, p < 0.001), stress from assignments and workload (r = 0.262, p < 0.001), and stress from the clinical environment (r = 0.254, p < 0.001). The weakest positive correlation was noted with stress stemming from peers and daily life (r = 0.186, p < 0.001).

Table  3 outlines the association between the socio-demographic characteristics of nursing students and their deep and surface learning approaches. Concerning age, statistically significant differences were observed in deep and surface learning approaches (F = 3.661, p = 0.003 and F = 7.983, p < 0.001, respectively). Gender also demonstrated significant differences in deep and surface learning approaches (t = 3.290, p = 0.001 and t = 8.638, p < 0.001, respectively). Female students exhibited higher scores in the deep learning approach (31.59 ± 8.28) compared to male students (29.59 ± 7.73), while male students had higher scores in the surface learning approach (29.97 ± 7.36) compared to female students (24.90 ± 7.97). Educational level exhibited statistically significant differences in deep and surface learning approaches (F = 5.599, p = 0.001 and F = 17.284, p < 0.001, respectively). Both deep and surface learning approach scores increased with higher educational levels. The duration of study hours demonstrated significant differences only in the surface learning approach (F = 3.550, p = 0.014), with scores increasing as study hours increased. However, no significant difference was observed in the deep learning approach (F = 0.861, p = 0.461). Hours of sleep per night and GPA from the previous semester did not exhibit statistically significant differences in deep or surface learning approaches.

Table  4 presents a multivariate linear regression analysis examining the factors influencing the learning approach among 1110 nursing students. The deep learning approach was positively influenced by age, gender (being female), educational year level, and stress from teachers and nursing staff, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by stress from a lack of professional knowledge and skills. The other factors do not significantly influence the deep learning approach. On the other hand, the surface learning approach was positively influenced by gender (being female), educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, as indicated by their positive coefficients and significant p-values (p < 0.05). However, it was negatively influenced by gender (being male). The other factors do not significantly influence the surface learning approach. The adjusted R-squared values indicated that the variables in the model explain 17.8% of the variance in the deep learning approach and 25.5% in the surface learning approach. Both models were statistically significant (p < 0.001).

Nursing students’ academic stress and learning approaches are essential to planning for effective and efficient learning. Nursing education also aims to develop knowledgeable and competent students with problem-solving and critical-thinking skills.

The study’s findings highlight the significant presence of stress among nursing students, with a majority experiencing moderate to severe levels of academic stress. This aligns with previous research indicating that academic stress is prevalent among nursing students. For instance, Zheng et al. (2022) observed moderated stress levels in nursing students during clinical placements [ 23 ], while El-Ashry et al. (2022) found that nearly all first-year nursing students in Egypt experienced severe academic stress [ 21 ]. Conversely, Ali and El-Sherbini (2018) reported that over three-quarters of nursing students faced high academic stress. The complexity of the nursing program likely contributes to these stress levels [ 24 ].

The current study revealed that nursing students identified the highest sources of academic stress as workload from assignments and the stress of caring for patients. This aligns with Banu et al.‘s (2015) findings, where academic demands, assignments, examinations, high workload, and combining clinical work with patient interaction were cited as everyday stressors [ 25 ]. Additionally, Anaman-Torgbor et al. (2021) identified lectures, assignments, and examinations as predictors of academic stress through logistic regression analysis. These stressors may stem from nursing programs emphasizing the development of highly qualified graduates who acquire knowledge, values, and skills through classroom and clinical experiences [ 26 ].

The results regarding learning approaches indicate that most nursing students predominantly employed the deep learning approach. Despite acknowledging a surface learning approach among the participants in the present study, the prevalence of deep learning was higher. This inclination toward the deep learning approach is anticipated in nursing students due to their engagement with advanced courses, requiring retention, integration, and transfer of information at elevated levels. The deep learning approach correlates with a gratifying learning experience and contributes to higher academic achievements [ 3 ]. Moreover, the nursing program’s emphasis on active learning strategies fosters critical thinking, problem-solving, and decision-making skills. These findings align with Mahmoud et al.‘s (2019) study, reporting a significant presence (83.31%) of the deep learning approach among undergraduate nursing students at King Khalid University’s Faculty of Nursing [ 27 ]. Additionally, Mohamed &Morsi (2019) found that most nursing students at Benha University’s Faculty of Nursing embraced the deep learning approach (65.4%) compared to the surface learning approach [ 28 ].

The study observed a negative correlation between the deep learning approach and the overall mean stress score, contrasting with a positive correlation between surface learning approaches and overall stress levels. Elevated academic stress levels may diminish motivation and engagement in the learning process, potentially leading students to feel overwhelmed, disinterested, or burned out, prompting a shift toward a surface learning approach. This finding resonates with previous research indicating that nursing students who actively seek positive academic support strategies during academic stress have better prospects for success than those who do not [ 29 ]. Nebhinani et al. (2020) identified interface concerns and academic workload as significant stress-related factors. Notably, only an interest in nursing demonstrated a significant association with stress levels, with participants interested in nursing primarily employing adaptive coping strategies compared to non-interested students.

The current research reveals a statistically significant inverse relationship between different dimensions of academic stress and adopting the deep learning approach. The most substantial negative correlation was observed with stress arising from a lack of professional knowledge and skills, succeeded by stress associated with the clinical environment, assignments, and workload. Nursing students encounter diverse stressors, including delivering patient care, handling assignments and workloads, navigating challenging interactions with staff and faculty, perceived inadequacies in clinical proficiency, and facing examinations [ 30 ].

In the current study, the multivariate linear regression analysis reveals that various factors positively influence the deep learning approach, including age, female gender, educational year level, and stress from teachers and nursing staff. In contrast, stress from a lack of professional knowledge and skills exert a negative influence. Conversely, the surface learning approach is positively influenced by female gender, educational year level, stress from lack of professional knowledge and skills, stress from assignments and workload, and stress from taking care of patients, but negatively affected by male gender. The models explain 17.8% and 25.5% of the variance in the deep and surface learning approaches, respectively, and both are statistically significant. These findings underscore the intricate interplay of demographic and stress-related factors in shaping nursing students’ learning approaches. High workloads and patient care responsibilities may compel students to prioritize completing tasks over deep comprehension. This pressure could lead to a surface learning approach as students focus on meeting immediate demands rather than engaging deeply with course material. This observation aligns with the findings of Alsayed et al. (2021), who identified age, gender, and study year as significant factors influencing students’ learning approaches.

Deep learners often demonstrate better self-regulation skills, such as effective time management, goal setting, and seeking support when needed. These skills can help manage academic stress and maintain a balanced learning approach. These are supported by studies that studied the effect of coping strategies on stress levels [ 6 , 31 , 32 ]. On the contrary, Pacheco-Castillo et al. study (2021) found a strong significant relationship between academic stressors and students’ level of performance. That study also proved that the more academic stress a student faces, the lower their academic achievement.

Strengths and limitations of the study

This study has lots of advantages. It provides insightful information about the educational experiences of Egyptian nursing students, a demographic that has yet to receive much research. The study’s limited generalizability to other people or nations stems from its concentration on this particular group. This might be addressed in future studies by using a more varied sample. Another drawback is the dependence on self-reported metrics, which may contain biases and mistakes. Although the cross-sectional design offers a moment-in-time view of the problem, it cannot determine causation or evaluate changes over time. To address this, longitudinal research may be carried out.

Notwithstanding these drawbacks, the study substantially contributes to the expanding knowledge of academic stress and nursing students’ learning styles. Additional research is needed to determine teaching strategies that improve deep-learning approaches among nursing students. A qualitative study is required to analyze learning approaches and factors that may influence nursing students’ selection of learning approaches.

According to the present study’s findings, nursing students encounter considerable academic stress, primarily stemming from heavy assignments and workload, as well as interactions with teachers and nursing staff. Additionally, it was observed that students who experience lower levels of academic stress typically adopt a deep learning approach, whereas those facing higher stress levels tend to resort to a surface learning approach. Demographic factors such as age, gender, and educational level influence nursing students’ choice of learning approach. Specifically, female students are more inclined towards deep learning, whereas male students prefer surface learning. Moreover, deep and surface learning approach scores show an upward trend with increasing educational levels and study hours. Academic stress emerges as a significant determinant shaping the adoption of learning approaches among nursing students.

Implications in nursing practice

Nursing programs should consider integrating stress management techniques into their curriculum. Providing students with resources and skills to cope with academic stress can improve their well-being and academic performance. Educators can incorporate teaching strategies that promote deep learning approaches, such as problem-based learning, critical thinking exercises, and active learning methods. These approaches help students engage more deeply with course material and reduce reliance on surface learning techniques. Recognizing the gender differences in learning approaches, nursing programs can offer gender-specific support services and resources. For example, providing targeted workshops or counseling services that address male and female nursing students’ unique stressors and learning needs. Implementing mentorship programs and peer support groups can create a supportive environment where students can share experiences, seek advice, and receive encouragement from their peers and faculty members. Encouraging students to reflect on their learning processes and identify effective study strategies can help them develop metacognitive skills and become more self-directed learners. Faculty members can facilitate this process by incorporating reflective exercises into the curriculum. Nursing faculty and staff should receive training on recognizing signs of academic stress among students and providing appropriate support and resources. Additionally, professional development opportunities can help educators stay updated on evidence-based teaching strategies and practical interventions for addressing student stress.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to restrictions imposed by the institutional review board to protect participant confidentiality, but are available from the corresponding author on reasonable request.

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Acknowledgements

Our sincere thanks go to all the nursing students in the study. We also want to thank Dr/ Rasha Badry for their statistical analysis help and contribution to this study.

The research was not funded by public, commercial, or non-profit organizations.

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

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Ayman M. El-Ashry & Rawhia S. Dogham: conceptualization, preparation, and data collection; methodology; investigation; formal analysis; data analysis; writing-original draft; writing-manuscript; and editing. Heba F. Mansy Ali & Asmaa S. Ghaly: conceptualization, preparation, methodology, investigation, writing-original draft, writing-review, and editing. Nermine M. Elcokany & Mohamed M. Seweid: Methodology, investigation, formal analysis, data collection, writing-manuscript & editing. All authors reviewed the manuscript and accept for publication.

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Dogham, R.S., Ali, H.F.M., Ghaly, A.S. et al. Deciphering the influence: academic stress and its role in shaping learning approaches among nursing students: a cross-sectional study. BMC Nurs 23 , 249 (2024). https://doi.org/10.1186/s12912-024-01885-1

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ORIGINAL RESEARCH article

Identification of factors associated with hospitalization in an outpatient population with mental health conditions: a case–control study.

• 1 Pôle Centre Rive Gauche, CH Le Vinatier, Bron, France
• 2 Université Claude Bernard Lyon 1, Villeurbanne, France
• 3 Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
• 4 UMR 5992 CNRS, U1028 INSERM, Centre de Recherche en Neurosciences de Lyon, Bron, France
• 5 Hospices Civils de Lyon, Lyon, France
• 6 UMR 5229 CNRS, Centre Ressource de Réhabilitation psychosociale, Le Vinatier, Bron, France

Introduction: Addressing relevant determinants for preserved person-centered rehabilitation in mental health is still a major challenge. Little research focuses on factors associated with psychiatric hospitalization in exclusive outpatient settings. Some variables have been identified, but evidence across studies is inconsistent. This study aimed to identify and confirm factors associated with hospitalization in a specific outpatient population.

Methods: A retrospective monocentric case-control study with 617 adult outpatients (216 cases and 401 controls) from a French community-based care facility was conducted. Participants had an index outpatient consultation between June 2021 and February 2023. All cases, who were patients with a psychiatric hospitalization from the day after the index outpatient consultation and up to 1 year later, have been included. Controls have been randomly selected from the same facility and did not experience a psychiatric hospitalization in the 12 months following the index outpatient consultation. Data collection was performed from electronic medical records. Sociodemographic, psychiatric diagnosis, historical issues, lifestyle, and follow-up-related variables were collected retrospectively. Uni- and bivariate analyses were performed, followed by a multivariable logistic regression.

Results: Visit to a psychiatric emergency within a year (adjusted odds ratio (aOR): 13.02, 95% confidence interval (CI): 7.32–23.97), drug treatment discontinuation within a year (aOR: 6.43, 95% CI: 3.52–12.03), history of mental healthcare without consent (aOR: 5.48, 95% CI: 3.10–10.06), medical follow-up discontinuation within a year (aOR: 3.17, 95% CI: 1.70–5.95), history of attempted suicide (aOR: 2.50, 95% CI: 1.48–4.30) and unskilled job (aOR: 0.26, 95% CI: 0.10–0.65) are the independent variables found associated with hospitalization for followed up outpatients.

Conclusions: Public health policies and tools at the local and national levels should be adapted to target the identified individual determinants in order to prevent outpatients from being hospitalized.

1 Introduction

The deinstitutionalization process in psychiatry began in the late twentieth century. This shift, especially seen in high-income countries, consists of a decrease in specialized psychiatric hospital beds for an increase of patients with a mental health condition, followed up in general medical hospitals, community-based care, and various outpatient settings ( 1 ). Between the mid-twentieth century and the 1990s, the number of psychiatric beds dropped to more than 80% in most western regions around the world ( 1 ).

However, the transition from an inpatient setting paradigm to an outpatient one needs to be carefully organized, with the necessary and appropriate structures and funding. Indeed, patients who suffer from a mental health disease need a deep consideration of the multifaceted world in which they live, to integrate and adapt their rehabilitation process for the outside world. The strengthening of community services has been heterogenous around the world ( 1 ). This deinstitutionalization failed, for example, in many places in the USA, leading to an increase in homelessness and crime among people with psychiatric diseases in the 1990s ( 2 ). More recently, there are still concerns about the good transitioning process that have been raised in central and eastern Europe, with a large body of evidence showing failures in deinstitutionalization and reinstitutionalization outcomes. Some of the causes found are lack of personal assistance, development and adaptation of social housing, and cuts to social support ( 3 ). The limited scaling up of community-based and primary care mental health services has also been identified as a failure factor of deinstitutionalization, along with fundamental concerns with the model. A deeper work on addressing social determinants is indeed also evoked, which are known to be fundamental structural drivers of mental illness ( 1 ). A relatively recent dramatic event that has to be remembered regarding the deinstitutionalization failure has been the “Life Esidimeni scandal” in 2016 in South Africa. Qualified as a humanitarian crisis, this event caused the deaths of a thousand psychiatric patients (94 according to an official report issued in 2017 ( 4 )) following their transfer from an inpatient setting to multiple outpatient settings without the appropriate care and follow-up required. Indeed, the cut in this 2,000-bed facility budget led to patients’ discharge regardless of individual autonomy and psychosocial disability into inadequately resourced nongovernmental facilities ( 5 ).

Deinstitutionalization requires strong, continuous efforts and should always stay person-centered. In this approach, the multidisciplinary team caring for the patient must bear in mind the individual factors that can predict the maintained recovery of the patient in the outpatient setting ( 6 ). Few settings succeed yet to address all structural determinants, even in high-income countries ( 1 ). Indeed, the Lancet Commission on Global Mental Health and Sustainable Development reminded us that regarding mental health, all countries are “developing” due to the relative underfunding of mental health services in relation to the burden of the condition ( 7 ). Ways to achieve success with deinstitutionalization may involve legislation with a mandate to establish community-based services (like in Italy ( 8 )) and to adapt them to a local context. Improvements will probably require a multitude of paradigm shifts within these structures, considering factors enabling their enhancement. If no adequate care is provided during deinstitutionalization or after it, patients may relapse after being discharged from the hospital and consequently readmitted. Many studies therefore considered readmission rate to be an indicator for intervention studies ( 1 ) and to identify protective and risk factors of relapses ( 9 ) ( 10 , 11 ).

A rich scientific literature is available on the study of risk factors of hospitalization in patients suffering from mental health pathologies. Nonexhaustively, for depression ( 12 ), the type of illness diagnosis, psychiatric comorbidity, treatment-related factors, and sociodemographic factors were associated with hospitalization. For bipolar disorders ( 13 ), characteristics of the index hospitalization (transfer, discharge disposition, length of stay), all-cause acute health service utilization in the year prior to it, and comorbidity were identified. For schizophrenia ( 14 , 15 ), recent medical follow-up discontinuation, medication nonadherence, life events, comorbidity, sex, age, and medication type were variables associated with hospitalization. Finally, for other psychiatric conditions ( 16 ) ( 9 , 10 ) ( 17 ) ( 11 ) ( 18 ), factors associated with hospitalization were shown to be recent medical follow-up discontinuation, multiple psychiatric hospitalization history, history of mental healthcare without consent, social isolation, socioeconomic status, violence history, psychiatric diagnosis, and patient’s satisfaction with treatment. A suicide attempt was found to be a risk factor for hospitalization in some studies and a protective factor at 1 year in others.

Nonetheless, the studies cited above only evaluate risk factors for readmission, i.e., for patients that are originally coming from an inpatient hospital setting. Literature focusing on an exclusive outpatient setting is scarce ( 19 , 20 ). It confirmed some previously identified risk factors in studies with an inpatient setting, such as alcohol/substance use, family history of mental health disease, and marital status, but have also diverging results for negative attitude/poor compliance with medication, identified by Antonio Ciudad et al. ( 20 ) as lowering the hazard of relapse during outpatient follow-up.

A systematic review of the literature carried out by Donisi et al. ( 11 ) additionally underlined some inhomogeneous results for identified risk factors associated with readmissions regarding sociodemographic variables, and a literature weakness for social support, considered only in a few papers. Furthermore, the authors emphasized that some factors were only identified in uni- or bivariate analyses and not in multiple regression.

More people are followed up in outpatient settings, and the minimal use of hospitalization remains a challenge in mental health. This study is of interest to mental health professionals and policymakers because more data on factors associated with hospitalization in followed up outpatients could help tailor appropriate follow-up care and adapt existing tools to reduce the need for hospitalization. Our study, therefore, aimed to identify and confirm risk factors of hospitalization in a specific outpatient population.

2.1 Study design

We conducted an observational, retrospective, monocentric case-control study based on hospitalization in one of the largest university-affiliated public psychiatric hospitals in France, with around 500 beds and 26,500 patients followed up on an outpatient basis, the Centre Hospitalier le Vinatier (CHV) in Bron. The CHV has several community-based care facilities called “Centre Médico-Psychologique” or “CMP”, providing medical–psychological and social consultations to anyone experiencing psychological difficulties. The present study was made in one of them. We reported this case-control study according to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). For details, see Supplementary File 1 .

This retrospective study investigated the data from patients followed up in an outpatient setting from June 2021 to February 2023. This study period has been chosen in order not to have repercussions of the health restrictions due to the COVID-19 pandemic on our variables. The studied sample comes from the Centre Médico-Psychologique Centre Rive Gauche facility, administratively attached to the CHV but which has an independent operation for outpatients requiring mental healthcare in a defined geographic area (third, sixth, and eighth districts of Lyon).

In this facility, participants were eligible if they were aged 18 or older and had at least one outpatient psychiatric medical consultation between June 2021 and February 2023 (defined as the index consultation).

The sample size for this study was determined considering an odds ratio of 1.5 to 3 clinically meaningful based on previous literature. With a significance level of 0.05, a type I error of 0.025, and a power of 0.9, the required sample size was calculated using R and its Epicalc package 2.9.0.1. An estimate was then made with the lowest and highest expected frequencies for the studied variables. An ideal sample size was calculated and ranged between 807 and 423, with an approximate 1:2 case/control ratio.

2.3 Outcome

The studied outcome was full psychiatric hospitalization from the day after the index outpatient consultation and up to 1 year later. Full psychiatric hospitalization was defined in this study as more than 24 h of hospitalization in a psychiatric hospital. Thus, participants who had this outcome of interest were referred to as cases, whereas others who did not have the outcome of interest were referred to as controls.

2.4 Selection of cases and controls

Cases were patients who had a full psychiatric hospitalization from the day after the index outpatient consultation and up to 1 year later.

Controls were patients who did not experience full psychiatric hospitalization in the 12 months following the index outpatient consultation (therefore, controls have an index outpatient consultation before February 2022 to have at least a 1-year psychiatric hospitalization-free period).

All cases in the sample responding to the case definition were included ( n = 216).

Controls ( n = 401) were then randomly selected from the sample list of patients who met the definition of controls in order to approximately respect a 1:2 case/control ratio and the sample size determination. The random selection was performed with simple random sampling using computer-generated random numbers to ensure an unbiased selection process.

All the detailed characteristics of cases and controls can be found in Table 1 .

Table 1 Descriptive analysis.

2.5 Variables

The following exposure or potential confounder variables were collected retrospectively from patients’ medical records (collected in a binary yes/no format for qualitative variables):

I. Sociodemographic variables: age (in years, quantitative variable), gender, birth in France, unemployed (including patients on sick leave but not retired patients), unskilled worker (i.e., job accessible without special qualifications, only job category collected), homeless, partner of life (in a relationship).

II. Main psychiatric diagnosis (1 only), according to the ICD-11: depressive disorders, schizophrenia or other primary psychotic disorders, bipolar or related disorders, anxiety or fear-related disorders, neurodevelopmental disorders, another psychiatric diagnosis (other diagnosis belonging to the ICD-11 category 6: mental, behavioral, or neurodevelopmental disorders).

III. Psychiatric comorbidity: the presence of a psychiatric comorbidity (in addition to the main diagnosis, the presence of another psychiatric disorder falling under category 6 of the ICD-11).

IV. Historical issues and lifestyle: traumatic history (exhaustively: rape and/or sexual assault and/or loss of first-degree relative before the patient’s age of 18 and/or torture and/or major physical assault and/or loss of a child by suicide and/or violent death of a first-degree relative in front of the patient and/or patient placed in foster care during childhood, and/or direct witness to a homicide), history of mental healthcare without consent (medical treatment undertaken without the consent of the patient being treated, as permitted by law), multiple psychiatric hospitalization history (> 5 full psychiatric hospitalization), alcohol abuse within the year (diagnosed by the psychiatrist as pathologic, and corresponding to the ICD-11 codes 6C40.0, 6C40.1, 6C40.20, 6C40.21, and 6C40.3), illicit drug abuse within the year (regular consumption of an illicit substance greater than 1/week), family history of mental health disease (known psychiatric disorder within the patient’s biological family), history of attempted suicide, and drug side effect reported within the year (presence of a side effect documented on the patient’s medical record).

V. Follow-up-related variables: visit to psychiatric emergency within the year (excluding the one that led to full psychiatric hospitalization of the case definition), drug treatment discontinuation within the year (discontinuation by the patient, without medical agreement, of a psychiatric background treatment regimen over a period of more than 1 week), medical follow-up discontinuation within the year, additional support within the year (follow-up by a psychiatrist at least twice a year and/or regular follow-up by a medical mobile team (> 1/trimester) and/or included in a psychoeducation care program with a total hourly volume > 15 h/year), and time since first admission to the psychiatric hospital in outpatient or inpatient setting (in years, quantitative variable).

The term “within the year” refers to the variable being present 12 months prior to hospitalization for cases or 12 months following the index consultation for controls.

These variables were chosen because they have already been identified in the literature as factors associated with psychiatric hospitalization or suggested to be potential risk factors or confounders.

We hypothesized that all variables might be potential confounders and were indiscriminately tested to include them in the regression model (see Section 2.6) and to control for potential confounders.

2.6 Analysis

Statistical analysis was conducted using R software version 4.2.1 (23 June 2022) (R Core Team, 2022). Collected variables in case and control groups have been compared using a bivariate analysis ( Table 1 ). For quantitative variables, the Student’s t -test was used. For qualitative variables (dichotomous variables collected in a yes/no format), a Chi-square ( χ 2 ) test was performed.

Multivariable logistic regression was used to study the relationship between the outcome and the assessed covariables (listed in Section 2.2) with adjusted odds ratios (aORs) and 95% confidence intervals (CIs). In the analysis and to interpret its results, control group variables were considered baseline/reference category and case variables were compared to them. Based on the significant factors identified in the univariate analysis, variables were added to the model when p < 0.10. The model was built using a forward, stepwise selection procedure. It involves iteratively adding variables to the model one at a time, based on their individual contribution to improving the model’s fit. The fitness of the models was compared with a likelihood-ratio test. The choice was made to work on a subset of patients without missing data (complete case analysis). Interactions between variables included in the model were tested. They were considered when they appeared significant ( p -value < 0.01 to avoid multiple testing problems) and had an interpretable clinical meaning. The multiple logistic regression model was adjusted for all the risk factor variables included in the full model ( Table 2 ). The data normality of residuals for this multiple logistic regression was assessed by the Shapiro–Wilk normality test.

Table 2 Univariate analysis and results of a multiple logistic regression model predicting psychiatric hospital admission of outpatients (on a no missing values dataset, n = 521).

2.7 Data collection and ethical approval

Data were retrieved from the CHV’s electronic medical record system by reading through each medical record one by one. It was collected anonymously and entered directly into a secure document to ensure the confidentiality and privacy of participants. Personal identifying information such as names, addresses, and contact details were not recorded. Instead, each participant was assigned a unique identification code, which was used to perform the analyses with the studied variables. All data were stored securely and accessible only to authorized research personnel. Only the first author acquired data to guarantee reproducibility. Only the selected variables cited above were collected in the binary format “yes” or “no”, except for the two quantitative variables “age” and “time since first admission to the psychiatric hospital in outpatient or inpatient setting” collected in years (whole number).

To ensure data reliability, data were directly collected during the reading of each medical record.

Ethical approval was obtained by the Ethics Committee of the CHV with the registration number CEREVI/2023/003 on 27 February 2023. The study was conducted in accordance with the Declaration of Helsinki.

3.1 Participants and missing data investigation

All eligible cases have been included in the study (216 cases). Based on the number of cases and the predetermined targeted sample size, 401 controls were included out of a total eligible population of 1,044. The included controls were randomly selected from the sample list of eligible controls.

No missing data were observed for n = 521 patients out of the 617 included in the study.

When considering the mechanism underlying these missing data, it is important to note that they predominantly pertain to variables that necessitate investigating past events. Specifically, these pertain to the presence or absence of a family history of mental health diseases ( n = 55 missing data points out of 617, i.e., 8.9%), the presence or not of personal traumatic history ( n = 38 missing data points out of 617, i.e., 6.2%), and whether or not there was a history of suicide attempt ( n = 17 missing data out of 617, i.e., 2.8%). The other variables have less than 10 missing data points each. The details regarding missing data points for each of the variables within cases and controls are available in Table 1 .

3.2 Sociodemographic characteristics and descriptive analysis

Data from N = 617 patients followed up in an outpatient setting from June 2021 to February 2023 have been investigated for descriptive analysis (216 cases and 401 controls). Men were a higher proportion of cases (65.3%) than controls (56.1%). Cases were slightly younger than controls, with a mean age of 42.7 years old versus 45.1 years, respectively. Unemployment was higher among cases than controls (75.9% of unemployment for cases versus 62.8% for controls), and in parallel, more people had unskilled work in the control group (18.0% versus 5.1% in the case group ( p < 0.001)). Homelessness was much more prevalent among cases than controls, with 13.4% of homeless individuals among cases versus 2.5% for controls ( p < 0.001).

There was a difference in proportion for the main psychiatric diagnosis between groups for depression, schizophrenia or other primary psychotic disorders, anxiety or fear-related disorders, and neurodevelopmental disorders. Schizophrenia, or other primary psychotic disorders, was the main diagnosed psychiatric disease in our population (66.7% and 51.4% for cases and controls, respectively, p < 0.001).

For historical issues and lifestyle variables: case and control groups significantly differed in proportion for history of mental healthcare without consent, multiple psychiatric hospitalization history (> 5), alcohol or illicit drug abuse within the year, family history of mental health disease, and history of attempted suicide ( p < 0.001 except for family history of mental health disease with p = 0.007).

Finally, considering follow-up-related variables, strong significant proportion differences between groups for the following variables were observed ( p < 0.001): visit to a psychiatric emergency, drug treatment discontinuation, medical follow-up discontinuation, and additional support (all within the year). For the variables: visit to psychiatric emergency, drug treatment discontinuation, and medical follow-up discontinuation, the rates were all higher among cases than controls with respectively 60.2%, 58.3%, and 49.1% (cases) versus 7.7%, 12.5%, and 14.0% (controls). Conversely, additional support had a higher proportion in controls (93.8%) than in cases (84.3%) ( p < 0.001).

Table 1 describes the detailed sociodemographic, clinical, personal history, and follow-up characteristics of cases and controls ( N = 617).

3.3 Analytic statistics: multivariable modeling using multiple logistic regression

For the analytic statistics, modeling was conducted using a subset of patients without missing data (complete case analysis) with n = 521. According to our model, we found that six independent variables are significantly associated with full psychiatric hospitalization for patients being followed up in an outpatient setting. Indeed, in multivariable analysis, psychiatric hospitalization of outpatients remained strongly associated with a visit to a psychiatric emergency within a year (aOR: 13.02 [95% CI: 7.32–23.97]), a drug treatment or medical follow-up discontinuation within a year (aOR: 6.43 [95% CI: 3.52–12.03] and aOR: 3.17 [95% CI: 1.70–5.95], respectively), a history of mental healthcare without consent (aOR: 5.48 [95% CI: 3.10–10.06]), and a history of attempted suicide (aOR: 2.50 [95% CI: 1.48–4.30]). Finally, having a work (unskilled work) was conversely associated with a smaller risk of psychiatric hospitalization (aOR: 0.26 [95% CI: 0.10–0.65]). Estimates of adjusted odds ratio were calculated using logistic regression adjusted for the variables included in the model: “visit to a psychiatric emergency within a year”, “drug treatment discontinuation within a year”, “history of mental healthcare without consent”, “medical follow-up discontinuation within a year”, “history of attempted suicide”, and “unskilled job”.

Table 2 presents these identified variables with their respective odds ratios and confidence intervals.

4 Discussion

This study aimed to identify and confirm variables associated with hospitalization, including both protective and risk factors. This information aims to guide and establish appropriate vigilance and follow-up care for mental health in an outpatient setting.

According to our multivariable logistic regression model, six variables have been independently found to be significantly associated with full hospitalization in psychiatry for patients followed up in an outpatient setting: visit to a psychiatric emergency within a year, drug treatment discontinuation within a year, history of mental healthcare without consent, medical follow-up discontinuation within a year, history of attempted suicide, and unskilled job. These findings highlight the importance of considering follow-up-related, historical issues and sociodemographic determinants for successful outpatient rehabilitation and, by extension, deinstitutionalization.

Visit to a psychiatric emergency within the year was the most strongly associated variable with hospitalization and had an aOR of 13.02 (95% CI: 7.32–23.97) in our model. This result is in line with literature that identified emergency visits associated with hospitalization, but to a lesser extent and not in an exclusive outpatient setting like in our study ( 21 ) ( 10 ). Drug treatment discontinuation within the year was associated with an aOR of 6.43 (95% CI: 3.52–12.03). A systematic literature review by Donisi et al. ( 11 ) identified medication compliance as a factor associated with readmissions of psychiatric patients, but Antonio Ciudad et al. ( 20 ) found conflicting results for schizophrenic outpatients. A recent study on early psychiatric rehospitalization also found mental health prescription adherence as a predictor of rehospitalization with a random forest analysis ( 10 ). Medication compliance is known to be an important and challenging factor in the care of psychiatric patients ( 22 ). Our study identified and confirmed the importance of medication compliance in an outpatient setting. History of mental healthcare without consent was also associated with hospitalization (aOR: 5.48, 95% CI: 3.10–10.06). We can assume that patients with a history of care without consent are the ones with bad insight into their illness and are therefore more complex patients, requiring more frequent hospitalization. This risk factor has already been identified, particularly in schizophrenic patients ( 23 ). In another study, conducted without distinction of psychiatric pathology and still in an inpatient setting, no statistical association was found ( 18 ). Medical follow-up discontinuation in psychiatry has also already been studied in the literature. Anne Nelson et al. examined whether patients discharged from inpatient psychiatric care (and not originated from outpatient care like in our study) would have lower rehospitalization rates if they kept an outpatient follow-up appointment after discharge ( 17 ). The authors showed a greater rate of rehospitalization for patients who did not keep an appointment after discharge. The same conclusions have been drawn on a general psychiatric inpatient population ( 10 ) and on a study focused on schizophrenia ( 14 ). In our study, where patients come from an outpatient setting, we also found that medical follow-up discontinuation is a risk factor for hospitalization (aOR: 3.17, 95% CI: 1.70–5.95). A history of attempted suicide also appeared to be a risk factor for psychiatric hospitalization for patients followed up in an outpatient mental health setting, with a 2.50 aOR (95% CI: 1.48–4.30). However, the literature shows conflicting results. Some studies also confirm this risk factor, which has previously been identified in studies conducted in inpatient settings ( 18 , 24 ); in other studies, this risk factor was unclear, with nonsignificant results ( 11 , 21 , 25 ). The ability to have a job, which has been collected in our study with the variable “unskilled worker”, has been identified as a protective factor in the multivariable logistic regression model ( p -value: 0.006) adjusted for potential confounders, as illustrated in Table 2 : aOR of 0.26 (95% CI: 0.10–0.65). We explain this protective effect by assuming that controls, supposed to be clinically less severe than cases, with fewer symptoms, are more likely to get and keep a work. Having a job is indeed linked with cognitive remediation and the recovery process ( 26 ). “Unskilled worker” has been the only job category collected because other job categories were almost nonexistent in our population.

The community-based outpatient setting of the present study is particularly interesting regarding its population characteristics. Indeed, it offers multi-professional monitoring, which is valuable for patients with severe illnesses. With 75.9% of cases and 62.8% of controls unemployed in our study, this strongly suggests that mental disability significantly impacts psychosocial determinants, highlighting its importance. As with other chronic illnesses, psychological disability is a barrier to employment, and the severity of the condition is related to the ability to work ( 26 ). This might also explain the protective effect found in the association of the variable “unskilled worker”. Patients followed up regularly in this setting are also considered “severe” for other reasons. They often cannot follow a liberal mental health specialist due to poor socioeconomic conditions and may have a too severe psychiatric disorder requiring hospital practitioners (due to complex pharmacotherapeutics or illness) to reach a stable medical state. From a clinical point of view, most patients having a main diagnosis of schizophrenia or other primary psychotic disorders (66.7% among cases and 51.4% among controls) is another argument for the population severity, with patients who cannot be adequately followed up by general practitioners and/or private psychiatrists. Interestingly, this does not represent the psychiatric diseases distribution of general population and is even the opposite. Indeed, in France, anxiety disorders have the highest prevalence, followed by depression, bipolar disorders, and finally, psychotic disorders ( 27 ). Regarding historical issues and lifestyle, the prevalence of traumatic history was notably high in both groups, with around 60% prevalence. Mental health conditions are well-known to have multifactorial origins ( 28 ). Nevertheless, it is noteworthy to observe the prevalence of traumatic exposure within our study population. The high proportions of patients with mental healthcare without consent history and multiple psychiatric hospitalization histories (> 5) also underline the specificities of our outpatient population, which have a certain severity. Multidisciplinary community-based care has the potential to address the specific needs of the population within the framework of deinstitutionalization when considering the identified determinants.

The case–control design and the multivariable logistic regression utilized have, however, their limitations. Firstly, the population selection has been made through “hospital recruitment” (outpatient service attached to the CHV public psychiatric hospital). It can therefore introduce a selection bias regarding the admission probability of participants to that public outpatient service (e.g., patients with poorest socioeconomic conditions). Nonetheless, as the probability of admission to that service relies on the geographical sectorization (population originating from a defined geographic urban area: third, sixth, and eighth districts of Lyon) and has few equivalents in the private sector, we consider this bias to be existent but limited. To limit classification bias, classification was made on electronical medical records identically for cases and controls. Sectorization also prevents the risk of missing a hospitalization in another facility by ensuring the patient is ultimately hospitalized in his or her local hospital. Confusion bias has been considered via modeling with multivariable logistic regression. We assessed interactions in our model with one being significant (variable history of mental healthcare without consent with variable history of attempted suicide, adjusted p -value of 0.004). We, however, decided not to include this interaction in the model because (i) the clinical relevance of this interaction was not key in our exploratory investigation, and we do not seek a predictive model; (ii) considering that this interaction barely improves our overall model significance (residual deviance of 361 when considered versus 370, p -value: 0.003). Lastly, a limitation of our model is the absence of residuals normality for this multiple logistic regression. Indeed, residuals do not seem independent of the predicted values. Some explanatory variables would thus be lacking and not exhaustively listed in this study, such as variables on education level or on patient’s attitude and perception.

The highlights of this study are, however, its overall consistency with literature data on previously identified risk factors associated with hospitalization and the confirmation of these factors in an exclusive outpatient setting. The recruitment method used in this study with the sectorization principle of the service is also a robust point because it allowed to limit selection bias and consider all the patients followed up in this special outpatient setting.

5 Conclusion

Our study identified several independent risk and protective factors for hospitalization among patients with a mental health condition who are being treated in an outpatient setting. These factors include variables related to follow-up, such as a recent visit to a psychiatric emergency and recent discontinuation of drug treatment or medical follow-up (within the year), as well as historical issues or lifestyle-related factors.

To our knowledge, this is the first time that these factors are assessed statistically together in a specific outpatient setting, with patients not originating exclusively from a hospital. That is of great interest in the deinstitutionalization era. Public health policies at local and to a bigger extent, at the national scale, should consider these new data to target and tailor appropriate follow-up of care in outpatient settings. Tools to distinguish patients with the identified risk factors and prevent them from being hospitalized should also be created and adapted.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: Medical information that cannot be shared according to the ethical approval obtained by the Ethics Committee of the CHV with the registration number CEREVI/2023/003 on 02/27/2023. Requests to access these datasets should be directed to [email protected].

Ethics statement

The studies involving humans were approved by Ethics Committee of the CHV with the registration number CEREVI/2023/003 on 02/27/2023. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because the data were obtained in routine care practice with patient information and possible retraction. The study was carried out in accordance with current legislations.

Author contributions

ML: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal Analysis, Data curation, Conceptualization. RM: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization. CD: Writing – review & editing, Visualization, Validation, Supervision, Methodology. LZ: Writing – review & editing, Validation, Supervision, Project administration. JP: Writing – review & editing, Software, Data curation. NF: Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1341160/full#supplementary-material

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Keywords: mental health system, outpatient clinic, deinstitutionalization, epidemiology, public health

Citation: Lebrat M, Megard R, Dananché C, Zimmer L, Plasse J and Franck N (2024) Identification of factors associated with hospitalization in an outpatient population with mental health conditions: a case–control study. Front. Psychiatry 15:1341160. doi: 10.3389/fpsyt.2024.1341160

Received: 19 November 2023; Accepted: 28 March 2024; Published: 18 April 2024.

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Copyright © 2024 Lebrat, Megard, Dananché, Zimmer, Plasse and Franck. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Matthieu Lebrat, [email protected]

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Political Typology 2017

Survey conducted June 8-18 and June 27-July 9, 2017

The Generation Gap in American Politics

Generational differences have long been a factor in U.S. politics. These divisions are now as wide as they have been in decades, with the potential to shape politics well into the future.

Political Typology Reveals Deep Fissures on the Right and Left

The partisan divide on political values grows even wider.

Gaps between Republicans and Democrats over racial discrimination, immigration and poverty assistance have widened considerably in recent years.

Partisan Shifts in Views of the Nation, but Overall Opinions Remain Negative

Republicans have become far more upbeat about the country and its future since before Donald Trump’s election victory. By contrast, Democrats have become much less positive.

Since Trump’s Election, Increased Attention to Politics – Especially Among Women

Following an election that had one of the largest gender gaps in history, women are more likely than men to say they are paying increased attention to politics.

Support for Same-Sex Marriage Grows, Even Among Groups That Had Been Skeptical

Two years after the Supreme Court decision that required states to recognize same-sex marriages nationwide, support for allowing gays and lesbians to marry legally is at its highest point in over 20 years of Pew Research Center polling on the issue.

Public Has Criticisms of Both Parties, but Democrats Lead on Empathy for Middle Class

Both political parties’ favorability ratings are more negative than positive and fewer than half say either party has high ethical standards.

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