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• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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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.

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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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• 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

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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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Psychological Research

Descriptive research, learning objectives.

• Differentiate between descriptive, experimental, and correlational research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys

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 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. Table 1 displays a quick overview of the characteristics of each research design.

Table 1. Characteristics of Descriptive, Experimental, and Correlational Research

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 this allows for 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 this 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 in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This 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.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

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 hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses 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 1 . Seeing a police car behind you would probably affect your driving behavior. (credit: Michael Gil)

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. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

Figure 2 . (a) Jane Goodall made a career of conducting naturalistic observations of (b) chimpanzee behavior. (credit “Jane Goodall”: modification of work by Erik Hersman; “chimpanzee”: modification of work by “Afrika Force”/Flickr.com)

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 or animals 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. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

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. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

Figure 3 . 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 of the survey in comparison 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 don’t 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 US 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

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY-SA: Attribution-ShareAlike
• Approaches to Research. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/2-2-approaches-to-research . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction.
• 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

• 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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2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

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 behavior 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 2.2 “Characteristics of the Three Research Designs” , 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.

Table 2.2 Characteristics of the Three Research Designs

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

Descriptive Research: Assessing the Current State of Affairs

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

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 behavior . 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).

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

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 is question 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 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 behaviors 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 ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), 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 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

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 2.5 “Height Distribution” , where most of the scores are located near the center 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 .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean , 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 2.5 “Height Distribution” , the mean height of the students is 67.12 inches. 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 2.6 “Family Income Distribution” ), 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 2.6 “Family Income Distribution” 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).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

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 2.6 “Family Income Distribution” 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, like this:

Or they may be more spread out away from it, like this:

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 2.5 “Height Distribution” 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 behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior 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 behaviors 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 like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

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 2.10 “Examples of Scatter Plots” , 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 2.10 “Examples of Scatter Plots” , 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 2.10 “Examples of Scatter Plots” 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 2.10 “Examples of Scatter Plots” 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 .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

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

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 2.11 “Prediction of Job Performance From Three Predictor Variables” shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. 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.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

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 behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade 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 behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

there are other possibilities. One alternate 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:

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:

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 both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

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 behavior 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 behavior 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 Behavior

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 2.2.3

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 behavior. 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 behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) 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 behavior, 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 behaviors 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?

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. (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.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Using Science to Inform Educational Practices

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior. 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 main categories of psychological research are descriptive, correlational, and experimental research. Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions.

Research studies that do not test specific relationships between variables are called  descriptive studies . For this method, the research question or hypothesis can be about a single variable (e.g., How accurate are people’s first impressions?) or can be a broad and exploratory question (e.g., What is it like to be a working mother diagnosed with depression?). The variable of the study is measured and reported without any further relationship analysis. A researcher might choose this method if they only needed to report information, such as a tally, an average, or a list of responses. Descriptive research can answer interesting and important questions, but what it cannot do is answer questions about relationships between variables.

Video 2.4.1.  Descriptive Research Design  provides explanation and examples for quantitative descriptive research. A closed-captioned version of this video is available here .

Descriptive research is distinct from  correlational research , in which researchers 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 causal relationships between variables. We will discuss each of these methods more in-depth later.

Table 2.4.1. Comparison of research design methods

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• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/edpsy/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/adolescent/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Educational Psychology Copyright © 2020 by Nicole Arduini-Van Hoose is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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Research: Articulating Questions, Generating Hypotheses, and Choosing Study Designs

Introduction.

Articulating a clear and concise research question is fundamental to conducting a robust and useful research study. Although “getting stuck into” the data collection is the exciting part of research, this preparation stage is crucial. Clear and concise research questions are needed for a number of reasons. Initially, they are needed to enable you to search the literature effectively. They will allow you to write clear aims and generate hypotheses. They will also ensure that you can select the most appropriate research design for your study.

This paper begins by describing the process of articulating clear and concise research questions, assuming that you have minimal experience. It then describes how to choose research questions that should be answered and how to generate study aims and hypotheses from your questions. Finally, it describes briefly how your question will help you to decide on the research design and methods best suited to answering it.

TURNING CURIOSITY INTO QUESTIONS

A research question has been described as “the uncertainty that the investigator wants to resolve by performing her study” 1 or “a logical statement that progresses from what is known or believed to be true to that which is unknown and requires validation”. 2 Developing your question usually starts with having some general ideas about the areas within which you want to do your research. These might flow from your clinical work, for example. You might be interested in finding ways to improve the pharmaceutical care of patients on your wards. Alternatively, you might be interested in identifying the best antihypertensive agent for a particular subgroup of patients. Lipowski 2 described in detail how work as a practising pharmacist can be used to great advantage to generate interesting research questions and hence useful research studies. Ideas could come from questioning received wisdom within your clinical area or the rationale behind quick fixes or workarounds, or from wanting to improve the quality, safety, or efficiency of working practice.

Alternatively, your ideas could come from searching the literature to answer a query from a colleague. Perhaps you could not find a published answer to the question you were asked, and so you want to conduct some research yourself. However, just searching the literature to generate questions is not to be recommended for novices—the volume of material can feel totally overwhelming.

Use a research notebook, where you regularly write ideas for research questions as you think of them during your clinical practice or after reading other research papers. It has been said that the best way to have a great idea is to have lots of ideas and then choose the best. The same would apply to research questions!

When you first identify your area of research interest, it is likely to be either too narrow or too broad. Narrow questions (such as “How is drug X prescribed for patients with condition Y in my hospital?”) are usually of limited interest to anyone other than the researcher. Broad questions (such as “How can pharmacists provide better patient care?”) must be broken down into smaller, more manageable questions. If you are interested in how pharmacists can provide better care, for example, you might start to narrow that topic down to how pharmacists can provide better care for one condition (such as affective disorders) for a particular subgroup of patients (such as teenagers). Then you could focus it even further by considering a specific disorder (depression) and a particular type of service that pharmacists could provide (improving patient adherence). At this stage, you could write your research question as, for example, “What role, if any, can pharmacists play in improving adherence to fluoxetine used for depression in teenagers?”

TYPES OF RESEARCH QUESTIONS

Being able to consider the type of research question that you have generated is particularly useful when deciding what research methods to use. There are 3 broad categories of question: descriptive, relational, and causal.

Descriptive

One of the most basic types of question is designed to ask systematically whether a phenomenon exists. For example, we could ask “Do pharmacists ‘care’ when they deliver pharmaceutical care?” This research would initially define the key terms (i.e., describing what “pharmaceutical care” and “care” are), and then the study would set out to look for the existence of care at the same time as pharmaceutical care was being delivered.

When you know that a phenomenon exists, you can then ask description and/or classification questions. The answers to these types of questions involve describing the characteristics of the phenomenon or creating typologies of variable subtypes. In the study above, for example, you could investigate the characteristics of the “care” that pharmacists provide. Classifications usually use mutually exclusive categories, so that various subtypes of the variable will have an unambiguous category to which they can be assigned. For example, a question could be asked as to “what is a pharmacist intervention” and a definition and classification system developed for use in further research.

When seeking further detail about your phenomenon, you might ask questions about its composition. These questions necessitate deconstructing a phenomenon (such as a behaviour) into its component parts. Within hospital pharmacy practice, you might be interested in asking questions about the composition of a new behavioural intervention to improve patient adherence, for example, “What is the detailed process that the pharmacist implicitly follows during delivery of this new intervention?”

After you have described your phenomena, you may then be interested in asking questions about the relationships between several phenomena. If you work on a renal ward, for example, you may be interested in looking at the relationship between hemoglobin levels and renal function, so your question would look something like this: “Are hemoglobin levels related to level of renal function?” Alternatively, you may have a categorical variable such as grade of doctor and be interested in the differences between them with regard to prescribing errors, so your research question would be “Do junior doctors make more prescribing errors than senior doctors?” Relational questions could also be asked within qualitative research, where a detailed understanding of the nature of the relationship between, for example, the gender and career aspirations of clinical pharmacists could be sought.

Once you have described your phenomena and have identified a relationship between them, you could ask about the causes of that relationship. You may be interested to know whether an intervention or some other activity has caused a change in your variable, and your research question would be about causality. For example, you may be interested in asking, “Does captopril treatment reduce blood pressure?” Generally, however, if you ask a causality question about a medication or any other health care intervention, it ought to be rephrased as a causality–comparative question. Without comparing what happens in the presence of an intervention with what happens in the absence of the intervention, it is impossible to attribute causality to the intervention. Although a causality question would usually be answered using a comparative research design, asking a causality–comparative question makes the research design much more explicit. So the above question could be rephrased as, “Is captopril better than placebo at reducing blood pressure?”

The acronym PICO has been used to describe the components of well-crafted causality–comparative research questions. 3 The letters in this acronym stand for Population, Intervention, Comparison, and Outcome. They remind the researcher that the research question should specify the type of participant to be recruited, the type of exposure involved, the type of control group with which participants are to be compared, and the type of outcome to be measured. Using the PICO approach, the above research question could be written as “Does captopril [ intervention ] decrease rates of cardiovascular events [ outcome ] in patients with essential hypertension [ population ] compared with patients receiving no treatment [ comparison ]?”

DECIDING WHETHER TO ANSWER A RESEARCH QUESTION

Just because a question can be asked does not mean that it needs to be answered. Not all research questions deserve to have time spent on them. One useful set of criteria is to ask whether your research question is feasible, interesting, novel, ethical, and relevant. 1 The need for research to be ethical will be covered in a later paper in the series, so is not discussed here. The literature review is crucial to finding out whether the research question fulfils the remaining 4 criteria.

Conducting a comprehensive literature review will allow you to find out what is already known about the subject and any gaps that need further exploration. You may find that your research question has already been answered. However, that does not mean that you should abandon the question altogether. It may be necessary to confirm those findings using an alternative method or to translate them to another setting. If your research question has no novelty, however, and is not interesting or relevant to your peers or potential funders, you are probably better finding an alternative.

The literature will also help you learn about the research designs and methods that have been used previously and hence to decide whether your potential study is feasible. As a novice researcher, it is particularly important to ask if your planned study is feasible for you to conduct. Do you or your collaborators have the necessary technical expertise? Do you have the other resources that will be needed? If you are just starting out with research, it is likely that you will have a limited budget, in terms of both time and money. Therefore, even if the question is novel, interesting, and relevant, it may not be one that is feasible for you to answer.

GENERATING AIMS AND HYPOTHESES

All research studies should have at least one research question, and they should also have at least one aim. As a rule of thumb, a small research study should not have more than 2 aims as an absolute maximum. The aim of the study is a broad statement of intention and aspiration; it is the overall goal that you intend to achieve. The wording of this broad statement of intent is derived from the research question. If it is a descriptive research question, the aim will be, for example, “to investigate” or “to explore”. If it is a relational research question, then the aim should state the phenomena being correlated, such as “to ascertain the impact of gender on career aspirations”. If it is a causal research question, then the aim should include the direction of the relationship being tested, such as “to investigate whether captopril decreases rates of cardiovascular events in patients with essential hypertension, relative to patients receiving no treatment”.

The hypothesis is a tentative prediction of the nature and direction of relationships between sets of data, phrased as a declarative statement. Therefore, hypotheses are really only required for studies that address relational or causal research questions. For the study above, the hypothesis being tested would be “Captopril decreases rates of cardiovascular events in patients with essential hypertension, relative to patients receiving no treatment”. Studies that seek to answer descriptive research questions do not test hypotheses, but they can be used for hypothesis generation. Those hypotheses would then be tested in subsequent studies.

CHOOSING THE STUDY DESIGN

The research question is paramount in deciding what research design and methods you are going to use. There are no inherently bad research designs. The rightness or wrongness of the decision about the research design is based simply on whether it is suitable for answering the research question that you have posed.

It is possible to select completely the wrong research design to answer a specific question. For example, you may want to answer one of the research questions outlined above: “Do pharmacists ‘care’ when they deliver pharmaceutical care?” Although a randomized controlled study is considered by many as a “gold standard” research design, such a study would just not be capable of generating data to answer the question posed. Similarly, if your question was, “Is captopril better than placebo at reducing blood pressure?”, conducting a series of in-depth qualitative interviews would be equally incapable of generating the necessary data. However, if these designs are swapped around, we have 2 combinations (pharmaceutical care investigated using interviews; captopril investigated using a randomized controlled study) that are more likely to produce robust answers to the questions.

The language of the research question can be helpful in deciding what research design and methods to use. Subsequent papers in this series will cover these topics in detail. For example, if the question starts with “how many” or “how often”, it is probably a descriptive question to assess the prevalence or incidence of a phenomenon. An epidemiological research design would be appropriate, perhaps using a postal survey or structured interviews to collect the data. If the question starts with “why” or “how”, then it is a descriptive question to gain an in-depth understanding of a phenomenon. A qualitative research design, using in-depth interviews or focus groups, would collect the data needed. Finally, the term “what is the impact of” suggests a causal question, which would require comparison of data collected with and without the intervention (i.e., a before–after or randomized controlled study).

CONCLUSIONS

This paper has briefly outlined how to articulate research questions, formulate your aims, and choose your research methods. It is crucial to realize that articulating a good research question involves considerable iteration through the stages described above. It is very common that the first research question generated bears little resemblance to the final question used in the study. The language is changed several times, for example, because the first question turned out not to be feasible and the second question was a descriptive question when what was really wanted was a causality question. The books listed in the “Further Reading” section provide greater detail on the material described here, as well as a wealth of other information to ensure that your first foray into conducting research is successful.

This article is the second in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous article in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Competing interests: Mary Tully has received personal fees from the UK Renal Pharmacy Group to present a conference workshop on writing research questions and nonfinancial support (in the form of travel and accommodation) from the Dubai International Pharmaceuticals and Technologies Conference and Exhibition (DUPHAT) to present a workshop on conducting pharmacy practice research.

Further Reading

• Cresswell J. Research design: qualitative, quantitative and mixed methods approaches. London (UK): Sage; 2009. [ Google Scholar ]
• Haynes RB, Sackett DL, Guyatt GH, Tugwell P. Clinical epidemiology: how to do clinical practice research. 3rd ed. Philadelphia (PA): Lippincott, Williams & Wilkins; 2006. [ Google Scholar ]
• Kumar R. Research methodology: a step-by-step guide for beginners. 3rd ed. London (UK): Sage; 2010. [ Google Scholar ]
• Smith FJ. Conducting your pharmacy practice research project. London (UK): Pharmaceutical Press; 2005. [ Google Scholar ]

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Descriptive Research Design

Descriptive research design is a scientific method which involves observing and describing the behavior of a subject without influencing it in any way.

This article is a part of the guide:

• Research Designs
• Quantitative and Qualitative Research
• Literature Review
• Quantitative Research Design

Browse Full Outline

• 1 Research Designs
• 2.1 Pilot Study
• 2.2 Quantitative Research Design
• 2.3 Qualitative Research Design
• 2.4 Quantitative and Qualitative Research
• 3.1 Case Study
• 3.2 Naturalistic Observation
• 3.3 Survey Research Design
• 3.4 Observational Study
• 4.1 Case-Control Study
• 4.2 Cohort Study
• 4.3 Longitudinal Study
• 4.4 Cross Sectional Study
• 4.5 Correlational Study
• 5.1 Field Experiments
• 5.2 Quasi-Experimental Design
• 5.3 Identical Twins Study
• 6.1 Experimental Design
• 6.2 True Experimental Design
• 6.3 Double Blind Experiment
• 6.4 Factorial Design
• 7.1 Literature Review
• 7.2 Systematic Reviews
• 7.3 Meta Analysis

Many scientific disciplines, especially social science and psychology, use this method to obtain a general overview of the subject.

Some subjects cannot be observed in any other way; for example, a social case study of an individual subject is a descriptive research design and allows observation without affecting normal behavior.

It is also useful where it is not possible to test and measure the large number of samples needed for more quantitative types of experimentation .

These types of experiments are often used by anthropologists, psychologists and social scientists to observe natural behaviors without affecting them in any way. It is also used by market researchers to judge the habits of customers, or by companies wishing to judge the morale of staff.

The results from a descriptive research can in no way be used as a definitive answer or to disprove a hypothesis but, if the limitations are understood, they can still be a useful tool in many areas of scientific research.

The subject is being observed in a completely natural and unchanged natural environment. A good example of this would be an anthropologist who wanted to study a tribe without affecting their normal behavior in any way. True experiments , whilst giving analyzable data, often adversely influence the normal behavior of the subject.

Descriptive research is often used as a pre-cursor to quantitative research designs, the general overview giving some valuable pointers as to what variables are worth testing quantitatively. Quantitative experiments are often expensive and time-consuming so it is often good sense to get an idea of what hypotheses are worth testing .

Disadvantages

Because there are no variables manipulated , there is no way to statistically analyze the results. Many scientists regard this type of study as very unreliable and ‘unscientific’.

In addition, the results of observational studies are not repeatable , and so there can be no replication of the experiment and reviewing of the results.

Descriptive research design is a valid method for researching specific subjects and as a precursor to more quantitative studies. Whilst there are some valid concerns about the statistical validity , as long as the limitations are understood by the researcher, this type of study is an invaluable scientific tool.

Whilst the results are always open to question and to different interpretations, there is no doubt that they are preferable to performing no research at all.

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Martyn Shuttleworth (Sep 26, 2008). Descriptive Research Design. Retrieved May 22, 2024 from Explorable.com: https://explorable.com/descriptive-research-design

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Turning Information into Knowledge

Questions to consider:

• What is the difference between information and knowledge?
• What is information literacy?
• What are the steps to a good research study?

What Is the Difference between Information and Knowledge?

Life is a series of problems needing solutions. We need to find information that matters and then discover why it matters. Curiosity, then, is a response to an environment of exploration, manifesting in wanting to know “why” or “how.” How do you make sense of the world? How does information translate to knowledge? Connecting ideas, thinking critically, acting responsibly, and communicating effectively are all essential to lifelong learning and active engagement in today’s world. You need to become proficient, ethical users and producers of information in a globally connected world. It is important to be able to reason, manage resources, work productively with others, acquire and evaluate information effectively, organize information, interpret and communicate the information, and work with an ever-evolving variety of technologies. In other words, you need to become information-savvy consumers and producers. You need to be able to adapt to, understand, evaluate, and make use of technology so you can be citizens that shape our society, rather than being its pawns. What you learn is often what you will want to communicate to others.

What Is Information Literacy?

Human beings are passionate, curious, and always seeking to connect with each other and make sense of things. Learning is more effective when new information is meaningful and linked to some personal experience or prior knowledge. Learning is about both context and content. It is necessary to learn how to assess, evaluate, and connect in order to make information become knowledge. Information literacy skills are the hallmark of the ability to do research. What is important is for you to learn how to find information that “matters” and then figure out why it might matter.

Information literacy is a link between the life experiences of you as a student, the academic world of scholarship, and the postcollege real world of application of learning. An information-literate person has the ability to ask questions and knows the difference between ignorance and understanding. (When do I need information?) Information literacy builds a lifelong ability to determine where information is kept (Where is the best place to find this?) and in what forms knowledge is stored (Which knowledge products will likely have what I need?).

Information literacy relies on the use of a critical mind to discern credible from not credible, valid from not valid. It is actually the core of the first-year experience. It lasts, while the specifics of particular courses fade over time. After all, the nature of research, the core of higher education, is a learning process: “How do I learn about something?” Communication skills are essential to your ability to both learn and share what you’ve learned.

What Are the Steps to a Good Research Study?

Research is a part of life. In fact, you conduct research daily. You look things up whenever you want a hotel or a good restaurant in a new city, or a recipe for cookies you’d like to make for a party. Sometimes you use Google for answers, and other times you ask people to help you answer your question. At times you might need to visit specific websites to find good information on the kind of used car you should buy or tickets to a sporting event or concert you hope to attend. All of this is part of research at its most basic level—asking a question and then answering it. Research can be defined as an activity that produces new knowledge. However, it is not timeless. Questions change, and so do answers. New questions bring new light to bear on any topic or issue. For example, consider the way we have controlled the use of pesticides. Over time, we moved from acceptance to shock and now horror at some of the side effects. It is new information on pesticides that has influenced our change in thinking. And the reason we know this information is that someone did the research and then communicated it to our community through newscasts, newspapers, online sites, and so forth.

We often accept ideas as fact. For instance, how do we come to believe such things as “Three out of four dentists recommend . . . ” or “McDonald’s french fries are preferred three to one over . . .”? Or that heroin is addictive, or that putting infants in car seats prevents fatal injuries, or that drinking while pregnant can be harmful? It is important to know that these statements are the result of questions that led to serious research. Understanding the methods used to do research will help us understand how we come to know what we know. In cases such as these, someone was interested in knowing the answer to a particular question, planned a research study, and then published the findings. When people do this kind of research, their purpose is not only to find an answer but also to communicate what they found to the rest of us. They are communicating new knowledge.

Research is exploration and the search for possible answers to questions. Most students think research is about finding answers, but it is more about the questions we ask that lead us to the answers. Good research starts with good questions. Researchers ask themselves a question, create a possible answer in the form of a hypothesis, and then begin a process of gathering information with a methodology. If we understand how important questions are to doing research, we are then better able to determine the credibility and validity of the information sources we use. When evaluating sources, we can ask: Why should I believe this author? What does she know that makes her someone I should pay attention to? And when deciding on credibility, we can ask: What did the author do to convince me his answer is the correct one? Did the evidence really match the question the author was asking? Thus, information literacy is the ability to evaluate sources on the basis of what questions were asked, determine if those are the best questions to ask, assess whether the answers offered really answer the questions, and decide if the author is prepared to answer those questions well. Remember the literacies that Howard Rheingold suggested in the “Communicating” chapter. Using these as guides leads us to mindfully explore the vast array of information available to us. And when we do so, we won’t find ourselves taking information at face value and passing it on as though it were valid, like some of the “fake news” that is prevalent today.

So let’s start the process of doing research. The activity below will help you begin the process. After this, you will be introduced to the simple steps you need to take to do the research and then communicate your findings appropriately.

Pick a topic you might like to research or have already been assigned to research for a class. Then take a close look at the list of knowledge products below, and rank them in order of which ones you would most likely use for a research paper. After ranking them, explain why you put them in that order.

• Books: histories, pictures, topic overviews
• Journals: research studies, expert opinions, analyses, lists of other information sources
• Magazines: basic and recent information, pictures, reviews
• Newspapers: very recent information, place-specific information, reviews
• Films, videos, television, music: pictures, speeches, sound
• Internet sources: current or historical information from a variety of sources or individuals, data or commentary compiled by individuals or specific organizations or companies, graphics, sound, music, animation, video, pictures
• Conversations, interviews: opinions, direct experiences, personal viewpoints, attitudes, histories
• Government publications: reports, studies, statistics, laws, regulations
• Documents: reports, laws, statistics, facts
• Diaries: personal stories, histories, opinions, reflections

These can also categorized by types of knowledge products. For your research, you have to choose wisely among these, too. There are scholarly knowledge products, which are mostly written for scholars in a particular field. The author is identified, and credentials are available. Sources are documented, and technical language is often used. Secondly, some knowledge products might be considered professional . These are written for professionals in a field, the author is most often identified, sources are not always documented, and the language may or may not be technical. Finally, there are popular knowledge products, which communicate a broad range of information. The author is often not identified, sources are often not documented, and language is not technical. Because they are commercial products packaged for wide sales, they often use color and have numerous ads.

When you are faced with a research assignment, it is important for you to be able to create successful search strategies. You need to find sources for specific purposes and audiences and be able to critically evaluate these sources. When doing research, you also have to incorporate the information you find for specific purposes, acknowledge the sources, and provide citations. To make this easier to understand, think of scholarly writing as a simple story told with a particular set of conventions (rules). What are these conventions? They are: a research question, a hypothesis, a methodology, a review of the literature, an interpretation of your work, and an analysis of the significance of what you’ve found.

Research Question

First of all, you need a topic. This is often the most difficult part of the whole process. So begin by thinking of something that is really interesting to you. Let’s take music for an example. You need to ask some questions about music to start the process. Some examples of questions are:

• What does music mean?
• What is the function of music?
• What is the value of music?
• What is the significance of music?
• How is music made?
• What causes music to happen?

The easiest way to come up with questions regarding whatever topic you choose is to start with basic questioning words: who , what , why , when , where , how , might , could , can , should , will , must , did , and so forth. You can ask better questions, and this will help you narrow down your hypothesis. For instance, why does music change over time? Who will play this music? How did this music come about? Why should we listen to this music?

Pick a topic and try to describe it:

• Name your topic: I am studying __________
• Suggest a question: Because I want to find out who/how/why/whether/when/what _______________
• State a rationale for the question: In order to understand who/what/where/how/why/whether ___________

Going through this exercise every time you are tasked with writing a research paper will help you clarify what you want to accomplish and why.

Scholars use information to answer one or more questions inspired by a topic of interest. Usually, a scholarly question identifies a problem and a solution. Such questions are usually written in the form of a hypothesis, which is a statement about the relationship between two things that identifies both a problem and an answer or solution. An example of a hypothesis would be: Different genres of music have an effect on the mood of the people listening to them. The questions asked to get to this hypothesis might be: Does music have an effect on mood? Do people listen to music to make them feel better? What kind of music is used as a way to energize the listener? Is there one type of music that is better than others for calming someone down?

Your hypothesis must reflect what is known about a research topic in such a way that your research project will add new knowledge and insight to what is already known. In order to arrive at a hypothesis that achieves this goal, you must learn as much as possible about your topic so you can narrow down your hypotheses to what you don’t know. Then your research project will produce new knowledge. Your hypothesis is about what you don’t know . However, you might find that you can’t prove your hypothesis. You might find evidence that contradicts it, and you will have to reflect on why your hypothesis might have been wrong.

Find two newspaper articles to analyze. Read through them and answer the following:

• What questions are being answered in the articles?
• What questions do you think need to be answered?
• What was the hypothesis that the writer of these articles was working from?

It is important to be able to find the hypothesis that a writer has constructed to tell you a story. You have to make sure you understand what they are trying to “prove” and what questions they asked in order to do so.

Methodology

Education is about discovery. This means that you need to learn how to question, evaluate, and determine the worth, credibility, and relevance of what you, as a student, find. Thus, when doing research, you need that hypothesis to begin the rest of your research.

The next step is to come up with key words or concepts that describe your topic. Start by preparing an outline for yourself. List the key words (for instance, on the topic of music, some key words might be music , instruments , genres , musicians , and so on). Then create a list of narrower terms, which are more specific things that you want to know about your topic, such as time frames , geography , population , and age groups . Finally, you can list broader terms that are the larger subjects that include your key words. For music these could be cultural expression , jazz , hip-hop , singers , and so forth. Your methodology will be a compilation of the sources you decide to review. It is an orderly approach to problem solving and gathering useful data, using such sources and strategies as interviews, public documents, surveys, experiments, the Internet, and many more.

The kind of methodology you decide to use depends on the type of research you will be conducting. You could do exploratory research , which basically answers the question “Does something exist?” This “something” could be an event, a thing, or an idea, such as a concert or music designed for relaxation. Or perhaps you want to do descriptive research , which is the kind of study that defines something by describing its characteristics, behaviors, or actions. For instance, you could describe a genre of music, how it was created, and what instruments are usually used to compose this type of music. A third type of research you may want to do is called prediction research , which involves identifying relationships that make it possible for us to speculate about one thing by knowing about something else. Music has taken many turns over time, and you might want to suggest that the next phase of music might all be electronically produced. And finally, you could choose to do explanatory research . This type of research examines cause-and-effect relationships. For example, there is music created to tell a particular story in a specific manner. This might be true of rap music. To study this, you would use explanatory research to describe this phenomenon.

Review of the Literature

One other piece of the research puzzle is a review of the literature. The literature in a particular field is its discourse , which is actually a conversation over time about a topic. When you do your literature review, you are inserting yourself in the middle of such a conversation and getting information only from that particular time and perspective. For instance, if you want to study the effects of music on children, you will find a wide variety of sources that will give you information about the topic. You will discover that many people have been interested in the issue and have done studies trying to find out the answer. These studies have been done over many years, and the perspectives involved have changed accordingly. The discourse continues over time, and you can insert information into the conversation by conducting your own research.

Thus, a review of the literature finds, evaluates, and integrates past research. It is a critical synthesis of research literature that:

• shows how previous studies relate to one another.
• shows similarities and differences between studies.
• discriminates between relevant and irrelevant information.
• indicates weaknesses in previous work.

The purpose of the literature review is to synthesize many specific events and details into a comprehensive whole. Synthesis results from weaving together many smaller generalizations and interpretations into a coherent main theme. You will find that a literature review is always required of an assigned research paper for a course. The purpose is to enable you to critically analyze a segment of an already published body of knowledge. A comprehensive literature review encompasses the following elements:

• Start the introduction by describing the problem or issue you are addressing, then focus on your research hypotheses or questions.
• Explicitly state the significance of the topic in the introduction.
• Present the review as an essay, not an annotated list.
• Emphasize the findings of previous research you have found.
• Point out the trends and themes in the literature.
• Point out the gaps in the literature.
• Express opinions about the quality and importance of the research you have found.
• Use the review to suggest that there is a need for more study.

Avoiding Plagiarism

You certainly have heard about plagiarism and how important it is not to let yourself participate in it. It is so easy to read through many other people’s work and grab a sentence here and there to put into your own paper. As you’re struggling to come up with ideas, you may also find yourself borrowing from others. Neither of these is a good idea.

Plagiarism often starts with the note-taking stage of the research process. Thus, when taking notes, be sure to distinguish between paraphrases and direct quotations. When you are copying an exact quotation, be extremely precise. Note all the information you will need for the citation. It is a good idea to make a system for yourself, perhaps color coding, when doing your research. Make direct quotations one color and your own paraphrasing of ideas another color. Both quotations and paraphrases need to be cited with sources, both within the paper and at the end.

Learning how to use the ideas of others to add weight to your own ideas involves effort and a commitment to academic honesty. It is not always clear exactly how or when to use sources, and sometimes you might need advice or guidance. Since your professors are most familiar with the expectations of their disciplines, they are the best people to ask. Your college likely offers support in the writing lab or online. If you need more guidance, the Purdue Online Writing Lab (OWL) has a section on safe practices for researching and drafting , where you can find excellent advice on identifying plagiarism and preventing yourself from plagiarizing.

While the process of writing authentically and avoiding plagiarism must be focused from the start, you can avoid a world of trouble by double-checking your near-final work with a source identification site or plagiarism detector. Doing so can help you avoid any unintentional reuse of others’ work and may simply identify a source you forgot to cite. Chegg Writing allows you to upload or paste in your paper for a detailed source evaluation. Note that this is only a check step; you must follow best practices to ensure that you don’t plagiarize.

Validity and Credibility

Before you move on to interpreting your data and addressing the significance of what you found, you need to understand the concepts of validity and credibility. There are many ways you can check the validity of a piece of information. Can you find contradictory or confirmatory data? Can you find evidence that disputes what you are reading? If so, use this information. It is always useful to mention opposing ideas. Ultimately, doing so might strengthen your own ideas. Is the topic within the expertise of the person offering the information? Was the method chosen to convey this information the best method to use? The credibility of the author is another important aspect of checking your sources. In other words, evaluate the authors. Are they experts on the topic? Do they have credentials to write on this particular topic? Has this author written anything else on this topic?

Evidence is the way we show that we are using the experiences, values, research, and perspectives of others. To be information literate is to apply the concepts of subjective and objective evidence to our selection, use, and evaluation of information. When we read a website or view a television program, can we recognize that a particular set of values and perspectives is being used? Are we able to identify when evidence is being used? Can we determine that the evidence being used shows a relevant connection between values, perspectives, and conclusions? Are enough different values and perspectives being presented that the conclusions can be considered objective? It is important to learn how to determine the validity and credibility of sources.

The Internet presents its own challenges when it comes to discovering valid and credible information. When looking at a website, you should be able to answer the following questions: Who is responsible for the site (i.e., who is the author)? What can you find out about the responsible party? Where does the site’s information come from (e.g., opinions, facts, documents, quotes, excerpts)? What are the key concepts, issues, and “facts” on the site? And finally, can the key elements of the site be verified by another site or source? In other words, if you want to find some information online, you shouldn’t just Google the topic and then depend on the first website that pops up.

For certain topics and types of information, you may need to dig deeper. Take into account the funding behind a website. Look up the author, and see if they have written anything else and if there are any obvious biases present in that writing. As an example, if you find a website about vaccinations and autism, and this website was put up by a parent group that opposes vaccinations, you have found information that has biases built in from the start. The point of view presented is most likely one-sided, and thus you need to look for more balanced sources to learn if there is in fact some relationship between childhood vaccinations and the onset of autism. This is just an example; you can find sources ranging from reasonably trustworthy to totally untrustworthy on any topic.

Interpretation

Interpretation is the task of drawing inferences from the facts that you collect in your research. It is a search for the broader meaning of your research findings. This is where you try to make sense of what you discovered. In this part of your research, you should discuss the most important knowledge you gained about your topic from your sources. Here is where you go back to your hypothesis and research questions to discuss your findings and whether or not your hypothesis is correct.

Significance

Remember that earlier it was stated, “Life is a series of problems needing solutions.” Consequently, an increased amount of inquiry leads to progress as we continue to expand our knowledge base on a variety of topics. Whatever you find in your research study has significance, as it adds to our knowledge in a particular area. In this section of your writing, it is important to describe the process by which you located your information and then provide advice to other researchers on how to effectively and efficiently find information on this topic. This allows for the continuation of inquiry and the development of more data and knowledge. This is where you communicate to others the new knowledge you discover in your research.

I Did the Research—How Do I Present It?

• How do I communicate my research findings?
• What are the elements of a good oral presentation?
• How do I successfully prepare a visual presentation?

Oral Presentations

When giving an oral presentation, you should pay special attention to voice, body, and attitude. If you take the following tips into consideration, you should do a fine job of conveying your ideas to an audience.

Voice is more than the sum of the noises you make as you speak. Pay attention to inflection, which is the change in pitch or loudness of your voice. You can deliberately use inflection to make a point, to get people’s attention, or to make it very obvious that what you are saying right now is important. You can also change the volume of your voice. Speak too softly, and people will think you are shy or unwilling to share your ideas; speak too loudly, and people will think you are shouting at them. Control your volume to fit the audience.

Some people have a tendency to rush through their presentations. This means they speed up their speech, and the audience has a difficult time following along. Take care to control the speed at which you give a presentation so that everyone can listen comfortably. Also, to add to the comfort of the listeners, it is always nice to use a conversational tone in a presentation.

This includes such components as stance, gesture, and eye contact—in other words, overall body language. How do you stand when you are giving a presentation? Do you move around and fidget? Do you look down at the ground or stare at your note cards? Are you chewing gum or sticking your hands in and out of your pockets nervously? Obviously, you don’t want to do any of these things. Make eye contact as often as possible. Stand in a comfortable manner, but don’t fidget. Use gestures sparingly to make certain points.

Attitude is everything. Your enthusiasm for your presentation will prime the audience. If you are bored by your own words, the audience will be yawning. If you are jazzed by what you have to offer, they will sit up in their seats and listen intently. Also, be interested in your audience. Let them know that you are excited to share your ideas with them because they are worth your effort.

Visual Presentations

You might also think about using technology to make your presentation. Perhaps you will do a slide presentation in addition to orally communicating your ideas to your class or another group. Keep in mind that the best presentations are those with minimal words or pictures on the screen, just enough to illustrate the information conveyed in your oral presentation. Do a search on lecture slides or presentation slides to find myriad suggestions on how to create them effectively. You may also create videos to communicate what you found in your research. Today, there are many different ways to take the information you found and create something memorable with which to share your knowledge.

When you are making a presentation that includes a visual component, pay attention to three elements: design, method, and function.

The design includes such elements as size, shape, color, scale, and contrast. You have a vast array of options for designing a background or structuring the visual part of your presentation, whether online or offline.

The method is how you visually present your ideas. Will it be better to show your ideas by drawing a picture, including a photograph, using clip art, or showing a video? Or will it be more powerful to depict your ideas through a range of colors or shapes? These decisions you make will alter the impact of your presentation. Will you present your ideas literally, as with a photograph, or in the abstract, as in some artistic rendition of an idea? For instance, if you decide to introduce your ideas symbolically, a picture of a pond surrounded by tall trees may be the best way to present the concept of a calm person.

The function is the purpose of the visual part of your presentation. Are you telling a story? Communicating a message? Creating movement for the audience to follow? Summarizing an idea? Motivating people to agree with an idea? Supporting and confirming what you are telling your audience? Knowing the function of the visual element of your presentation will make your decisions about design and method more meaningful and successful.

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Introduction

Observation-driven exploration versus data-driven analyses, embarking on the journey of experimental design, perspectives, acknowledgements, hypothesis-driven quantitative fluorescence microscopy – the importance of reverse-thinking in experimental design.

Competing interests

The authors declare no competing or financial interests.

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Eric C. Wait , Michael A. Reiche , Teng-Leong Chew; Hypothesis-driven quantitative fluorescence microscopy – the importance of reverse-thinking in experimental design. J Cell Sci 1 November 2020; 133 (21): jcs250027. doi: https://doi.org/10.1242/jcs.250027

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One of the challenges in modern fluorescence microscopy is to reconcile the conventional utilization of microscopes as exploratory instruments with their emerging and rapidly expanding role as a quantitative tools. The contribution of microscopy to observational biology will remain enormous owing to the improvements in acquisition speed, imaging depth, resolution and biocompatibility of modern imaging instruments. However, the use of fluorescence microscopy to facilitate the quantitative measurements necessary to challenge hypotheses is a relatively recent concept, made possible by advanced optics, functional imaging probes and rapidly increasing computational power. We argue here that to fully leverage the rapidly evolving application of microscopes in hypothesis-driven biology, we not only need to ensure that images are acquired quantitatively but must also re-evaluate how microscopy-based experiments are designed. In this Opinion, we present a reverse logic that guides the design of quantitative fluorescence microscopy experiments. This unique approach starts from identifying the results that would quantitatively inform the hypothesis and map the process backward to microscope selection. This ensures that the quantitative aspects of testing the hypothesis remain the central focus of the entire experimental design.

Advancements in optical engineering, labeling technologies, and computational capacity have turned fluorescence microscopy into an indispensable tool in the life sciences. Its unique capacity to probe biological questions across a large range of biological length scales has made it a popular tool in cell biology, neurobiology and developmental biology, as well as many other fields of research. Modern microscopy can reveal valuable information on molecular ultrastructure, dynamic biological processes and biological functions. Yet, the appeal of seemingly limitless promises, the myriad of technical details and the rapid development of computational capabilities has also created confusion for many seeking the right combination of imaging tools. As has been previously pointed out by Jonkman and colleagues ( Jonkman et al., 2020 ), biologists can spend considerable time and resources acquiring huge amounts of data without proper planning, only to realize later that the data cannot appropriately address a particular biological question. This usually occurs when the design of a microscopy experiment is not guided by a suitable hypothesis, the experimenter gets side-tracked by new observations or the experiment starts without a design at all. The method proposed here aims to assist the gathering of appropriate data that directly addresses a quantitative hypothesis. The intent is to give the reader a better understanding of the process and potential issues that arise in quantitative experiments.

The importance of fluorescence microscopy lies in its ability to serve both as an exploratory and a quantitative tool. In other words, microscopy has a combined capacity that enables a biologist to both formulate hypotheses based on observation and to perform quantitative measurements to test those hypotheses. For example, one might easily observe the localization of a target protein within a mitochondrial compartment. However, it takes a shift in mindset to design an appropriate experiment capable of quantifying this localization change in response to an oxidative stress. Quantitative measurements, however, can only produce results that directly address a proposed hypothesis when the experiment is designed appropriately. In fact, even an accurate, quantitative set of data that has been generated with the best practices will not necessarily yield biologically meaningful results. An image acquired with a digital detector is inherently a data map – an array of values. While any digital image can be quantified, these measurements are only biologically meaningful when they are pertinent to the hypothesis. Take for example a study that investigates the rates of filopodia extension during cell migration. Data revealing the super-resolved, 3D actin filaments are not sufficient for determining the rate of filopodia extension. However, an experiment that captures the change in location of the filopodial tip will provide the necessary data. In other words, when testing a quantitative hypothesis, informative data are quantitative, but not all quantitative data are informative.

Reliable and informative results require high-quality image data and relevant analyses. Fortunately, there is no shortage of excellent reviews in the literature that offer step-by-step guidance to perform microscopy experiments, from image acquisition to quantitative image analysis ( Berg et al., 2019 ; Jonkman et al., 2014 ; McQuin et al., 2018 ; North, 2006 ; Rueden et al., 2017 ; Swedlow, 2013 ; Van Den Berge et al., 2019 ; Waters, 2009 ; Weigert et al., 2018 ). The task now lies in ensuring that data acquisition and analyses can be translated into biologically meaningful information, capable of challenging a hypothesis. We argue that this must be achieved through rational experimental design.

Designing a hypothesis-driven experiment is a vital step in the overall experimental scheme, but it is often over-simplified and represented by a single step. The conventional workflow of an imaging experiment, as astutely observed by Lee and Kitaoka (2018) , is adapted in Fig. 1 A. In this generalized diagram, the execution of the experiment begins with sample preparation after experimental design. The images are acquired, and the data will then be processed and analyzed – usually followed by several iterations of optimization – before the final results are presented. What is important to note is that experimental design is appropriately singled out as the key first step ( Fig. 1 A). Yet, in stark contrast to the wealth of technical guides, there is a paucity of discussion in the literature on the logic of rational experimental design and how it can be harnessed to successfully perform a hypothesis-driven, quantitative experiment. This is an unfortunate omission, partly due to the difficulty in summarizing a logical scheme that is sufficiently general to be applicable to most biological questions. In this Opinion article, we aim to fill this important gap and focus on rational, hypothesis-driven experimental design. This guide is aimed toward biologists interested in learning how to design quantitative experiments that are geared toward testing their hypotheses. It embodies our experience in steering imaging projects from hypotheses to quantitative, informative results at the Advanced Imaging Center at HHMI Janelia Research Campus ( Chew et al., 2017 ). We include in Box 1 a case study of how we have successfully steered the development of such a quantitative microscopy project.

Conducting and designing quantitative fluorescence microscopy experiments . (A) Typical workflow in microscopy experiment. This workflow is forward-facing, progressing from the formulation of a hypothesis to the eventual presentation of the data as results. Adapted with permission of American Society for Cell Biology from Lee and Kitaoka (2018) ; permission conveyed through Copyright Clearance Center, Inc. (B) A focused view of the experimental planning phase. We propose that experimental design would be more efficient and effective by adopting a reverse-facing workflow. Here, the hypothesis should determine what the necessary results should be. From there, the experimenter can plan backward from the required data to the point where the experiment can be executed. The processes outlined in A and B are iterative, and the experimenter should re-evaluate whether the best decision has been made at each step. (C) A flow diagram to determine whether the experimental output generated from the microscope will lead to informative results. Answering the questions outlined here will identify the corresponding step in the design that needs re-evaluation. Reaching the ‘Informative results’ box would indicate that the data acquired were most likely collected in a manner that would directly test the hypothesis. Alternatively, the bulleted lists provide insight into which step in the design process requires re-evaluation to be improved in subsequent design iterations.

This case study partially summarizes one of the quantitative experiments performed by McArthur and colleagues ( McArthur et al., 2018 ). Preliminary observations indicated that the mitochondrial network of cells deficient in induced myeloid leukemia cell differentiation protein (MCL-1), a Bcl-2 family member, broke down during apoptosis (A in the box figure), followed by the presence of mitochondrial DNA (mtDNA) in the cytoplasm (B in the box figure). This observation led to the conceptualization of the working model – ‘during apoptosis, mitochondrial morphology changes prior to the release of mtDNA into the cytoplasm’.

To properly plan a quantitative experiment to test this model, we used our reverse-logic to steer the following experimental design:

1. A more-defined hypothesis was formulated – ‘during apoptosis, the mitochondrial sphericity increases prior to an increase in the number of externalized mtDNA’. Note how the initial descriptive semantics have been translated into quantitative semantics that will guide subsequent measurements.

2. Two sets of informative results were essential to test this hypothesis: (i) mitochondrial sphericity, and (ii) mtDNA externalization, both measured as a function of time.

3. To achieve these informative results, the required data must include time-lapsed, volumetric images of labeled mitochondria and mtDNA.

4. To produce these data, the following experimental imaging parameters had to be met:

• high-speed volumetric imaging to accurately track 3D mitochondrial network reorganization

• high signal-to-noise ratio and resolution in order to accurately measure the 3D structures of the mitochondria

• near-isotropic resolution to precisely characterize the sphericity of mitochondria and mtDNA extrusion

• two-color acquisition to provide information on both the mitochondria and the mtDNA.

5. While both lattice lightsheet microscopy (LLSM) and 3D structured illumination microscopy (SIM) met these benchmarks, it was also important to meet the biological requirements. Pilot studies established that two-channel volumes of 50 slices each, acquired approximately every 10 s for a total of 50 min would be necessary to capture and follow this rare process in its entirety. Photoxicity could affect the mitochondrial biology, introducing artifacts. To mitigate phototoxicity, the gentle illumination of LLSM established it as the clear choice. To further reduce light exposure, brighter fluorescent labels, such as mNeonGreen ( Shaner et al., 2013 ) and HaloTag™ (Promega, USA) with Janelia Fluor ® 646 ( Grimm et al., 2017 ) (instead of EGFP and mCherry), were used. Note that the experimental design process was iterative and benefited from pilot studies used to identify the necessary imaging parameters, suitable fluorophores, and the optimal microscope.

C to E in the box figure illustrate the successful completion of this quantitative experiment. The LLSM micrograph (C) shows mtDNA extrusion from mitochondria. These images were used to create 3D segmentations (D) and were quantified. The mitochondrial sphericity and mtDNA externalization were measured over time, and plotted in E. This graph shows that an increase in mitochondrial sphericity (thin red line) preceded the onset of mtDNA extrusion (thin green line) – providing the informative result that ultimately supported the hypothesis.

The box figure shows morphological changes of mitochondria and mitochondrial DNA release during apoptosis; images were previously published in McArthur et al. (2018) and are reused here with permission. Scale bars: 5 µm.

The success of a microscopy-based quantitative experiment hinges on the appreciation and understanding of (i) how the underlying biological query and defined hypothesis directs the experimental design, and (ii) how experimental design and instrument choice are related to the way in which image data will eventually be analyzed. For this reason, we outline a logic that exemplifies these themes ( Fig. 1 B). We propose, in this Opinion article, that the very first step of experimental design, following the formulation of a hypothesis, is to determine the informative results that can quantitatively test that hypothesis. In other words, informative results are the ultimate goal of the designed experiment. Therefore, an experiment that has been developed to specifically generate data pertinent to the biological query will produce informative results. As such, the production of the required data will necessitate that a certain set of experimental parameters be met, which would in turn prescribe the features of the instrument needed to make such measurements. Overall, such a systematic workflow ensures that the hypothesis remains central to the experiment and that the experiment yields information capable of challenging the hypothesis. This will help chart the roadmap of how microscopy-based experiments should be designed for quantitative analyses. We will not replicate the many superbly written reviews and guides in the literature here, but rather aim to help readers better utilize these guides, as we embark on our journey of experimental design.

The capacity of modern optical microscopy to support both visual exploration and content-rich measurement has made it a versatile biological research technique. Unfortunately, it is also one that is commonly misunderstood. Biologists are keen observers, exceptional in recognizing patterns, finding anomalies and identifying new phenotypes. In fact, when it comes to studying structures and processes, visualization by itself is often sufficient to prompt biologists to formulate working models of the observed systems, and these working models provide abstract representations of the observation. The descriptive semantics used in these working models have served as powerful tools in life sciences and enable biologists to organize and communicate information about the complexity of the living systems ( Courtot et al., 2011 ). Indeed, specific follow-up questions can often already be framed by experienced biologists as soon as the initial images appear on their monitor; and this is the inception point of many biological queries. This is the essence of observation-driven, empirical inferences – ‘I know it when I see it’, and this is where the power of microscopy has historically been leveraged. Observational biology will continue to play an important role, and it is certainly true that not all biological hypotheses must be quantitatively tested. However, there is no denying that with the advent of modern experimental methods, hypotheses in general have become, and are increasingly expected to be, formulated in more quantitative terms. Addressing these increasingly focused hypotheses is where the quantitative capacity of microscopy has the most impact and is the core of this Opinion article.

If one were to accept the idea that ‘seeing is believing’ with microscopy as an exploratory instrument, then surely one must also accept the notion that ‘measuring is knowing’ when using microscopy as an analytical technique. The challenge here is to reconcile observation and quantification using the same instrument. Quantitative measurement is intrinsically analysis-rich and semantics-agnostic ( Shasha, 2003 ). However, this is where the disparity between observation and quantification often arises. It is common to see proposed microscopy studies with phrases such as ‘to analyze the spatial-temporal dynamics of an organelle’. There is unfortunately no specific analytical metric for the ‘dynamics’ of an organelle or any other biological structure. Dynamics is an ambiguous term that is often used to encapsulate several different metrics that together describe a particular observation. To transform vague biological queries such as this into quantifiable goals for microscopic analysis, we need to consider how intuitive biological semantics can be reformulated. With this in mind, we will begin by exploring how hypotheses shape the rationale of microscopy-based experiments.

Testable hypothesis

The cornerstone of the classical scientific method is to determine whether evidence supports or negates a postulated idea. Hypotheses, at the experimental level, must therefore be negatable by observation or measurements ( Popper, 2005 ). A clearly stated, verifiable hypothesis will guide every step of an experiment and will provide invaluable checkpoints. More importantly, the negatable hypothesis will impart the necessary restraint to mitigate being side-tracked from the initial question. This disciplined approach does not preclude future exploration of other observations, but it serves to balance both the exploratory and the analytical priorities of an experiment ( Fig. 1 C). This is why a hypothesis such as ‘condition X will increase the rate of mitochondrial fission’ has stronger semantic specificity than ‘condition X will affect the spatial-temporal dynamics of mitochondria’. The latter hypothesis cannot be tested because the experimental variables (i.e. fission events) that either support or negate it are not defined.

Interestingly, such cautionary advice is rarely needed for biochemical and molecular biology assays. These are assays that are uniquely quantitative and do not usually serve as observational tools, and biologists learn these techniques extensively during their training. As a result, biologists formulate testable hypotheses and perform quantitative analyses with ease using assays such as immunoblots, PCRs, ELISAs or enzyme kinetic assays. What differentiates these assays from microscopy is that they are explicitly linked to well-defined sets of output. For example, an immunoblot yields specific information on molecular mass and abundance. In contrast, a vast plethora of information can be derived from microscopy data, including molecular abundance, spatial location, movement behavior, morphological changes, structural features, molecular association, enzymatic activity, and the list goes on. Microscopy is therefore not a single assay; instead, it is a collection of assays that vary depending on how the experiment is designed. Without a defined boundary, the scope of an experiment can quickly become too ambitious and unnecessarily complex. This underscores the importance of identifying the appropriate experimental output that addresses the hypothesis early in the design process.

Compared to biochemical and molecular biology assays, the complexity of microscopy is further compounded by the variability in the nature of the sample. In comparison to molecular biology assays that use defined samples for input, such as nucleic acids or proteins, microscopy can accommodate a wide variety of complex samples (from purified molecules to a multitude of model organisms at various stages of development, for example) that in turn change the requirements and implementation of the experiment. Thus, it does not come as a surprise that the experimental scheme and sample choice often have to be considered in parallel due to their interdependencies ( Galas et al., 2018 ). Sample compatibility is a complex issue that comprises both the specimen and fluorescent labels. Likewise, the labeling strategy and sample viability are critically important factors to the success of an experiment, and these topics have been extensively discussed in the literature ( Albrecht and Oliver, 2018 ; Dean and Palmer, 2014 ; Frigault et al., 2009 ; Heppert et al., 2016 ; Icha et al., 2017 ; Kiepas et al., 2020 ; Lambert, 2019 ; Schneider and Hackenberger, 2017 ; Specht et al., 2017 ; Thorn, 2017 ). Overall, the compatibility of a sample will be determined by all aspects of the experiment and demands careful consideration. As a result, the hypothesis and the associated experiment will be heavily influenced by what can be realistically achieved given the nature of the sample. Once the hypothesis has been appropriately defined, rather than proceeding directly to performing microscopy experiments, the most critical step at this point is to evaluate what it means to challenge the hypothesis.

Informative results

Not all results can adequately test a hypothesis. It is important to differentiate between a ‘desired outcome’ and an ‘informative result’. The desired outcome would naturally be for the evidence to support the hypothesis. Continuing with the example of mitochondrial fission stated above, the informative result in this case would be the number of mitochondrial fission events as a function of time, both in the presence and absence of condition X. This is in contrast to the ‘desired outcome’ of finding an increased rate of mitochondrial fission given condition X. In addition, to be informative, the required data should encompass appropriate controls and sufficient replicates to support statistical analyses. The informative result is not designed to affirm one's intuition; it is required to support or negate the hypothesis.

Required data

As depicted in Fig. 1 B, experimental design involves a reverse-thinking workflow that begins with informative results and concludes with microscope choice. This reverse-flow provides the necessary logic for designing a quantitative experiment. The essence of efficient experimental design is to home in on the appropriate assay from the multitude of possibilities offered by fluorescence microscopy. It is therefore imperative that the experimenter identifies what the necessary data are, as this will ultimately define the appropriate assay. This underscores the importance of thinking in reverse, as the necessary data can only be defined by informative results. While results and data are sometimes used interchangeably elsewhere, they are distinctly different in this context. Results refer to the final analytical metrics compiled from a set of related experiments. In contrast, a set of data generated from the microscope, by itself, is insufficient to speak to the validity of a hypothesis.

The transition from data to results requires certain translational steps. A good example of such translation is the process of connecting coordinates of a moving object, be it a cell or particle, between time points into a defined track. Without further analyses, the tracked data of a moving object is only minimally informative; it merely indicates that the object has moved. If one were to hypothesize that the object would change its migratory behavior under certain conditions, then one would need to consider which measurements could describe that behavior. These informative measurements, when performed on the data, are referred to as the analytical metrics. In this example of characterizing migration patterns, the analytical metrics may include directionality, velocity and motion persistence ( Aaron et al., 2019 ). Informative results are produced when these analytical metrics are applied to the appropriate data .

Adhering to our reverse-design approach, the types of analytical metrics that will lead to the informative results are the next factor an experimenter must consider. Table 1 shows how common biological objectives dictate the relevant analytical metrics, which in turn prescribe the necessary experimental tools. Analytical metrics is a form of semantics. What sets it apart from the semantics used in working models is that, in analytical metrics, the semantics are quantitative and specific rather than descriptive. What should be clear from Table 1 is that careful consideration is required to choose the appropriate analytical metrics. In fact, as reflected in the mitochondrial fission example, analytical metrics (mitochondrial fission rate) should be central to the hypothesis, so that it can be tested. An additional example where the choice of the correct analytical metric would affect the results is in colocalization studies. One must first determine whether measuring the degree of overlap (co-occurrence) of the two signals is more appropriate than measuring the extent of their correlation. This decision will dictate the analytical metric that should be used ( Aaron et al., 2018 ). Likewise, if a certain treatment is postulated to increase the dissemination of cancer cells from a cell cluster, it is important, from a mechanistic standpoint, to properly frame the testable hypothesis. This can be accomplished by avoiding vague descriptions such as ‘dissemination’ and instead frame the descriptor in quantitative terms, such as velocity, directionality and persistence of the cellular movement ( Aaron et al., 2019 ). This is how descriptive semantics should be translated into quantitative semantics, thereby enabling the underlying biology to be measured.

Selecting analytical metrics based on biological questions

Interestingly, and perhaps ironically, many of the analytical metrics listed in Table 1 , such as velocity, directionality, or curvature, collectively describe ‘spatial-temporal dynamics’. Yet, owing to various limitations of individual microscope design, it is impossible to capture them all in one experiment (see the section on microscope selection below). Similarly, it is often counter-productive to acquire more data than one needs, as this complicates data analysis and also compounds the problem of data storage ( Andreev and Koo, 2020 ). Added complexity can lead to the experimenter being side-tracked from the original goal and makes data interpretation more difficult. Fig. 1 C shows how the iterative evaluation of the experimental output will ensure that these readouts stay pertinent to the hypothesis and allow room for observational biology to take place. Parsimonious selection of analytical metrics will focus the scope of the experiment, generating data that can test the hypothesis. However, the well-considered selection of analytical metrics only fulfills half of the data requirement. One also needs to consider the validity of the data. In other words, how to ensure that the data set is accurate and reproducible.

Accuracy and reproducibility together describe the rigor of the experiment. While highly related, it is possible that accurate data are not reproducible, and reproducibility does not ensure accuracy ( Payne-Tobin Jost and Waters, 2019 ). Too often, the accuracy and reproducibility of microscopy data is only an afterthought, which can potentially jeopardize an entire experiment. There are two places in which rigor can be compromised: during data generation and in the experimental design. Great care should be taken to ensure unbiased sampling, appropriate use of standards and controls, uniform instrument performance and consistent data processing pipelines. In this light, preserving accuracy and reproducibility during image acquisition has been extensively covered ( Jonkman, 2020 ; McQuin et al., 2018 ; Payne-Tobin Jost and Waters, 2019 ), and is beyond the scope of our discussion. Nevertheless, this is extremely important advice and should be followed closely.

However, identifying the appropriate constraints for a rigorous experimental design can be equally challenging. How experimental controls and baselines are chosen can alter the data and the results, and therefore cannot be taken lightly as it can skew data interpretation. In stark contrast to physics, in which absolute numbers of various universal constants can be mathematically derived, biology is a comparative science. In biology, it is the change of experimental readouts in response to a modification of the experimental variables that is the important factor. As previously mentioned, modern microscopes will always generate quantifiable data because a digital image is intrinsically a data map. However, not all quantifiable digital images are meaningful. An absolute number derived from a colocalization experiment (for example, a calculated Pearson's correlation coefficient of 0.75) between two proteins is quantitative, but utterly meaningless as a stand-alone piece of data. It has to be compared to controls to become biologically informative – has the Pearson's coefficient changed in response to a variation in the experimental condition? The importance of establishing an experimental baseline for comparison cannot be overstated. Owing to our inherent tendency to look for the desired outcome, experimental bias occurs in the absence of a rigorous baseline. Validation of an experimental pipeline will ensure the measurements accurately represent the biological truth. This can be achieved by the effective use of controls and standards ( Payne-Tobin Jost and Waters, 2019 ). While this sounds cliché, we found that comparative baselines are often forgotten. By articulating the necessary controls for a given hypothesis, the underlying nature of the experiment can become more apparent. This in turn can be used to refine the hypothesis and home in on what the biologist seeks to test. Stringent controls will indeed make for better experiments.

When an experiment is driven by a hypothesis, the hypothesis itself will define the requirements of the experiment. These, in turn, will define the parameters that subsequently circumscribe the rest of the microscopy assay. The key parameters in any microscopy experiment will include one or more of the following: (i) lateral and axial spatial resolution, (ii) temporal resolution, (iii) tolerance to phototoxicity and photobleaching, (iv) field of view, (v) imaging depth, (vi) multiplexing capacity to acquire a combination of colors, and (vii) spectroscopic imaging capabilities. In a perfect world, a microscope will encompass all these parameters. Unfortunately, in reality, such a microscope does not exist as every microscope requires trade-offs ( Combs, 2010 ; Lemon and McDole, 2020 ; Scherf and Huisken, 2015 ; Schermelleh et al., 2010 ). Occasionally, the trade-off can come at an exorbitant price, and this is especially the case with super-resolution microscopy. To gain the extra resolution, these modalities either completely sacrifice the capacity to image live phenomena or incur unacceptable doses of illumination light that rapidly induces phototoxicity ( Schermelleh et al., 2019 ). Thus, the trade-off of an otherwise suitable microscope may render it incapable of producing the required data.

In order to avoid such situations, it is best to understand what needs to be captured by the microscope before selecting an instrument. This can be achieved by changing the ambiguous, descriptive semantics (e.g. ‘membrane 3D dynamics’) to those that are framed in the semantics of analytical metrics (e.g. ‘filopodial angular deflection’, ‘membrane surface curvature’) (see Table 1 ). By identifying the necessary metrics, the required imaging parameters can be prioritized. For example, the analytical metrics required to sufficiently measure the 3D membrane ruffles of a cell ( Fritz-Laylin et al., 2017 ) include angular deflection, surface curvature, volumetric changes and the turnover rate of these membranous structures. These metrics will mandate the following imaging parameters: (i) high volumetric imaging speed (multiple volumes per min); (ii) improved axial resolution producing near or true isotropic resolution in all three axes, so that the ruffling structures can be resolved and segmented accurately; (iii) gentle illumination to minimize phototoxicity; (iv) live-cell-compatible imaging; and (iv) labeling of the cell membrane that is capable of facilitating the high number of image acquisitions. Box 1 also provides a case study of how analytical metrics influence microscope choice. Specific analytical metrics do not preclude the experimenter from observing (and even exploring) the biology; instead, they help winnow the imaging parameters down to the bare essentials. Together, quantitative metrics and experimental parameters will guide the user to the optimal microscope(s).

Microscope selection

The task of microscope selection can be bewildering to novices, and at times is confusing to even experienced microscopists. Biologists often face multiple hurdles in identifying suitable microscopes for an experiment through no fault of their own. These include (i) the lack of access to the desired instrument, (ii) ill-informed demand from reviewers to use the latest technology in the name of innovation, (iii) over-promise of instrument capabilities from the manufacturers, (iv) under-reporting of the instrument limitations, and (v) insufficient or erroneous reporting of published results that render experimental conditions irreproducible. Table 2 summarizes the features of various commonly used microscope modalities, as well as their relative advantages and shortcomings in our experience. Biologists have access to a wide range of modalities beyond standard widefield epifluorescence microscopes: total internal reflection fluorescence microscopy ( Mattheyses et al., 2010 ), lightsheet microscopy ( Chatterjee et al., 2018 ; Chen et al., 2014 ; Power and Huisken, 2017 ), confocal microscopy ( Claxton et al., 2011 ; Conchello and Lichtman, 2005 ; Jonkman et al., 2020 ; Oreopoulos et al., 2014 ), two-photon excitation fluorescence microscopy ( Benninger and Piston, 2013 ; So et al., 2000 ) and image scanning microscopy ( Gregor and Enderlein, 2019 ), as well as super-resolution techniques ( Demmerle et al., 2017 ; Sahl et al., 2017 ; Schermelleh et al., 2019 ; Sydor et al., 2015 ; Vicidomini et al., 2018 ). What should be immediately obvious from their comparison is that there is no ‘winner’ or ‘loser’ ( Table 2 ). No microscope scores equally well or poorly across the various parameters, reinforcing the notion that every microscope compromises a combination of parameters in order to excel at others. As a result, the process of microscope selection is rarely linear. Many instruments have overlapping capabilities that obscure the selection process and will require that more than one instrument be considered at a time. By defining the required parameters beforehand, they can be used to filter the selection down to the most appropriate instrument(s), as exemplified in the case study presented in Box 1 . The process of microscope selection is aided by a good understanding of the necessary imaging parameters. Ultimately, the justification for an instrument lies solely on the ability of that microscope to provide the necessary analytical metrics and the data informative of the biology.

Performance comparison of various microscope modalities

It is impractical to expect biologists to understand the myriad of technical nuances of these rapidly evolving technologies. Likewise, most advanced imaging systems are usually concentrated in shared microscopy facilities, managed by experienced microscopists. This makes it all the more important for biologists to communicate, precisely and concisely, the desired analytical metrics and the corresponding parameters required for a successful experiment. It is sometimes difficult to appreciate that the latest imaging technology is not always the most appropriate. A super-resolution microscope or advanced lightsheet microscope may not necessarily be more suitable than a widefield epifluorescence microscope for a particular experiment. A microscope can only enhance certain parameters, and it is only beneficial if the enhanced parameters are utilized wisely. Even though structured illumination microscopy (SIM) offers improved resolution (see Table 2 ), it does not enhance the data of cell tracking studies over what can be achieved with a standard epifluorescence widefield microscope. It is also important to note that sometimes no single existing imaging technology may be able to produce the required data, necessitating the use of multiple instruments, or even the modification of the testable hypothesis. However, the availability of a new technology can open up the possibility of previously unfeasible analytical metrics that make it possible to address different biological queries.

The microcopy literature has no shortage of excellent reviews on the technical aspects of various imaging modalities, as well as tutorials on how to generate quantitative and reproducible data. However, topical discussion of best practices and optics does not necessarily engender a coherent framework of how these sets of information can be integrated to facilitate a hypothesis-driven, quantitative experimental design. Here, we present not only a roadmap of how to use these guides in the literature, but we break with the convention and argue that microscopy-based quantitative experiments should be designed in reverse, starting with determining the informative results needed to challenge a hypothesis.

Despite the promises of the latest technologies, no microscope is perfect. Usually, a feature gained in a technique comes at the cost of other key parameters. The essence of experimental design is never about the inclusion of every parameter the experimenter wants; rather it is about the careful exclusion of unnecessary parameters. This will allow accurate measurements to be performed and will ensure that the parameters relevant to the information the experimenter needs are maintained. This is the core concept of our approach. The essential parameters must be determined by what is required to test a hypothesis. These parameters will, in turn, naturally shape the rest of the elements of an experimental pipeline ( Fig. 1 A). A hypothesis-driven experimental design must be just that – driven by the hypothesis. It should be based on the biological question at hand, and not by the lure of the latest technologies. Fortunately, this process is an iterative feedback loop. The key questions left unanswered due to lack of technology inspire the development of novel microscopes. New technologies then reciprocally inform biology so that new hypotheses can be formulated. This cyclical process, however, does not negate the fact that experiments should be framed within the confines of existing technologies.

This Opinion article does not, by any means, diminish the exploratory power of microscopes and the well-honed acumen of biologists to observe and deduce. On the contrary, most hypotheses are synthesized following keen observation. The scope of this discussion is to focus on the process of quantitatively verifying a hypothesis. We have not addressed here how the power of modern microscopy has been harnessed in big-data scientific exploration. Such experiments are usually hypothesis-free; instead machine-learning algorithms are employed to search for patterns beyond what human perception can efficiently discern ( Chessel and Carazo Salas, 2019 ; Piccinini et al., 2017 ).

Quantitative microscopy experiments are not easy to design, as they require knowledge at the confluence of optics, imaging probes, data analysis and how the biological samples interact with the microscope. It is therefore of paramount importance for biologists to seek and heed the advice of expert microscopists and data scientists, especially those in core facilities, who are experienced in the application of microscopy. The conventional practice of generating a lot of data first, followed by data analysis as a secondary consideration should be avoided. Microscopy-related experiments demand careful planning and continued, iterative evaluation before the optimal approaches can be implemented. The fact that this message is echoed in every review and guide cited here is because it is important, and unfortunately, because it is also commonly overlooked. The perils of ignoring it cannot be overstated.

We thank Dr Christopher Obara as well as the members of the Advanced Imaging Center for their thoughtful discussion and insightful contributions.

The Advanced Imaging Center at Janelia Research Campus is generously supported by the Howard Hughes Medical Institute and the Gordon and Betty Moore Foundation.

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• Open access
• Published: 24 February 2024

Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level

• Monika Teuber 1 ,
• Daniel Leyhr 1 , 2 &
• Gorden Sudeck 1 , 3  

BMC Public Health volume  24 , Article number:  598 ( 2024 ) Cite this article

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Physical activity has been proven to be beneficial for physical and psychological health as well as for academic achievement. However, especially university students are insufficiently physically active because of difficulties in time management regarding study, work, and social demands. As they are at a crucial life stage, it is of interest how physical activity affects university students' stress load and recovery as well as their academic performance.

Student´s behavior during home studying in times of COVID-19 was examined longitudinally on a daily basis during a ten-day study period ( N  = 57, aged M  = 23.5 years, SD  = 2.8, studying between the 1st to 13th semester ( M  = 5.8, SD  = 4.1)). Two-level regression models were conducted to predict daily variations in stress load, recovery and perceived academic performance depending on leisure-time physical activity and short physical activity breaks during studying periods. Parameters of the individual home studying behavior were also taken into account as covariates.

While physical activity breaks only positively affect stress load (functional stress b = 0.032, p  < 0.01) and perceived academic performance (b = 0.121, p  < 0.001), leisure-time physical activity affects parameters of stress load (functional stress: b = 0.003, p  < 0.001, dysfunctional stress: b = -0.002, p  < 0.01), recovery experience (b = -0.003, p  < 0.001) and perceived academic performance (b = 0.012, p  < 0.001). Home study behavior regarding the number of breaks and longest stretch of time also shows associations with recovery experience and perceived academic performance.

Conclusions

Study results confirm the importance of different physical activities for university students` stress load, recovery experience and perceived academic performance in home studying periods. Universities should promote physical activity to keep their students healthy and capable of performing well in academic study: On the one hand, they can offer opportunities to be physically active in leisure time. On the other hand, they can support physical activity breaks during the learning process and in the immediate location of study.

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Introduction

Physical activity (PA) takes a particularly key position in health promotion and prevention. It reduces risks for several diseases, overweight, and all-cause mortality [ 1 ] and is beneficial for physical, psychological and social health [ 2 , 3 , 4 , 5 ] as well as for academic achievement [ 6 , 7 ]. However, PA levels decrease from childhood through adolescence and into adulthood [ 8 , 9 , 10 ]. Especially university students are insufficiently physically active according to health-oriented PA guidelines [ 11 ] because of academic workloads as well as difficulties in time management regarding study, work, and social demands [ 12 ]. Due to their independence and increasing self-responsibility, university students are at a crucial life stage. In this essential and still educational stage of the students´ development, it is important to study their PA behavior. Furthermore, PA as health behavior represents one influencing factor which is considered in the analytical framework of the impact of health and health behaviors on educational outcomes which was developed by the authors Suhrcke and de Paz Nieves [ 13 , 14 ]. In light of this, the present study examines how PA affects university students' academic situations.

Along with the promotion of PA, the reduction of sedentary behavior has also become a crucial part of modern health promotion and prevention strategies. Spending too much time sitting increases many health risks, including the risk of obesity [ 15 ], diabetes [ 16 ] and other chronic diseases [ 15 ], damage to muscular balances, bone metabolism and musculoskeletal system [ 17 ] and even early death [ 15 ]. University students are a population that has shown the greatest increase in sedentary behavior over the last two decades [ 18 ]. In Germany, they show the highest percentage of sitting time among all working professional groups [ 19 ]. Long times sitting in classes, self-study learning, and through smartphone use, all of which are connected to the university setting and its associated behaviors, might be the cause of this [ 20 , 21 ]. This goes along with technological advances which allow students to study in the comfort of their own homes without changing locations [ 22 ].

To counter a sedentary lifestyle, PA is crucial. In addition to its physical health advantages, PA is essential for coping with the intellectual and stress-related demands of academic life. PA shows positive associations with stress load and academic performance. It is positively associated with learning and educational success [ 6 ] and even shows stress-regulatory potential [ 23 ]. In contrast, sedentary behavior is associated with lower cognitive performance [ 24 ]. Moreover, theoretical derivations show that too much sitting could have a negative impact on brain health and diminish the positive effects of PA [ 16 ]. Given the theoretical background of the stressor detachment model [ 25 ] and the cybernetic approach to stress management in the workplace [ 26 ], PA can promote recovery experience, it can enhance academic performance, and it is a way to reduce the impact of study-related stressors on strain. Load-related stress response can be bilateral: On the one hand, it can be functional if it is beneficial to help cope with the study demands. On the other hand, it can be dysfunctional if it puts a strain on personal resources and can lead to load-related states of strain [ 27 ]. Thus, both, the promotion of PA and reduction of sedentary behavior are important for stress load, recovery, and performance in student life, which can be of particular importance for students in an academic context.

A simple but (presumably) effective way to integrate PA and reduce sedentary behavior in student life are short PA breaks. Due to the exercises' simplicity and short duration, students can perform them wherever they are — together in a lecture or alone at home. Short PA breaks could prevent an accumulation of negative stressors during the day and can help with prolonged sitting as well as inactivity. Especially in the university setting, evidence of the positive effects of PA breaks exists for self-perceived physical and psychological well-being of the university students [ 28 ]. PA breaks buffer university students’ perceived stress [ 29 ] and show positive impacts on recovery need [ 30 ] and better mood ratings [ 31 , 32 ]. In addition, there is evidence for reduction in tension [ 30 ], overall muscular discomfort [ 33 ], daytime sleepiness or fatigue [ 33 , 34 ] and increase in vigor [ 34 ] and experienced energy [ 30 ]. This is in line with cognitive, affective, behavioral, and biological effects of PA, all categorized as palliative-regenerative coping strategies, which addresses the consequences of stress-generating appraisal processes aiming to alleviate these consequences (palliative) or restore the baseline of the relevant reaction parameter (regenerative) [ 35 , 36 ]. This is achieved by, for example, reducing stress-induced cortisol release or tension through physical activity (reaction reduction) [ 35 ]. Such mechanisms are also in accordance with the previously mentioned stressor detachment model [ 25 ]. Lastly, there is a health-strengthening effect that impacts the entire stress-coping-health process, relying on the compensatory effects of PA which is in accordance to the stress-buffering effect of exercise [ 37 ]. Health, in turn, effects educational outcomes [ 13 , 14 ]. Therefore, stress regulating effects are also accompanied with the before mentioned analytical framework of the impact of health and health behaviors on educational outcomes [ 13 , 14 ].

Focusing on the effects of PA, this study is guided by an inquiry into how PA affects university students' stress load and recovery as well as their perceived academic performance. For that reason, the student´s behavior during home studying in times of COVID-19 is examined, a time in which reinforced prolonged sitting, inactivity, and a negative stress load response was at a high [ 38 , 39 , 40 , 41 , 42 ]. Looking separately on the relation of PA with different parameters based on the mentioned evidence, we assume that PA has a positive impact on stress load, recovery, and perceived academic performance-related parameters. Furthermore, a side effect of the home study behavior on the mentioned parameters is assumed regarding the accumulation of negative stressors during home studying. These associations are presented in Fig.  1 and summarized in the following hypotheses:

Overview of the assumed effects and investigated hypotheses of physical activity (PA) behavior on variables of stress load and recovery and perceived academic performance-related parameters

Hypothesis 1 (path 1): Given that stress load always occurs as a duality—beneficial if it is functional for coping, or exhausting if it puts a strain on personal resources [ 27 ] – we consider two variables for stress load: functional stress and dysfunctional stress. In order to reduce the length of the daily surveys, we focused the measure of recovery only on the most obvious and accessible component of recovery experience, namely psychological detachment. PA (whether performed in leisure-time or during PA breaks) encourages functional stress and reduce dysfunctional stress (1.A) and has a positive effect on recovery experience through psychological detachment (1.B).

Hypothesis 2 (path 2): The academic performance-related parameters attention difficulties and study ability are positively influenced by PA (whether done in leisure-time or during PA breaks). We have chosen to assess attention difficulties for a cognitive parameter because poor control over the stream of occurring stimuli have been associated with impairment in executive functions or academic failure [ 43 , 44 , 45 , 46 ]. Furthermore, we have assessed the study ability to refer to the self-perceived feeling of functionality regarding the demands of students. PA reduces self-reported attention difficulties (2.A) and improves perceived study ability, indicating that a student feels capable of performing well in academic study (2.B).

Hypothesis 3: We assume that a longer time spent on studying at home (so called home studying) could result in higher accumulation of stressors throughout the day which could elicit immediate stress responses, while breaks in general could reduce the influence of work-related stressors on strain and well-being [ 47 , 48 ]. Therefore, the following covariates are considered for secondary effects:

the daily longest stretch of time without a break spent on home studying

the daily number of breaks during home studying

Study setting

The study was carried out during the COVID-19 pandemic containment phase. It took place in the middle of the lecture period between 25th of November and 4th of December 2020. Student life was characterized by home studying and digital learning. A so called “digital semester” was in effect at the University of Tübingen when the study took place. Hence, courses were mainly taught online (e.g., live or via a recorded lecture). Other events and actions at the university were not permitted. As such, the university sports department closed in-person sports activities. For leisure time in general, there were contact restrictions (social distancing), the performance of sports activities in groups was not permitted, and sports facilities were closed.

Thus, the university sports department of the University of Tübingen launched various online sports courses and the student health management introduced an opportunity for a new digital form of PA breaks. This opportunity provided PA breaks via videos with guided physical exercises and health-promoting explanations for a PA break for everyday home studying: the so called “Bewegungssnack digital” [in English “exercise snack digital” (ESD)] [ 49 ]. The ESD videos took 5–7 min and were categorized into three thematic foci: activation, relaxation, and coordination. Exercises were demonstrated by one or two student exercise leaders, accompanied by textual descriptions of the relevant execution features of each exercise.

Participants

Participants were recruited within the framework of an intervention study, which was conducted to investigate whether a digital nudging intervention has a beneficial effect on taking PA breaks during home study periods [ 49 ]. Students at the University of Tübingen which counts 27,532 enrolled students were approached for participation through a variety of digital means: via an email sent to those who registered for ESD course on the homepage of the university sports department and to all students via the university email distribution list; via advertisement on social media of the university sports department (Facebook, Instagram, YouTube, homepage). Five tablets, two smart watches, and one iPad were raffled off to participants who engaged actively during the full study period in an effort to motivate them to stick with it to the end. In any case, participants knew that the study was voluntary and that they would not suffer any personal disadvantages should they opt out. There was a written informed consent prompt together with a prompt for the approval of the data protection regulations immediately within the first questionnaire (T0) presented in a mandatory selection field. Positive ethical approval for the study was given by the first author´s institution´s ethics committee of the faculty of the University of Tübingen.

Participants ( N  = 57) who completed the daily surveys on at least half of the days of the study period, were included in the sample (male = 6, female = 47, diverse = 1, not stated = 3). As not all subjects provided data on all ten study days, the total number of observations was between 468 and 540, depending on the variable under study (see Table  1 ). The average number of observations per subject was around eight. Their age was between 18 and 32 years ( M  = 23.52, SD  = 2.81) and they were studying between the 1st to 13th semester ( M  = 5.76, SD  = 4.11) within the following major courses of study: mathematical-scientific majors (34.0%), social science majors (22.6%), philosophical majors (18.9%), medicine (13.2%), theology (5.7%), economics (3.8%), or law (1.9%). 20.4% of the students had on-site classroom teaching on university campus for at least one day a week despite the mandated digital semester, as there were exceptions for special forms of teaching.

Design and procedures

To examine these hypothesized associations, a longitudinal study design with daily surveys was chosen following the suggestion of the day-level study of Feuerhahn et al. (2014) and also of Sonnentag (2001) measuring recovery potential of (exercise) activities during leisure time [ 50 , 51 ]. Considering that there are also differences between people at the beginning of the study period, initial base-line value variables respective to the outcomes measured before the study period were considered as independent covariates. Therefore, the well-being at baseline serves as a control for stress load (2.A), the psychological detachment at baseline serves as a control for daily psychological detachment (2.B), the perception of study demands serves as a control for self-reported attention difficulties (1.A), and the perceived study ability at baseline serves as a control for daily study ability (2.B).

Subjects were asked to continue with their normal home study routine and additionally perform ESD at any time in their daily routine. Data were collected one to two days before (T0) as well as daily during the ten-day study period (Wednesday to Friday). The daily surveys (t 1 -t 10 ) were sent by email at 7 p.m. every evening. Each day, subjects were asked to answer questions about their home studying behavior, study related requirements, recovery experience from study tasks, attention, and PA, including ESD participation. The surveys were conducted online using the UNIPARK software and were recorded and analyzed anonymously.

Measures and covariates

In total, five outcome variables, two independent variables, and seven covariates were included in different analyses: three variables were used for stress load and recovery parameters, two variables for academic performance-related parameters, two variables for PA behavior, two variables for study behavior, four variables for outcome specific baseline values and one variable for age.

Outcome variables

Stress load & recovery parameters (hypothesis 1).

Stress load was included in the analysis with two variables: functional stress and dysfunctional stress. Followingly, a questionnaire containing a word list of adjectives for the recording of emotions and stress during work (called “Erfassung von Emotionen und Beanspruchung “ in German, also known as EEB [ 52 ]) was used. It is an instrument which were developed and validated in the context of occupational health promotion. The items are based on mental-workload research and the assessment of the stress potential of work organization [ 52 ]. Within the questionnaire, four mental and motivational stress items were combined to form a functional stress scale (energetic, willing to perform, attentive, focused) (α = 0.89) and four negative emotional and physical stress items were combined to form dysfunctional stress scale (nervous, physically tensioned, excited, physically unwell) (α = 0.71). Participants rated the items according to how they felt about home studying in general on the following scale (adjustment from “work” to “home studying”): hardly, somewhat, to some extent, fairly, strongly, very strongly, exceptionally.

Recovery experience was measured via psychological detachment. Therefore, the dimension “detachment” of the Recovery Experience Questionnaire (RECQ [ 53 ]) was adjusted to home studying. The introductory question was "How did you experience your free time (including short breaks between learning) during home studying today?". Students responded to four statements based on the extent to which they agreed or disagreed (not at all true, somewhat true, moderately true, mostly true, completely true). The statements covered subjects such as forgetting about studying, not thinking about studying, detachment from studying, and keeping a distance from student tasks. The four items were combined into a score for psychological detachment (α = 0.94).

Academic performance-related parameters (hypothesis 2)

Attention was assessed via the subscale “difficulty maintaining focused attention performance” of the “Attention and Performance Self-Assessment” (ASPA, AP-F2 [ 54 ]). It contains nine items with statements about disturbing situations regarding concentration (e.g. “Even a small noise from the environment could disturb me while reading.”). Participants had to answer how often such situations happened to them on a given day on the following scale: never, rarely, sometimes, often, always. The nine items were combined into the AP-F2 score (α = 0.87).

The perceived study ability was assessed using the study ability index (SAI [ 55 ]). The study ability index captures the current state of perceived functioning in studying. It is based on the Work Ability Index by Hasselhorn and Freude ([ 56 ]) and consists of an adjusted short scale of three adapted items in the context of studying. Firstly, (a) the perceived academic performance was asked after in comparison to the best study-related academic performance ever achieved (from 0 = completely unable to function to 10 = currently best functioning). Secondly, the other two items were aimed at assessing current study-related performance in relation to (b) study tasks that have to be mastered cognitively and (c) the psychological demands of studying. Both items were answered on a five-point Likert scale (1 = very poor, 2 = rather poor, 3 = moderate, 4 = rather good, 5 = very good). A sum index, the SAI, was formed which can indicate values between 2 and 20, with higher values corresponding to higher assessed functioning in studies (α = 0.86). In a previous study it already showed satisfying reliability (α = 0.72) [ 55 ].

Independent variables

Pa behavior.

Two indicators for PA behavior were included via self-reports: the time spent on ESD and the time spent on leisure-time PA (LTPA). Participants were asked the following overarching question daily: “How much time did you spend on physical activity today and in what context”. For the independent variable time spent on PA breaks, participants could answer the option “I participated in the Bewegungssnack digital” with the amount of time they spent on it (in minutes). To assess the time spent on LTPA besides PA breaks, participants could report their time for four different contexts of PA which comprised two forms: Firstly, structured supervised exercise was reported via time spent on (a) university sports courses and (b) other organized sports activities. Secondly, self-organized PA was indicated via (c) independent PA at home, such as a workout or other physically demanding activity such as cleaning or tidying up, as well as via (d) independent PA outside, like walking, cycling, jogging, a workout or something similar. Referring to the different domains of health enhancing PA [ 57 ], the reported minutes of these four types of PA were summed up to a total LTPA value. The total LTPA value was included in the analysis as a metric variable in minutes.

Covariates (hypothesis 3)

Regarding hypothesis 3 and home study behavior, the longest daily stretch of time without a break spent on home studying (in hours) and the daily number of breaks during home studying was assessed. Therein, participants had to answer the overarching question “How much time did you spend on your home studying today?” and give responses to the items: (1) longest stretch of time for home studying (without a break), and (2) number of short and long breaks you took during home studying.

In principle, efforts were made to control for potential confounders at the individual level (level 2) either by including the baseline measure (T0) of the respective variable or by including variables assessing related trait-like characteristics for respective outcomes. The reason why related trait-like characteristics were used for the outcomes was because brief assessments were used for daily surveys that were not concurrently employed in the baseline assessment. To enable the continued use of controlling for person-specific baseline characteristics in the analysis of daily associations, trait-like characteristics available from the baseline assessment were utilized as the best possible approximation.To sum up, four outcome specific baseline value variables were measured before the study period (at T0). The psychological detachment with the RECQ (α = 0.87) [ 53 ] was assessed at the beginning to monitor daily psychological detachment. Further, the SAI [ 55 ] was assessed at the beginning of the study period to monitor daily study ability. To monitor daily stress load, which in part measures mental stress aspects and negative emotional stress aspects, the well-being was assessed at the beginning using the WHO-Five Well-being Index (WHO-5 [ 58 ]). It is a one-dimensional self-report measure with five items. The index value is the sum of all items, with higher values indicating better well-being. As the well-being and stress load tolerance may linked with each other, this variable was assumed to be a good fit with the daily stress load indicating mental and emotional stress aspects. With respect to student life, daily academic performance-related attention was monitored with an instrument for the perception of study demands and resources (termed “Berliner Anforderungen Ressourcen-Inventar – Studierende” in German, the so-called BARI-S [ 59 ]). It contains eight items which capture overwork in studies, time pressure during studies, and the incompatibility of studies and private life. All together they form the BARI-S demand scale (α = 0.85) which was included in the analysis. As overwork and time pressure may result in attention difficulties (e.g. Elfering et al., 2013), this variable was assumed to have a good fit with academic performance-related attention [ 60 ]. Additionally, age in years at T0 was considered as a sociodemographic factor.

Statistical analysis

Since the study design provided ten measurement points for various people, the hierarchical structure of the nested data called for two-level analyses. Pre-analyses of Random-Intercept-Only models for each of the outcome variables (hypothesis 1 to 3) revealed an Intra-Class-Correlation ( ICC ) of at least 0.10 (range 0.26 – 0.64) and confirmed the necessity to perform multilevel analyses [ 61 ]. Specifically, the day-level variables belong to Level 1 (ESD time, LTPA time, longest stretch of time without a break spent on home studying, daily number of breaks during home studying). To analyze day-specific effects within the person, these variables were centered on the person mean (cw = centered within) [ 50 , 62 , 63 , 64 ]. This means that the analyses’ findings are based on a person’s deviations from their average values. The variables assessed at T0 belong to Level 2, which describe the person level (psychological detachment baseline, SAI baseline, well-being, study demands scale, age). These covariates on person level were centered around the grand mean [ 50 ] indicating that the analyses’ findings are based how far an individual deviates from the sample's mean values. As a result, the models’ intercept reflects the outcome value of an average student in the sample at his/her daily average behavior in PA and home study when all parameters are zero. For descriptive statistics SPSS 28.0.1.1 (IBM) and for inferential statistics R (version 4.1.2) were used. The hierarchical models were calculated using the package lme4 with the lmer-function in R in the following steps [ 65 ]. The Null Model was analyzed for all models first, with the corresponding intercept as the only predictor. Afterwards, all variables were entered. The regression coefficient estimates (”b”) were considered for statistical significance for the models and the respective BIC was provided.

In total, five regression models with ‘PA break time’ and ‘LTPA time’ as independent variables were computed due to the five measured outcomes of the present study. Three models belonged to hypothesis 1 and two models to hypothesis 2.

Hypothesis 1: To test hypothesis 1.A two outcome variables were chosen for two separate models: ‘functional stress’ and ‘dysfunctional stress’. Besides the PA behavior variables, the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’, and the ‘well-being’ at the beginning of the study as corresponding baseline variable to the output variable were also included as independent variables in both models. The outcome variable ‘psychological detachment’ was utilized in conjunction with the aforementioned independent variables to test hypotheses 1.B, with one exception: psychological detachment at the start of the study was chosen as the corresponding baseline variable.

Hypothesis 2: To investigate hypothesis 2.A the outcome variable ‘attention difficulties’ was selected. Hypothesis 2.B was tested with the outcome variables ‘study ability’. Both models included both PA behavior variables as well as the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’ and one corresponding baseline variable each: the ‘study demand scale’ at the start of the study for ‘attention difficulties’ and the ‘SAI’ at the beginning of the study for the daily ‘study ability’.

Hypothesis 3: In addition to both PA behavior variables, age and one baseline variable that matched the outcome variable, the covariates ‘daily longest stretch of time spent on home studying’ and ‘daily number of breaks during home studying’ were included in the models for all five outcome variables.

Handling missing data

The dataset had up to 18% missing values (most exhibit the variables ‘daily longest stretch of time without a break spent on home studying’ with 17.89% followed by ‘daily number of breaks during homes studying’ with 16.67%, and ‘functional / dysfunctional stress’ with 12.45%). Therefore, a sensitivity analysis was performed using the multiple imputation mice-package in the statistical program R [ 66 ], the package howManyImputation based on Von Hippel (2020, [ 67 ]), and the additional broom package [ 68 ]. The results of the models remained the same, with one exception for the Attention Difficulties Model: The daily longest stretch of time without a break spent on home studying showed a significant association (Table  1 in supplement). Due to this almost perfect consistency of results between analyses based on the dataset with missing data and those with imputed data alongside the lack of information provided by the packages for imputed datasets, we decided to stick with the main analysis including the missing data. Thus, in the following the results of the main analysis without imputations are presented.

Table 1 shows the descriptive statistics of the variables used in the analysis. An overview of the analysed models is presented in Table  2 .

Effects on stress load and recovery (hypothesis 1)

Hypothesis 1.A: The Model Functional Stress explained 13% of the variance by fixed factors (marginal R 2  = 0.13), and 52% by both fixed and random factors (conditional R 2  = 0.52). The time spent on ESD as well as the time spent on PA in leisure showed a positive significant influence on functional stress (b = 0.032, p  < 0.01). The same applied to LTPA (b = 0.003, p  < 0.001). The Model Dysfunctional Stress (marginal R 2  = 0.027, conditional R 2  = 0.647) showed only one significant result. The dysfunctional stress was only significantly negatively influenced by the time spent on LTPA (b = 0.002, p  < 0.01).

Hypothesis 1.B: With the Model Detachment, fixed factors contributed 18% of the explained variance and fixed and random factors 46% of the explained variance for psychological detachment. Only the amount of time spent on LTPA revealed a positive impact on psychological detachment (b = 0.003, p  < 0.001).

Effects on academic performance-related parameters (hypothesis 2)

Hypothesis 2.A: The Model Attention Difficulties showed 13% of the variance explained by fixed factors, and 51% explained by both fixed and random factors. It showed a significant negative association only for the time spent on LTPA (b = 0.003, p  < 0.001).

Hypothesis 2.B: The Model SAI showed 18% of the variance explained by fixed factors, and 39% explained by both fixed and random factors. There were significant positive associations for time spent on ESD (b = 0.121, p  < 0.001) and time spent on LTPA (b = 0.012, p  < 0.001). The same applied to LTPA (b = 0.012, p  < 0.001).

Effects of home study behavior (hypothesis 3)

Regarding the independent covariates for the outcome variables functional and dysfunctional stress, there were no significant results for the number of breaks during homes studying or the longest stretch of time without a break spent on home studying. Considering the outcome variable ‘psychological detachment’, there were significant results with negative impact for both study behavior variables: breaks during home studying (b = 0.058, p  < 0.01) and daily longest stretch of time without a break (b = 0.120, p  < 0.01). Evaluating the outcome variables ‘attention difficulties’, there were no significant results for the number of breaks during home studying or the longest stretch of time without a break spent on home studying. Testing the independent study behavior variables for the SAI, it increased with increasing number in daily breaks during homes studying relative to the person´s mean (b = 0.183, p  < 0.05). No significant effect was found for the longest stretch of time without a break spent on home studying ( p  = 0.07).

The baseline covariates of the models showed expected associations and thus confirmed their inclusion. The baseline variables well-being showed a significant impact on functional stress (b = 0.089, p  < 0.001), psychological detachment showed a positive effect on the daily output variables psychological detachment (b = 0.471, p  < 0.001), study demand scale showed a positive association on difficulties in attention (b = 0.240, p  < 0.01), and baseline SAI had a positive effect on the daily SAI (b = 0.335, p  < 0.001).

The present study theorized that PA breaks and LTPA positively influence the academic situation of university students. Therefore, impact on stress load (‘functional stress’ and ‘dysfunctional stress’) and ‘psychological detachment’ as well as academic performance-related parameters ‘self-reported attention difficulties’ and ‘perceived study ability’ was taken into account. The first and second hypotheses assumed that both PA breaks and LTPA are positively associated with the aforementioned parameters and were confirmed for LTPA for all parameters and for PA breaks for functional stress and perceived study ability. The third hypothesis assumed that home study behavior regarding the daily number of breaks during home studying and longest stretch of time without a break spent on home studying has side effects. Detected negative effects for both covariates on psychological detachment and positive effects for the daily number of breaks on perceived study ability were partly unexpected in their direction. These results emphasize the key position of PA in the context of modern health promotion especially for students in an academic context.

Regarding hypothesis 1 and the detected positive associations for stress load and recovery parameters with PA, the results are in accordance with the stress-regulatory potential of PA from the state of research [ 23 ]. For hypothesis 1.A, there is a positive influence of PA breaks and LTPA on functional stress and a negative influence of LTPA on dysfunctional stress. Given the bilateral role of stress load, the results indicate that PA breaks and LTPA are beneficial for coping with study demands, and may help to promote feelings of joy, pride, and learning progress [ 27 ]. This is in line with previous evidence that PA breaks in lectures can buffer university students’ perceived stress [ 29 ], lead to better mood ratings [ 29 , 31 ], and increase in motivation [ 28 , 69 ], vigor [ 34 ], energy [ 30 ], and self-perceived physical and psychological well-being [ 28 ]. Looking at dysfunctional stress, the result point that LTPA counteract load-related states of strain such as inner tension, irritability and nervous restlessness or feelings of boredom [ 27 ]. In contrast, short PA breaks during the day could not have enough impact in countering dysfunctional stress at the end of the day regarding the accumulation of negative stressors during home studying which might have occurred after the participant took PA breaks. Other studies have been able to show a reduction in tension [ 30 ] and general muscular discomfort [ 33 ] after PA breaks. However, this was measured as an immediate effect of PA breaks and not with general evening surveys. Blasche and colleagues [ 34 ] measured effects immediately and 20 min after different kind of breaks and found that PA breaks led to an additional short‐ and medium‐term increase in vigor while the relaxation break lead to an additional medium‐term decrease in fatigue compared to an unstructured open break. This is consistent with the results of the present study that an effect of PA breaks is only observed for functional stress and not for dysfunctional stress. Furthermore, there is evidence that long sitting during lectures leads to increased fatigue and lower concentration [ 31 , 70 ], which could be counteracted by PA breaks. For both types of stress loads, functional and dysfunctional stress, there is an influence of students´ well-being in this study. This shows that the stress load is affected by the way students have mentally felt over the last two weeks. The relevance of monitoring this seems important especially in the time of COVID-19 as, for example, 65.3% of the students of a cross-sectional online survey at an Australian university reported low to very low well-being during that time [ 71 ]. However, since PA and well-being can support functional stress load, they should be of the highest priority—not only as regards the pandemic, but also in general.

Looking at hypothesis 1.B; while there is a positive influence of LTPA on experienced psychological detachment, no significant influence for PA breaks was detected. The fact that only LTPA has a positive effect can be explained by the voluntary character of the activity [ 50 ]. The voluntary character ensures that stressors no longer affect the student and, thus, recovery as detachment can take place. Home studying is not present in leisure times, and thus detachment from study is easier. The PA break videos, on the other hand, were shot in a university setting, which would have made it more difficult to detach from study. In order to further understand how PA breaks affect recovery and whether there is a distinction between PA breaks and LTPA, future research should also consider other types of recovery (e.g. relaxation, mastery, and control). Additionally, different types of PA breaks, such as group PA breaks taken on-site versus video-based PA breaks, should be taken into account.

Considering the confirmed positive associations for academic performance-related parameters of hypothesis 2, the results are in accordance with the evidence of positive associations between PA and learning and educational success [ 6 ], as well as between PA breaks and better cognitive functioning [ 28 ]. Looking at the self-reported attention difficulties of hypothesis 2.A, only LTPA can counteract it. PA breaks showed no effects, contrary to the results of a study of Löffler and collegues (2011, [ 31 ]), in which acute effects of PA breaks could be found for higher attention and cognitive performance. Furthermore, the perception of study demands before the study periods has a positive impact on difficulties in attention. That means that overload in studies, time pressure during studies, and incompatibility of studies and private life leads to higher difficulties with attention in home studying. In these conditions, PA breaks might have been seen as interfering, resulting in the expected beneficial effects of exercise on attention and task-related participation behavior [ 72 , 73 ] therefore remaining undetected. With respect to the COVID-19 pandemic, accompanying education changes, and an increase in student´s worries [ 74 , 75 ], the perception of study demands could be affected. This suggests that especially in times of constraint and changes, it is important to promote PA in order to counteract attention difficulties. This also applies to post-pandemic phase.

Regarding the perceived academic performance of hypothesis 2.B, both PA breaks and LTPA have a positive effect on perceived study ability. This result confirms the positive short-term effects on cognition tasks [ 76 ]. It is also in line with the positive function of PA breaks in interrupting sedentary behavior and therefore counteracting the negative association between sitting behavior and lower cognitive performance [ 24 ]. Additionally, this result also fits with the previously mentioned positive relationship between LTPA and functional stress and between PA breaks and functional stress.

According to hypothesis 3, in relation to the mentioned stress load and recovery parameters, there are negative effects of the daily number of breaks during home studying and the longest stretch of time without a break spent on home studying on psychological detachment. As stressors result in negative activation, which impede psychological detachment from study during non-studying time [ 25 ], it was expected and confirmed that the longest stretch of time without a break spent on home studying has a negative effect on detachment. Initially unexpected, the number of breaks has a negative influence on psychological detachment, as breaks could prevent the accumulation of strain reactions. However, if the breaks had no recovery effect through successful detachment, the number might not have any influence on recovery via detachment. This is indicated by the PA breaks, which had no impact on psychological detachment. Since there are other ways to recover from stress besides psychological detachment, such as relaxation, mastery, and control [ 53 ], PA breaks must have had an additional impact in relation to the positive results for functional stress.

In relation to the mentioned academic performance-related parameters, only the number of breaks has a positive influence on the perceived study ability. This indicates that not only PA breaks but also breaks in general lead to better perceived functionality in studying. Paulus and colleagues (2021) found out that an increase in cognitive skills is not only attributed to PA breaks and standing breaks, but also to open breaks with no special instructions [ 28 ]. Either way, they found better improvement in self-perceived physical and psychological well-being of the university students with PA breaks than with open breaks. This is also reflected in the present study with the aforementioned positive effects of PA breaks on functional stress, which does not apply to the number of breaks.

Overall, it must be considered that the there is a more complex network of associations between the examined parameters. The hypothesized separate relation of PA with different parameters do not consider associations between parameters of stress load / recovery and academic performance although there might be a interdependency. Furthermore, moderation aspects were not examined. For example, PA could be a moderator which buffer negative effects of stress on the study ability [ 55 ]. Moreover, perceived study ability might moderate stress levels and academic performance. Further studies should try to approach and understand the different relationships between the parameters in its complexity.

Limitations

Certain limitations must be taken into account. Regarding the imbalanced design toward more female students in the sample (47 female versus 6 male), possible sampling bias cannot be excluded. Gender research on students' emotional states during COVID-19, when this study took place, or students´ acceptance of PA breaks is diverse and only partially supplied with inconsistent findings. For example, during the COVID-19 pandemic, some studies reported that female students were associated with lower well-being [ 71 ] or worse mental health trajectories [ 75 , 77 ]. Another study with a large sample of students from 62 countries reported that male students were more strongly affected by the pandemic because they were significantly less satisfied with their academic life [ 74 ]. However, Keating and colleges (2020) discovered that, despite the COVID-19 pandemic, females rated some aspects of PA breaks during lectures more positively than male students did. However, this was also based on a female slanted sample [ 78 ]. Further studies are needed to get more insights into gender bias.

Furthermore, the small sample size combined with up to 16% missing values comprises a significant short-coming. There were a lot of possibilities which could cause such missing data, like refused, forgotten or missed participation, technical problems, or deviation of the personal code for the questionnaire between survey times. Although the effects could be excluded by sensitive analysis due to missing data, the sample is still small. To generalize the findings, future replication studies are needed.

Additionally, PA breaks were only captured through participation in the ESD, the specially instructed PA break via video. Effects of other short PA breaks were not include in the study. However, participants were called to participate in ESD whenever possible, so the likelihood that they did take part in PA breaks in addition to the ESD could be ignored.

With respect to the baseline variables, it must be considered that two variables (stress load, attention difficulties) were adjusted not with their identical variable in T0, but with other conceptually associated variables (well-being index, BARI-S). Indeed, contrary to the assumption the well-being index does only show an association with functional stress, indicating that it does not control dysfunctional stress. Although the other three assumed associations were confirmed there might be a discrepancy between the daily measured variables and the variables measured in T0. Further studies should either proof the association between these used variables or measure the same variables in T0 for control the daily value of these variables.

Moreover, the measuring instruments comprised the self-assessed perception of the students and thus do not provide an objective information. This must be considered, especially for measuring cognitive and academic-performance-related measures. Here, existing objective tests, such as multiple choice exams after a video-taped lecture [ 72 ] might have also been used. Nevertheless, such methods were mostly used in a lab setting and do not reflect reality. Due to economic reasons and the natural learning environment, such procedures were not applied in this study. However, the circumstances of COVID-19 pandemic allowed a kind of lab setting in real life, as there were a lot of restrictions in daily life which limited the influence of other covariates. The study design provides a real natural home studying environment, producing results that are applicable to the healthy way that students learn in the real world. As this study took place under the conditions of COVID-19, new transformations in studying were also taken into account, as home studying and digital learning are increasingly part of everyday study.

However, the restrictions during the COVID-19 pandemic could result in a greater extent of leisure time per se. As the available leisure time in general was not measured on daily level, it is not possible to distinguish if the examined effects on the outcomes are purely attributable to PA. It is possible that being more physical active is the result of having a greater extent of leisure time and not that PA but the leisure time itself effected the examined outcomes. To address this issue in future studies, it is necessary to measure the proportion of PA in relation to the leisure time available.

Furthermore, due to the retrospective nature of the daily assessments of the variables, there may be overstated associations which must be taken into account. Anyway, the daily level of the study design provides advantages regarding the ability to observe changes in an individual's characteristics over the period of the study. This design made it possible to find out the necessity to analyze the hierarchical structure of the intraindividual data nested within the interindividual data. The performed multilevel analyses made it possible to reflect the outcome of an average student in the sample at his/her daily average behavior in PA and home study.

Conclusion and practical implications

The current findings confirm the importance of PA for university students` stress load, recovery experience, and academic performance-related parameters in home studying. Briefly summarized, it can be concluded that PA breaks positively affect stress load and perceived study ability. LTPA has a positive impact on stress load, recovery experience, and academic performance-related parameters regarding attention difficulties and perceived study ability. Following these results, universities should promote PA in both fashions in order to keep their students healthy and functioning: On the one hand, they should offer opportunities to be physically active in leisure time. This includes time, environment, and structural aspects. The university sport department, which offers sport courses and provides sport facilities on university campuses for students´ leisure time, is one good example. On the other hand, they should support PA breaks during the learning process and in the immediate location of study. This includes, for example, providing instructor videos for PA breaks to use while home studying, and furthermore having instructors to lead in-person PA breaks in on-site learning settings like universities´ libraries or even lectures and seminars. This not only promotes PA, but also reduces sedentary behavior and thereby reduces many other health risks. Further research should focus not only on the effect of PA behavior but also of sedentary behavior as well as the amount of leisure time per se. They should also try to implement objective measures for example on academic performance parameters and investigate different effect directions and possible moderation effects to get a deeper understanding of the complex network of associations in which PA plays a crucial role.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Attention and Performance Self-Assessment

"Berliner Anforderungen Ressourcen-Inventar – Studierende" (instrument for the perception of study demands and resources)

Centered within

Grand centered

“Erfassung von Emotionen und Beanspruchung “ (questionnaire containing a word list of adjectives for the recording of emotions and stress during work)

Exercise snack digital (special physical activity break offer)

Intra-Class-Correlation

Leisure time physical activity

• Physical activity

Recovery Experience Questionnaire

Study ability index

World Health Organization-Five Well-being index

Knight JA. Physical inactivity: associated diseases and disorders. Ann Clin Lab Sci. 2012;42(3):320–37.

PubMed   Google Scholar  

Kemel PN, Porter JE, Coombs N. Improving youth physical, mental and social health through physical activity: a systematic literature review. Health Promot J Austr. 2002;33(3):590–601.

Article   Google Scholar  

Gothe NP, Ehlers DK, Salerno EA, Fanning J, Kramer AF, McAuley E. Physical activity, sleep and quality of life in older adults: influence of physical, mental and social well-being. Behav Sleep Med. 2020;18(6):797–808.

Article   PubMed   Google Scholar  

Eime RM, Young JA, Harvey JT, Charity MJ, Payne WR. A systematic review of the psychological and social benefits of participation in sport for children and adolescents: informing development of a conceptual model of health through sport. Int J Behav Nutr Phys Act. 2013;10(1):1–21.

Google Scholar  

Iannotti RJ, Janssen I, Haug E, Kololo H, Annaheim B, Borraccino A. Interrelationships of adolescent physical activity, screen-based sedentary behaviour, and social and psychological health. Int J Public Health. 2009;54:191–8.

Dadaczynski K, Schiemann S. Welchen Einfluss haben körperliche Aktivität und Fitness im Kindes-und Jugendalter auf Bildungsoutcomes? German J Exerc Sport Res. 2015;4(45):190–9.

Kari JT, Pehkonen J, Hutri-Kähönen N, Raitakari OT, Tammelin TH. Longitudinal associations between physical activity and educational outcomes. Med Sci Sports  Exerc. 2017;49(11).

Grim M, Hortz B, Petosa R. Impact evaluation of a pilot web-based intervention to increase physical activity. Am J Health Promot. 2011;25(4):227–30.

Irwin JD. Prevalence of university students’ sufficient physical activity: A systematic review. Percept Mot Skills. 2004;98:927–43.

Kwan MY, Cairney J, Faulkner GE, Pullenayegum EE. Physical activity and other health-risk behaviors during the transition into early adulthood: a longitudinal cohort study. Am J Prev Med. 2012;42(1):14–20.

John JM, Gropper H, Thiel A. The role of critical life events in the talent development pathways of athletes and musicians: A systematic review. Psychol Sport Exerc. 2019;45.

Bopp M, Bopp C, Schuchert M. Active transportation to and on campus is associated with objectively measured fitness outcomes among college students. J Phys Act Health. 2015;12(3):418–23.

Dadaczynski K. Stand der Forschung zum Zusammenhang von Gesundheit und Bildung. Überblick und Implikationen für die schulische Gesundheitsförderung. Zeitschrift für Gesundheitspsychologie. 2012;20(3):141–53

Suhrcke M, de Paz NC. The impact of health and health behaviours on educational outcomes in high-income countries: a review of the evidence. Copenhagen: WHO Regional Offi ce for Europe; 2011.

Lynch BM, Owen N. Too much sitting and chronic disease risk: steps to move the science forward. Ann Intern Med. 2015;16(2):146–7.

Voss MW, Carr LJ, Clark R, Weng T. Revenge of the “sit” II: Does lifestyle impact neuronal and cognitive health through distinct mechanisms associated with sedentary behavior and physical activity? Ment Health Phys Act. 2014;7(1):9–24.

Huber G. Ist Sitzen eine tödliche Aktivität? B&G Bewegungstherapie und Gesundheitssport. 2014;30(01):13–6.

Peterson NE, Sirard JR, Kulbok PA, DeBoer MD, Erickson JM. Sedentary behavior and physical activity of young adult university students. Res Nurs Health. 2018;4(1):30–8.

Rupp R, Dold C, Bucksch J. Sitzzeitreduktion und Bewegungsaktivierung in der Hochschullehre – Entwicklung und Implementierung der Mehrebenen-Intervention Kopf-Stehen. Die Hochschullehre. 2019;5:525–42.

Ickes MJ, McMullen J, Pflug C, Westgate PM. Impact of a University-based Program on Obese College Students’ Physical Activity Behaviors, Attitudes, and Self-efficacy. Am J Health Educ. 2016;47(1):47–55.

Lepp A, Barkley JE, Karpinski AC. The relationship between cell phone use and academic performance in a sample of US college students. Sage Open. 2015;5(1).

Stapp AC, Prior LF. The Impact of Physically Active Brain Breaks on College Students’ Activity Levels and Perceptions. J Physic Activ Res. 2018;3(1):60–7.

Fuchs R, Klaperski S. Sportliche Aktivität und Stressregulation. In: Fuchs R, Schlicht W, editors. Sportaktivität und seelische Gesundheit Göttingen: Hogrefe; 2012. p. 100–21.

Falck RS, Davis JC, Liu-Ambrose T. What is the association between sedentary behaviour and cognitive function? A systematic review. Br J Sports Med. 2017;51(10):800–11.

Sonnentag S, Fritz C. Recovery from job stress: The stressor-detachment model as an integrative framework. J Organ Behav. 2015;36:72–103.

Edwards JR. A cybernetic theory of stress, coping, and well-being in organizations. Acad Manag Rev. 1992;17(2):238–74.

Wieland R. Status-Bericht: Psychische Gesundheit in der betrieblichen Gesundheitsförderung – eine arbeitspsychologische Perspektive. In: Nold H, Wenninger G, editors. Rückengesundheit und psychische Gesundheit. Rückengesundheit und psychische Gesundheit.: Asanger Verlag; 2013.

Paulus M, Kunkel J, Schmidt SCE, Bachert P, Wäsche H, Neumann R, et al. Standing breaks in lectures improve university students’ self-perceived physical, mental, and cognitive condition. Int J Environ Res Public Health. 2021;18.

Marschin V, Herbert C. A Short, Multimodal Activity Break Incorporated Into the Learning Context During the Covid-19 Pandemic: Effects of Physical Activity and Positive Expressive Writing on University Students’ Mental Health — Results and Recommendations From a Pilot Study. Front Psychol. 2021;12.

Gollner E, Savil M, Schnabel F, Braun C, Blasche G. Unterschiede in der Wirksamkeit von Kurzpausenaktivitäten im Vergleich von Bewegungspausen zu psychoregulativen Pausen bei kognitiver Belastung. Bewegungstherapie Gesundheitssport. 2019;35:134–43.

Löffler SN, Dominok E, von Haaren B, Schellhorn R, Gidion G. Aktivierung, Konzentration, Entspannung: Interventionsmöglichkeiten zur Förderung fitnessrelevanter Kompetenzen im Studium: KIT Scientific Publishing; 2011.

Marschin V, Herbert C. A short, multimodal activity break incorporated into the learning context during the Covid-19 pandemic: effects of physical activity and positive expressive writing on university students' mental health—results and recommendations from a pilot study. Front Psychol. 2021.

Kowalsky RJ, Farney TM, Hearon CM. Resistance Exercise Breaks Improve Ratings of Discomfort and Sleepiness in College Students. Res Q Exerc Sport. 2022;94(1):210–5.

Blasche G, Szabo B, Wagner-Menghin M, Ekmekcioglu C, Gollner E. Comparison of rest-break interventions during a mentally demanding task. Stress Health. 2018;34(5):629–38.

Article   PubMed   PubMed Central   Google Scholar  

Fuchs R, Klaperski S. Stressregulation durch Sport und Bewegung. In: Fuchs R, Gerber M, editors. Handbuch Stressregulation und Sport. Berlin: Springer; 2018. p. 205–26.

Chapter   Google Scholar  

Kaluza G, Renneberg B. Stressbewältigung. In: Bengel J, Jerusalem M, editors. HandbuchGesundheitspsychologie und medizinische Psychologie. Göttingen: Hogrefe; 2009. p. 265–72.

Klaperski S. Exercise, Stress and Health: The Stress-Buffering Effect of Exercise. In: Fuchs R, Gerber M, editors. Handbuch Stressregulation und Sport. Berlin: Springer; 2018. p. 227–50.

Lesser IA, Nienhuis CP. The impact of COVID-19 on physical activity behavior and well-being of Canadians. Int J Environ Res Public Health. 2020;17(11):3899.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Moore SA, Faulkner G, Rhodes RE, Brussoni M, Chulak-Bozzer T, Ferguson LJ, et al. Impact of the COVID-19 virus outbreak on movement and play behaviours of Canadian children and youth: a national survey. Int J Behav Nutr Phys Act. 2020;17(1):1–11.

Rodríguez-Larrad A, Mañas A, Labayen I, González-Gross M, Espin A, Aznar S, et al. Impact of COVID-19 confinement on physical activity and sedentary behaviour in Spanish university students: Role of gender. Int J Environ Res Public Health. 2021;18(2):369.

Stanton R, To QG, Khalesi S, Williams SL, Alley SJ, Thwaite TL, et al. Depression, anxiety and stress during COVID-19: associations with changes in physical activity, sleep, tobacco and alcohol use in Australian adults. Int J Environ Res Public Health. 2020;17(11):4065.

Zheng C, Huang WY, Sheridan S, Sit CHP, Chen XK, Wong SHS. COVID-19 pandemic brings a sedentary lifestyle in young adults: a cross-sectional and longitudinal study. Int J Environ Res Public Health. 2020;17(17):6035.

Commodari E. Attention Skills and Risk of Developing Learning Difficulties. Curr Psychol. 2012;31:17–34.

Commodari E, Guarnera M. Attention and reading skills. Percept Mot Skills. 2005;100:3753–86.

Raaijmakers MAJ, Smidts DP, Sergeant JA, Maassen GH, Posthumus JA, van Engeland H, et al. Executive functions in preschool children with aggressive behavior: impairments in inhibitory control. J Abnorm Child Psychol. 2008;36:1097–107.

Vellutino FR, Scanlon DM, Sipay ER, Small SG, Pratt A, Chen RS, et al. Cognitive profiles of difficulty to remediate and readily remediate poor readers: early intervention as a vehicle for distinguishing between cognitive and experiential deficits as basic of specific reading disability. J Educ Psychol. 1996;88:601–38.

Ilies R, Dimotakis N, De Pater IE. Psychological and physiological reactions to high workloads: Implications for well-being. Pers Psychol. 2010;63(2):407–36.

Rodell JB, Judge TA. Can “good” stressors spark “bad” behaviors? The mediating role of emotions in links of challenge and hindrance stressors with citizenship and counterproductive behaviors. J Appl Psychol. 2009;94(6).

Teuber M, Leyhr D, Moll J, Sudeck G. Nudging digital physical activity breaks for home studying of university students—A randomized controlled trial during the COVID-19 pandemic with daily activity measures. Front Sports Active Living. 2022;4.

Feuerhahn N, Sonnentag S, Woll A. Exercise after work, psychological mediators, and affect: A day-level study. Eur J Work Organ Psy. 2014;23(1):62–79.

Sonnentag S. Work, Recovery Activities, ans Individual Well-Being: A Diary Study. J Occup Health Psychol. 2001;6(3):196–210.

Article   CAS   PubMed   Google Scholar  

Wieland R. Gestaltung gesundheitsförderlicher Arbeitsbedingungen. In: Kleinbeck U, Schmidt K-H, editors. Arbeitspsychologie (Enzyklopädie der Psychologie, Serie Wirtschafts-, Organisations- und Arbeitspsychologie). 1. Göttingen: Hogrefe; 2010. p. 869–919.

Sonnentag S, Fritz C. The Recovery Experience Questionnaire: development and validation of a measure for assessing recuperation and unwinding from work. J Occup Health Psychol. 2007;12(3):204–21.

Bankstahl US, Görtelmeyer R. Measuring subjective complaints of attention and performance failures development and psychometric validation in tinnitus of the self-assessment scale APSA. Health and Quality of Life Outcomes. 2013;11(86).

Teuber M, Arzberger I, Sudeck G. Körperliche Aktivität, Gesundheit und Funktionsfähigkeit im Studium: Sportliche Freizeitaktivitäten und aktive Fortbewegung als Ressource im Studium? In: Göring A, Mayer J, Jetzke M, editors. Sport und Studienerfolg - Analysen zur Bedeutung sportlicher Aktivität im Setting Hochschule. Hochschulsport: Bildung und Wissenschaft, 4. Göttingen: Universitätsverlag Göttingen; 2020. p. 27–49.

Hasselhorn H-M, Freude G. Der Work-Ability-Index: ein Leitfaden In: Arbeitsmedizin BfAu, editor. Dortmund/Berlin/Dresden: Wirtschaftsverl. NW, Verlag für Neue Wissenschaft GmbH; 2007.

Rütten A, Pfeifer K. Nationale Empfehlungen für Bewegung und Bewegungsförderung. Köln: Bundeszentrale für Gesundheitliche Aufklärung (BZgA); 2017.

Brähler E, Mühlan H, Albani C, Schmidt S. Teststatistische Prüfung und Normierung der deutschen Versionen des EUROHIS-QOL Lebensqualität-Index und des WHO-5 Wohlbefindens-Index. Diagnostica. 2007;53(2):83–96.

Gusy B, Wörfel F, Lohmann K. Erschöpfung und Engagement im Studium. Zeitschrift für Gesundheitspsychologie. 2016;24(1):41–53.

Elfering A, Grebner S, de Tribolet-Hardy F. The long arm of time pressure at work: Cognitive failure and commuting near-accidents. Eur J Work Organ Psy. 2013;22(6):737–49.

Kreft IG, de Leeuw J. Introducing multilevel modeling. London: Sage; 1998.

Book   Google Scholar  

Bates D, Mächler M, Bolker BM, Walker SC. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw. 2015;67(1):1–48.

Hofmann DA, Gavin MB. Centering decisions in hierarchical linear models: Implications for research in organizations. J Manag. 1998;24(5):623–41.

Nezlek J. Diary Studies in Social and Personality Psychology: An Introduction With Some Recommendations and Suggestions. Social Psychological Bulletin. 2020;15(2).

Knapp G. Gemischte Modelle in R. Begleitskriptum zur Weiterbildung. In: Dortmund TU, editor. Braunschweig2019.

Van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R. J Stat Softw. 2011;45:1–67.

von Hippel PT. How Many Imputations Do You Need? A Twostage Calculation Using a Quadratic Rule. Sociological Methods & Research. 2020;49(3):699–718.

Article   MathSciNet   Google Scholar  

Robinson D. broom: An R package for converting statistical analysis objects into tidy data frames. arXiv preprint arXiv:14123565. 2014.

Young-Jones A, McCain J, Hart B. Let’s Take a Break: The Impact of Physical Activity on Academic Motivation. Int J Teach Learn High Educ. 2022;33(3):110–8.

Barr-Anderson DJ, AuYoung M, Whitt-Glover MC, Glenn BA, Yancey AK. Integration of short bouts of physical activity into organizational routine: A systematic review of the literature. Am J Prev Med. 2011;40(1):76–93.

Dodd RH, Dadaczynski K, Okan O, McCaffery KJ, Pickles K. Psychological Wellbeing and Academic Experience of University Students in Australia during COVID-19. Int J Environ Res Public Health 2021;18.

Fenesi B, Lucibello K, Kim JA, Heisz JJ. Sweat so you don’t forget: exercise breaks during a university lecture increase on-task attention and learning. J Appl Res Mem Cogn. 2018;7(2):261–9.

Ruhland S, Lange KW. Effect of classroom-based physical activity interventions on attention and on-task behavior in schoolchildren: A systematic review. Sports Med Health Sci. 2021;3:125–33.

Aristovnik A, Keržič D, Ravšelj D, Tomaževič N, Umek L. Impacts of the COVID-19 Pandemic on Life of Higher Education Students: A Global Perspective. Sustainability. 2020;12(20).

Browning MHEM, Larson LR, Sharaievska I, Rigolon A, McAnirlin O, Mullenbach L, et al. Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States. PLoS ONE 2021;16(1).

Chang Y-K, Labban JD, Gapin JI, Etnier JL. The effects of acute exercise on cognitive performance: a meta-analysis. Brain Res. 2012;1453:87–101.

Elmer T, Mepham K, Stadtfeld C. Students under lockdown: Comparisons of students’ social networks and mental health before and during the COVID-19 crisis in Switzerland. PLoS ONE. 2020;15(7):e0236337.

Keating R, Ahern S, Bisgood L, Mernagh K, Nicolson GH, Barrett EM. Stand up, stand out. Feasibility of an active break targeting prolonged sitting in university students. J Am Coll Health. 2020;70(7).

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Acknowledgements

We would like to thank Juliane Moll, research associate of the Student Health Management of University of Tübingen, for the support in the coordination and realization study. We would like to express our thanks also to Ingrid Arzberger, Head of University Sports at the University of Tübingen, for providing the resources and co-applying for the funding. We acknowledge support by Open Access Publishing Fund of University of Tübingen.

Open Access funding enabled and organized by Projekt DEAL. This research regarding the conduction of the study was funded by the Techniker Krankenkasse, health insurance fund.

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Contributions

M.T. and G.S. designed the study. M.T. coordinated and carried out participant recruitment and data collection. M.T. analyzed the data and M.T. and D.L. interpreted the data. M.T. drafted the initial version of the manuscript and prepared the figure and all tables. All authors contributed to reviewing and editing the manuscript and have read and agreed to the final version of the manuscript.

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Correspondence to Monika Teuber .

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Teuber, M., Leyhr, D. & Sudeck, G. Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level. BMC Public Health 24 , 598 (2024). https://doi.org/10.1186/s12889-024-18082-z

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Received : 30 June 2023

Accepted : 12 February 2024

Published : 24 February 2024

DOI : https://doi.org/10.1186/s12889-024-18082-z

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