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  • v.10(1); Jan-Mar 2019

Study designs: Part 2 – Descriptive studies

Rakesh aggarwal.

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

Priya Ranganathan

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

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


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


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


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

Case reports and case series

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

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

Cross-sectional studies

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

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

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

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

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

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

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

Ecological studies

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

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

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

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

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


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

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Research Design in Business and Management pp 201–220 Cite as

Longitudinal Research Design

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This chapter addresses the peculiarities, characteristics, and major fallacies of longitudinal research designs. Longitudinal studies represent an examination of correlated phenomena over a period. Its analysis stresses changes over time. The aim of a longitudinal research design is to enable or improve the validity of inferences not possible to achieve in cross-sectional research, to draw conclusions based on arguments that are not workable if we look at a point in time. Also, researchers find relevant information on how to write a longitudinal research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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Hunziker, S., Blankenagel, M. (2021). Longitudinal Research Design. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_11

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Observational Study Designs: Synopsis for Selecting an Appropriate Study Design


The selection of a study design is the most critical step in the research methodology. Crucial factors should be considered during the selection of the study design, which is the formulated research question, as well as the method of participant selection. Different study designs can be applied to the same research question(s). Research designs are classified as qualitative, quantitative, and mixed design. Observational design occupies the middle and lower parts of the hierarchy of evidence-based pyramid. The observational design is subdivided into descriptive, including cross-sectional, case report or case series, and correlational, and analytic which includes cross-section, case-control, and cohort studies. Each research design has its uses and points of strength and limitations. The aim of this article to provide a simplified approach for the selection of descriptive study design.

Keywords: descriptive; observational; prospective; retrospective; strength; study design.

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What is a Longitudinal Study?: Definition and Explanation

What is a longitudinal study and what are it's uses

In this article, we’ll cover all you need to know about longitudinal research. 

Let’s take a closer look at the defining characteristics of longitudinal studies, review the pros and cons of this type of research, and share some useful longitudinal study examples. 

Content Index

What is a longitudinal study?

Types of longitudinal studies, advantages and disadvantages of conducting longitudinal surveys.

Types of surveys that use a longitudinal study

Longitudinal study examples.

A longitudinal study is a research conducted over an extended period of time. It is mostly used in medical research and other areas like psychology or sociology. 

When using this method, a longitudinal survey can pay off with actionable insights when you have the time to engage in a long-term research project.

Longitudinal studies often use surveys to collect data that is either qualitative or quantitative. Additionally, in a longitudinal study, a survey creator does not interfere with survey participants. Instead, the survey creator distributes questionnaires over time to observe changes in participants, behaviors, or attitudes. 

Many medical studies are longitudinal; researchers note and collect data from the same subjects over what can be many years. 

Longitudinal studies are versatile, repeatable, and able to account for quantitative and qualitative data. Consider the three major types of longitudinal studies for future research:

Types of longitudinal studies

Panel study: A panel survey involves a sample of people from a more significant population and is conducted at specified intervals for a more extended period. 

One of the panel study’s essential features is that researchers collect data from the same sample at different points in time. Most panel studies are designed for quantitative analysis, though they may also be used to collect qualitative data and analysis.

Cohort Study: A cohort study samples a cohort (a group of people who typically experience the same event at a given point in time). Medical researchers tend to conduct cohort studies. Some might consider clinical trials similar to cohort studies. 

In cohort studies, researchers merely observe participants without intervention, unlike clinical trials in which participants undergo tests.

Retrospective study: A retrospective study uses already existing data, collected during previously conducted research with similar methodology and variables. 

While doing a retrospective study, the researcher uses an administrative database, pre-existing medical records, or one-to-one interviews.

As we’ve demonstrated, a longitudinal study is useful in science, medicine, and many other fields. There are many reasons why a researcher might want to conduct a longitudinal study. One of the essential reasons is, longitudinal studies give unique insights that many other types of research fail to provide. 

Advantages of longitudinal studies

Additional data points can be collected to study unexpected findings, allowing changes to be made to the survey based on the approach that is detected.

Disadvantages of longitudinal studies


Longitudinal studies vs. Cross-sectional studies

Longitudinal studies are often confused with cross-sectional studies. Unlike longitudinal studies, where the research variables can change during a study, a cross-sectional study observes a single instance with all variables remaining the same throughout the study. A longitudinal study may follow up on a cross-sectional study to investigate the relationship between the variables more thoroughly.

The design of the study is highly dependent on the nature of the research questions. Whenever a researcher decides to collect data by surveying their participants, what matters most are the questions that are asked in the survey.

Cross-sectional Study vs Longitudinal study

Knowing what information a study should gather is the first step in determining how to conduct the rest of the study. 

With a longitudinal study, you can measure and compare various business and branding aspects by deploying surveys. Some of the classic examples of surveys that researchers can use for longitudinal studies are:

Market trends and brand awareness: Use a market research survey and marketing survey to identify market trends and develop brand awareness. Through these surveys, businesses or organizations can learn what customers want and what they will discard. This study can be carried over time to assess market trends repeatedly, as they are volatile and tend to change constantly.

Product feedback: If a business or brand launches a new product and wants to know how it is faring with consumers, product feedback surveys are a great option. Collect feedback from customers about the product over an extended time. Once you’ve collected the data, it’s time to put that feedback into practice and improve your offerings.

Customer satisfaction: Customer satisfaction surveys help an organization get to know the level of satisfaction or dissatisfaction among its customers. A longitudinal survey can gain feedback from new and regular customers for as long as you’d like to collect it, so it’s useful whether you’re starting a business or hoping to make some improvements to an established brand.

Employee engagement: When you check in regularly over time with a longitudinal survey, you’ll get a big-picture perspective of your company culture. Find out whether employees feel comfortable collaborating with colleagues and gauge their level of motivation at work.

Now that you know the basics of how researchers use longitudinal studies across several disciplines let’s review the following examples:

Example 1: Identical twins

Consider a study conducted to understand the similarities or differences between identical twins who are brought up together versus identical twins who were not. The study observes several variables, but the constant is that all the participants have identical twins.

In this case, researchers would want to observe these participants from childhood to adulthood, to understand how growing up in different environments influences traits, habits, and personality.

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Over many years, researchers can see both sets of twins as they experience life without intervention. Because the participants share the same genes, it is assumed that any differences are due to environmental analysis , but only an attentive study can conclude those assumptions.

Example 2: Violence and video games

A group of researchers is studying whether there is a link between violence and video game usage. They collect a large sample of participants for the study. To reduce the amount of interference with their natural habits, these individuals come from a population that already plays video games. The age group is focused on teenagers (13-19 years old).

The researchers record how prone to violence participants in the sample are at the onset. It creates a baseline for later comparisons. Now the researchers will give a log to each participant to keep track of how much and how frequently they play and how much time they spend playing video games. This study can go on for months or years. During this time, the researcher can compare video game-playing behaviors with violent tendencies. Thus, investigating whether there is a link between violence and video games.

Conducting a longitudinal study with surveys is straightforward and applicable to almost any discipline. With our survey software you can easily start your own survey today. 



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Longitudinal Study Design: Definition & Examples

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A longitudinal study is a type of observational and correlational study that involves monitoring a population over an extended period of time.

Table of Contents

What is a Longitudinal Study?

In longitudinal studies, researchers do not manipulate any variables or interfere with the environment. Instead, they simply conduct observations on the same group of subjects over a period of time.

These research studies can last as short as a week or as long as multiple years or even decades. Unlike cross-sectional studies that measure a moment in time, longitudinal studies last beyond a single moment, enabling researchers to discover cause-and-effect relationships between variables.

They are beneficial for recognizing any changes, developments, or patterns in the characteristics of a target population. Longitudinal studies are often used in clinical and developmental psychology to study shifts in behaviors, thoughts, emotions, and trends throughout a lifetime.

For example, a longitudinal study could be used to examine the progress and well-being of children at critical age periods from birth to adulthood.

The Harvard Study of Adult Development is one of the longest longitudinal studies to date. Researchers in this study have followed the same men group for over 80 years, observing psychosocial variables and biological processes for healthy aging and well-being in late life (see Harvard Second Generation Study).

Panel Study

  • A panel study is a type of longitudinal study that involves sampling a cross-section of individuals at specific intervals for an extended period.
  • These studies measure people’s behaviors over time, specifically their opinions, feelings, emotions, and thoughts. Panel studies are a type of prospective study.

Cohort Study

  • A cohort study is a type of longitudinal study that samples a group of people with a common characteristic.
  • Researchers observe a population based on the shared experience of a specific event, such as birth, geographic location, or historical experience. These studies are typically used among medical researchers.

Retrospective Study

  • In a retrospective study , researchers either collect data on events that have already occurred or use existing data that already exists in databases, medical records, or interviews to gain insights about a population.

Allows researchers to look at changes over time

Because longitudinal studies observe variables over extended periods of time, researchers can use their data to study developmental shifts and understand how certain things change as we age.

High validation

Since objectives and rules for long-term studies are established before data collection, these studies are authentic and have high levels of validity.

Eliminates recall bias

Recall bias occurs when participants do not remember past events accurately or omit details from previous experiences.


The variables in longitudinal studies can change throughout the study. Even if the study was created to study a specific pattern or characteristic, the data collection could show new data points or relationships that are unique and worth investigating further.


Costly and time-consuming.

Longitudinal studies can take months or years to complete, rendering them expensive and time-consuming. Because of this, researchers tend to have difficulty recruiting participants, leading to smaller sample sizes.

Large sample size needed

Longitudinal studies tend to be challenging to conduct because large samples are needed for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

Participants tend to drop out

Not only is it a struggle to recruit participants, but subjects also tend to leave or drop out of the study due to various reasons such as illness, relocation, or a lack of motivation to complete the full study. This tendency is known as selective attrition and can threaten the validity of an experiment. For this reason, researchers using this approach typically recruit many participants, expecting a substantial number to drop out before the end.

Report bias is possible

Longitudinal studies will sometimes rely on surveys and questionnaires, which could result in inaccurate reporting as there is no way to verify the information presented.

How to Perform a Longitudinal Study

When beginning to develop your longitudinal study, you must first decide if you want to collect your own data or use data that has already been gathered.

Using already collected data will save you time, but it will be more restricted and limited than collecting it yourself. When collecting your own data, you can choose to conduct either a retrospective or prospective study .

In a retrospective study, you are collecting data on events that have already occurred. You can examine historical information, such as medical records, in order to understand the past. In a prospective study, on the other hand, you are collecting data in real-time. Prospective studies are more common for psychology research.

Once you determine the type of longitudinal study you will conduct, you then must determine how, when, where, and on whom the data will be collected.

A standardized study design is vital for efficiently measuring a population. Once a study design is created, it is important that researchers maintain the same study procedures over time to uphold the validity of the observation.

A schedule should be maintained, complete results should be recorded with each observation, and observer variability should be minimized.

Researchers must observe each subject under the same conditions in order to compare them. In this type of study design, each subject is the control.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies and cross-sectional studies are two different observational study designs where researchers analyze a target population without manipulating or altering the natural environment in which the participants exist.

Yet, there are apparent differences between these two forms of study. One key difference is that longitudinal studies follow the same sample of people over an extended period of time, while cross-sectional studies look at the characteristics of different populations at a given moment in time.

Longitudinal studies tend to require more time and resources, but they can be used to detect cause-and-effect relationships and establish patterns among subjects.

On the other hand, cross-sectional studies tend to be cheaper and quicker but can only provide a snapshot of a point in time and thus cannot identify cause-and-effect relationships.

Both types of studies are valuable for psychologists to observe a given group of subjects. Still, cross-sectional studies are more beneficial for establishing associations between variables, while longitudinal studies are necessary for examining a sequence of events.

Frequently asked questions

1. are longitudinal studies qualitative or quantitative, 2. what’s the difference between a longitudinal and case-control study, 3. does a longitudinal study have a control group.

Cook, N. R., & Ware, J. H. (1983). Design and analysis methods for longitudinal research. Annual review of public health, 4, 1–23. https://doi.org/10.1146/annurev.pu.04.050183.000245

Fitchett, G., Rybarczyk, B., Demarco, G., & Nicholas, J.J. (1999). The role of religion in medical rehabilitation outcomes: A longitudinal study. Rehabilitation Psychology, 44, 333-353.

Harvard Second Generation Study. (n.d.). Harvard Second Generation Grant and Glueck Study. Harvard Study of Adult Development. Retrieved from https://www.adultdevelopmentstudy.org.

Le Mare, L., & Audet, K. (2006). A longitudinal study of the physical growth and health of postinstitutionalized Romanian adoptees. Paediatrics & child health, 11(2), 85-91.

Luo, Y., Hawkley, L. C., Waite, L. J., & Cacioppo, J. T. (2012). Loneliness, health, and mortality in old age: a national longitudinal study. Social science & medicine (1982), 74(6), 907–914. https://doi.org/10.1016/j.socscimed.2011.11.028

Marques, S. C., Pais-Ribeiro, J. L., & Lopez, S. J. (2011). The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents: A two-year longitudinal study. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 12(6), 1049–1062. https://doi.org/10.1007/s10902-010-9244-4

Sidney W.A. Dekker & Wilmar B. Schaufeli (1995) The effects of job insecurity on psychological health and withdrawal: A longitudinal study, Australian Psychologist, 30:1,57-63, DOI: 10.1080/00050069508259607

Stice, E., Mazotti, L., Krebs, M., & Martin, S. (1998). Predictors of adolescent dieting behaviors: A longitudinal study. Psychology of Addictive Behaviors, 12(3), 195–205. https://doi.org/10.1037/0893-164X.12.3.195

Tegan Cruwys, Katharine H Greenaway & S Alexander Haslam (2015) The Stress of Passing Through an Educational Bottleneck: A Longitudinal Study of Psychology Honours Students, Australian Psychologist, 50:5, 372-381, DOI: 10.1111/ap.12115

Thomas, L. (2020). What is a longitudinal study? Scribbr. Retrieved from https://www.scribbr.com/methodology/longitudinal-study/

Van der Vorst, H., Engels, R. C. M. E., Meeus, W., & Deković, M. (2006). Parental attachment, parental control, and early development of alcohol use: A longitudinal study. Psychology of Addictive Behaviors, 20(2), 107–116. https://doi.org/10.1037/0893-164X.20.2.107

Further Information

Schaie, K. W. (2005). What can we learn from longitudinal studies of adult development?. Research in human development, 2(3), 133-158.

Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal studies. Journal of thoracic disease, 7(11), E537.

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Challenges in Conducting an Explorative, Descriptive, Longitudinal Study, Including Nine Specialized Rehabilitation Centers in Seven Countries

Prospective, descriptive international multicenter studies are complex but provide important data that may be generalized to a higher degree on specific themes like, in our case, specialized rehabilitation of stroke patients. However, there are many considerations in the process of collaboration, collecting data, and long-term follow-up. This case study provides some insight into the complexity and practicalities of performing a longitudinal descriptive study in an international multicenter study.

Learning Outcomes

By the end of this case, students should be able to


Sunnaas Rehabilitation Hospital is a specialized rehabilitation hospital, working with severely injured and sick persons postacutely. For many years, there has been an international collaboration called Sunnaas International Network (SIN), an international network working with the same severe neurological rehabilitation groups. This collaboration started in 1991, when China and Norway signed a Letter of Intent to cooperate within rehabilitation medicine, both in theory and in practice. Since then, several delegations from both sides met to exchange knowledge in rehabilitation medicine. In 1999, this collaboration was extended with Rusk Rehabilitation Institute in New York, thus starting the China–the U.S.–Norway rehabilitation group. The network was further expanded in 2005 with several other cooperating countries: Sweden, Russia, Israel, and Palestine. The involved clinics in these countries were all specialized rehabilitation centers, but diverse in terms of organization, level of technical standard of equipment, academic level of health personnel and cultural backgrounds.

During the seminar in 2011, at Rusk Rehabilitation Institute in New York, it was agreed to initiate a fellow research project, to raise the bar from exchange of knowledge between clinics to evaluate to what extent this knowledge was implemented in the different clinics in the SIN network. The following year, in the Tel Aviv meeting 2012, a research protocol was agreed upon where the focus was on stroke rehabilitation. The background was an interest of describing what the different clinics meant by specialized rehabilitation of stroke patients, and also to study the outcomes after specialized rehabilitation in the different centers.

Project Overview and Context

Stroke is a disease associated with the highest odds of reporting severe disability and might be considered to be the most common cause of complex disability in the adult population ( Feigin, Lawes, Bennett, Barker-Collo, & Parag, 2009 ; Langhammer et al., 2015 ). Consequently, many individuals with stroke are in need of rehabilitation, some also of specialized rehabilitation. Specialized rehabilitation has been defined by the British Society of Rehabilitation Medicine ( BSRM; 2015 ) as “services that support patients with complex disability, whose rehabilitation needs are beyond the scope of their local rehabilitation services.” Specialized rehabilitation services consist of a combination of individual and group-based interventions with the goal of stimulating social interaction, communication, life, work skills, and indirectly or directly, influence perceived quality of life (QoL) and life satisfaction.

The SIN stroke project had as a goal to describe how all these goals were handled and to what extent goals were achieved in the different clinics, with the differences in methods and organizations of the involved clinics. In this study, we focused on describing the process in obtaining the QoL data in a multicultural setting with the different attitudes, understandings, and perceptions of life satisfaction.

QoL is a broad multidimensional concept that usually includes both objective and subjective, positive and negative aspects of life ( Lau & McKenna, 2001 ). However, the concept is criticized as being vague. Life satisfaction, on the contrary, is considered to be purely subjective and related to goals ( Fugl-Meyer, Melin, & Fugl-Meyer, 2002 ). It is defined as “the degree to which an individual experience himself as being able to attain his goals.” Consequently, a person will be satisfied with a life-domain when aspirations and achievements are met but dissatisfied if aspirations exceed his or her achievements. This was in line with the aims of the study, where the goals were to investigate how the individuals regained skills, independence, and personal satisfaction after specialized rehabilitation in the different clinics.

A major stroke has severe consequences for the individual, both in a physical and in a psychological sense. Many people will find themselves in a difficult economic situation after a major stroke. Many cannot go back to work ( Andersen, Christensen, Kirkevold, & Johnsen, 2012 ; Baldwin & Brusco, 2011 ). This has implications both for the individuals and for their families and may influence life satisfaction. There are reasons to believe that cultural context such as social, educational, religious, and linguistically diversities may influence how rehabilitation services are provided, but also how life satisfaction is perceived, reflecting personal aims.

As the study was performed in clinics with multicultural and organizational differences, and with participating persons with varied knowledge and educational levels in terms of research, a simple descriptive explorative design was aspired. This, we believed, would enhance recruitment of informants, participation, implementation, and adherence to the protocol.

Section Summary

Research Design

The design was a prospective, descriptive study of the specialized rehabilitation of stroke patients in rehabilitation institutions in Norway, People’s Republic of China, the United States, Russia, Israel, Palestine, and Sweden. Patients with a primary diagnosis of stroke consecutively attending one of these institutions for specialized rehabilitation were invited to participate in the study. The study was designed in accordance with the Strengthening the Reporting of Observational studies in Epidemiology ( STROBE; 2009 ) checklist.

In accordance with recommendations, the project was registered in ClinicalTrials.gov (NCT01732679).

At first, the characteristics of the specialized rehabilitation units in the different countries, the therapies provided and the respective principles for admission and discharge were described ( Langhammer et al., 2015 ). This provided an overview of current practice of specialized stroke rehabilitation in the participating countries, which was useful to understand the theoretical aspects of organization of services and methods applied.

Ethical approval and information to the participants were developed in accordance with the Helsinki declaration ( World Medical Association, 2018 ).

Study-specific files and procedures were developed to support the standardization of recruitment, tests, test procedures, meetings (Skype, physical meetings, workshops, visits to respective clinics by the project leader and assistant), and description of analysis of the data.

An investigator and a contact person, local team, were appointed at each participating clinic. The investigator had the overall responsibility and control in the clinic for collaboration, recruitment, and standardization. The contact person was responsible for testing, keeping time-schedule, ensuring that standardized procedures were followed, and was in regular contact/“meetings” (every 2 weeks) with the project core team at Sunnaas. The project core team consisted of three persons, head of research department/administrative leader, principle investigator/executive leader, and project coordinator/responsible for contact and follow-up with participating clinics.

The nine clinics providing data were Sunnaas Rehabilitation Hospital, Norway; China Rehabilitation and Research Center (CRRC), Beijing, PR China; Bayi Rehabilitation Center, Chengdu, PR China; Rusk Rehabilitation Institute, New York, USA; Policlínica nr 2, Petrozavodsk, Karelian Republic, Russian Federation; Sheba Medical Center, Tel Aviv, Israel; Bethlehem Arab Society Rehabilitation (BASR), Palestine; El Wafa Hospital, Gaza, Palestine; and Högsbo Hospital, Gothenburg University, Sweden. A specific questionnaire was developed in collaboration of the team members, to gather information of organization of services, services provided from the administration and rehabilitation staff.

Patients with a primary diagnosis of stroke as defined by the World Health Organization ( Aho et al., 1980 ), irrespective of age and gender, were invited to enroll in the study, as they consecutively attended one of the nine institutions for specialized rehabilitation. Inclusion criteria were persons with stroke admitted to specialized rehabilitation and willing to participate in the study. Exclusion criteria were subarachnoid hemorrhage, tumor, or another severe medical condition in combination with stroke. The patients had to be able to cooperate in performing the planned assessments. Information to participants was given, both in written and in verbal forms, in accordance with the Helsinki Declaration ( World Medical Association, 2018 ). Patients could withdraw from the study at any time during the study. The contact person in each clinic was the key person for collecting the patient data, such as demographic information, and follow-up data that are possible changes in dependence, motor function, and life satisfaction.

Sample Size

A priori, the power of the study was calculated with respect to the activities of daily living (ADL). A sample size of 30 patients in each center would allow us to detect a change in score of mean 15% on the Barthel index (BI) or the functional independence measure (FIM) total score. This was considered a meaningful clinical change, with a power of 90% and p < .05. However, no data existed for life satisfaction, so the same power calculation was applied. This may result in an underpowered study and may jeopardize generalization of the results obtained. However, the results may serve as a pilot for upcoming studies on the same theme.

Outcome Measures

General descriptive data of the collaborating centers (capacity, admission criteria, services provided for patients, determinants for length of stay, having performed clinical research earlier) and descriptive data of patients participating in the study (gender, age, residential condition, occupational situation, and formal education) were recorded. In addition to a description of therapies provided to patients in terms of frequency, intensity, time, and type were registered. The primary outcome measure for the patients was the BI or the FIM, to assess the performance of the ADL. Secondary outcome measures were the Life Satisfaction Questionnaire–11 (LISAT-11) ( Spinal Cord Injury Research Evidence [SCIRE] Project, 2019 ), the Modified Rankin Scale (mRS), and the National Institutes of Health Stroke Scale (NIHSS), for measuring the degree of disability or dependence, and a structured questionnaire focusing on the patient’s social situation performed as an interview. Tests were performed at baseline on admission to rehabilitation, 18–22 days into rehabilitation (BI/FIM), discharge, and 6 and 12 months after discharge.

The generic LISAT-11 ( SCIRE Project, 2019 ) entails one global item and 10 domain-specific items capturing life satisfaction. LISAT-11 may be self-administered or used as an interview tool, rated on a six-step ordinal scale ranging from 1 = very dissatisfying to 6 = very satisfying . In the present study, LISAT-11 was used in a one-to-one interview between tester and patient. The scale had been tested for reliability and validity, and related normative data existed for a Swedish population, for patients with chronic pain, acquired brain injury, spinal cord injuries, and stroke ( Boonstra, Reneman, Stewart, & Balk, 2012 ; Fugl-Meyer et al., 2002 ; Silvemark, Källmén, Portala, & Molander, 2008 ; Viitanen, Fugl-Meyer, Bernspång, & Fugl-Meyer, 1988 ; Visser et al., 2015 ). Results were categorized as satisfied for scores ranging from 5 to 6 and dissatisfied for scores ranging from 1 to 4 ( Fugl-Meyer et al., 2002 ). LISAT-11 can also be divided into four gender-independent descriptive domains: closeness (sexual life, partner relationship, and family life), health (ability to care for self/ADL, physical and mental health), spare time (leisure, contact with friends and acquaintances), and provision (vocational and financial situations) ( Fugl-Meyer et al., 2002 ). For simplicity, a total score, summarizing all item scores ranging from 1 = dissatisfied to a maximum of 66 = satisfied , was added ( Visser et al., 2015 ).

The contact persons, as described earlier, continuously reported data to the project team. One copy of data was stored at the centers involved; the other copy was sent to the project team for plotting and filing.

Statistical Analysis

The content of specialized rehabilitation was described and compared with a standard, the BSRM (2015) , qualitatively.

Patients’ quantitative data were analyzed with descriptive and analytical statistics: M, SD , Student’s t test, Mann–Whitney U test for comparisons of the performance at baseline, discharge, and 6 and 12 months post discharge ( Altman, 1991 ). The semi-structured questionnaire, performed as an interview, was analyzed as categorical data. Data were presented in frequencies, percentages, and associations from cross-tabular calculations.

All the analysis were performed using IBM SPSS, version 23. Descriptive statistics were used to identify patients’ demographics, and test results were given in percentages and M with SD . A correlation analysis between item “life as a whole” and the total score was performed to establish associations.

Repeated measurements were performed with a general linear model and Bonferroni correction to analyze possible differences between clinics and models of rehabilitation in perceived life satisfaction on the different time points. Disability and occupational status were entered as covariates.

To evaluate explanatory factors for life satisfaction, a linear regression analysis was performed. LISAT-11 items with low and significantly different scores were consecutively entered as dependent variables, and age, gender, marital status, occupation, country, medical condition, NIHSS, mRS, ADL, models of collaboration, length of stay, amount of therapy, and hours in therapy were entered as independent variables to establish explanatory internal and external factors. The significance level was set to p p < .01.

In addition, logistic regression analyses were performed with “life as a whole” and total score at discharge as dependent variables. Age, gender, marital status, occupation, country, medical condition, NIHSS, mRS, ADL, models of rehabilitation, length of stay, amount of therapy, and hours in therapy were used as independent factors for the evaluation of predictors for outcomes.

Approval from the local ethical committees was obtained in all the participating clinics and from the Regional Ethical Committee of Health in South-East Norway (2012/768). Patients included in the study were informed verbally and in writing about the aim of the study. The contact person provided forms for written consent for all participants in all clinics.

Research Practicalities

As described previously, within the SIN, it was agreed to initiate a fellow research project in 2011. The following year, a research protocol was agreed upon with a focus on stroke rehabilitation. A common start date was set and a recruitment period of 3 months, after which it was expected that all clinics had consecutively recruited 30 patients, in total 270 patients. Patients were to be tested at baseline, 18–22 days into rehabilitation, at discharge, and 6 months post discharge was initially decided. Recruitments of patients started as soon as ethical approval was given at the respective clinics.

The protocol had to be revised because some institutions had problems, and the recruitment period dragged out for a few clinics. Consequently, the recruitment was prolonged, and in addition to the 6-month follow-up, a revision with a final follow-up 12 months post discharge was added.

The research team consisted of medical staff at nine different clinics in seven countries, originating from the Network (SIN). A strength with the teams was that all were engaged in the rehabilitation of stroke patients. Of the 22 members of the team, 14 were medical doctors, four occupational therapists (OTs), one registered nurse, two psychologists, and one psychiatrist. All contact persons had long experience from stroke rehabilitation, but the experience with research varied. As for academic level, five had a PhD, three had a master’s, and one had a bachelor’s degree. Only one country had English as mother tongue, other languages were Chinese, Russian, Hebrew, Arabian, Swedish, and Norwegian, which of course led to translational issues. As a result, the standardization of protocols and information was crucial in addition to close monitoring, contact, and provision of information from project leader group.

The agreement was to use BI or FIM for ADL, mRS, NIHSS to measure stroke disability and LISAT-11 as standardized outcome measures.

The ADL, severity, and disability scales were used in most clinics on a more or less regular basis and were therefore easy to implement for all participating countries and clinics. However, QoL and life satisfaction were not regularly measured. Therefore, LISAT-11 was proposed as a new tool that could be explored in this setting. The checklist was seemingly easy to use, intuitive in its scoring, and it was assumed easy to introduce in all clinics. In addition, the tool was new to the majority so it would be a good opportunity to discuss pros and cons in a multicultural and multinational setting. Only one hospital (Sweden) had experience with and used LISAT-11. The scale was originally designed for persons with spinal cord injury and had recently been introduced in stroke research.

To standardize the process in all clinics, the scale had to be introduced and the testers had to be familiarized with LISAT-11. One of the developers of LISAT-11, professor Kerstin Fugl-Meyer, was affiliated to Sunnaas Rehabilitation Hospital. This was very helpful in the process of introducing, working with, discussing, and analyzing the data obtained with LISAT-11. At the first meeting in Tel Aviv, professor Fugl-Meyer introduced LISAT-11 to all participating clinics, and an open discussion of possible difficulties followed. In addition, the translational issues were discussed, but the LISAT-11 scale had already been translated into the majority of languages, except Chinese. For this purpose, a back-and-forth translation to Chinese was performed. This translation was tested for reliability and validity in an additional PhD related to the SIN stroke study; the results are submitted for publication ( Wang, Zhang, Fugl-Meyer, & Langhammer, 2019 ). Furthermore, the LISAT-11 checklist was discussed, and problematic issues addressed in workshops with the goal to have a standardized process were data could be assumed “true” and handled in a similar fashion in all clinics. Ethical considerations were an ongoing process.

A study-specific patient file was created to collect and standardize the process of obtaining the same medical, demographic, and outcome data cross-country. This file was carefully monitored versus source documents at each clinic by the project team.

Sunnaas Rehabilitation Hospital signed individual letter of agreement with each head of clinic to clarify what Sunnaas would pay and what was expected to be covered by each clinic, at the beginning of the study. The costs for all participants to be covered by Sunnaas were invitations to annual seminars and workshops, and benefits were participation, the possibility to be included on the author list (depending on contribution either as a coauthor according to the Vancouver agreement or as acknowledgment). After the accepted main publications of the protocol and primary outcomes, each clinic could publish its own reports based on data from its own hospital. Sunnaas covered costs for traveling for the project team to perform personal visits and monitoring in-house/clinics. Sunnaas covered publication costs and participation at international conferences for selected participants in the team if invited as speakers.

Method in Action

We emphasized to keep a close contact with the team from the very beginning. This procedure helped to foresee many challenges related to different political systems, different religious systems, different cultural conceptions, communication and understanding of language, time differences, and differences in theoretical background and research experiences. The project team arranged regular Skype and video conferences on individual basis as well as in plenum. Regular and frequent NEWS letters to inform and inspire the group covered the current status and shared useful experiences. During the annual workshops, specific issues were covered in depth within the team.

To strengthen our personal perceptions of the participating hospitals, we documented each clinic on video. A professional filmmaker followed the project leader during the in-house visits, and edited the material to a documentary, covering all participating hospitals. This video provided valuable additional knowledge to the written description of each clinic. In addition, the video was given to each clinic to be used when presenting our study in various fora.


Patient inclusion and data collection started as planned. However, the inclusion period had to be prolonged due to unforeseen events, such as the war between Gaza and Israel, leading to war-injured patients being prioritized before stroke patients, and Rusk Rehabilitation Institute was damaged and out of service for 8 months due to the hurricane Sandy. As a result, these clinics did not manage to recruit the stipulated number of patients.


We were aware that the LISAT-11 was a new instrument to most of the team, also to the project leaders. The checklist presents many sensitive questions, and therefore we were well prepared to train the staff at workshops dedicated to LISAT-11.

However, with the ADL measurements and stroke severity tests, we did not find it necessary to pay extra attention. We relied on the competence in staff who were familiar with these instruments from many years in stroke rehabilitation. We did not notice discrepancies in the local versions until we started plotting data. When asking for an explanation, it turned out that some translations into local language did not follow the original order of items.

Team-building and frequent meetings were priority one. Personal visits at each clinic were important to learn more about the daily life at each place, the working conditions, and to see in real life how the rehabilitation services were provided. Personal visits also allowed us to monitor source data together with the tester and to reveal protocol deviations, misunderstanding, and/or misinterpretation of some of the measurements and lacking information in the study-specific file. By discovering faults at site, it is easy and quick and nondramatic to clarify and correct.

A small project team who could easily meet and clarify issues and take quick decisions minimized the research staff and ensured efficiency and also reduced the costs. Sunnaas Rehabilitation Hospital prioritized this study, as it was the first clinical research within the SIN. We had economy to allow all team members to attend annual workshops over 5 years, and to participate at international conferences.

Practical Lessons Learned

When implementing an instrument new to most of the staff, a pilot study may be recommended. LISAT-11 was in regular use at only one clinic out of nine, and it was also new to the project lead. A well-conducted pilot study might have identified the most crucial problem areas. Considering the ongoing discussions over the years, a pilot would probably uncover many but not all problems.

A general challenge throughout the study was the fact that English was the second (or third) language for all but one clinic (USA), and the possibilities for misunderstanding and misinterpretation are many.

Recruitment and Adherence

External, not modifiable, factors influenced our study in three of the participating clinics. We therefore had to adjust the recruitment period. Internal factors, such as staff pregnancy and career move, led to retraining of new staff members. Some patients were reluctant to answer the LISAT-11, resulting in rejection of participation in the study. In addition, patients’ adherence to the 12-month follow-up was low in some countries, and a few study patients declined another interview.

Stroke may lead to cognitive as well as physical impairment, and many may have difficulties with reading, understanding words, and writing. How to perform the test? Patient responding on their own or together with the tester or family member? We reached consensus to let one of the test staff sit together with the patient and complete the questionnaire together.

The life satisfaction statements contain many sensitive questions not only for the patients but also for the testers. Physicians did not find a problem to ask any of the question while OTs and nurses did, so professional background may influence how the outcome measure is performed. Another issue may be “timing,” when the test is performed. In hospital, many participants had no experience of how these issues would influence their life.


There was no official manual to LISAT-11, and it was supposedly “easy to understand and to use.” We arranged workshops with the author of the test to analyze and discuss the questions and the scoring. Still, people understood some of the statements in different ways as well as the score.

Irrespective of English speaking or not, linguistic discussions and confusions were revealed when asked to define the items and statements regarding “Vocation,” “Retired,” “Leisure,” and “Sexual life.” In China, to be a farmer is not considered a “Vocation/profession” because they have no formal education, and in most countries, “Retired” is not similar to a “Vocation.” Furthermore, statements “Vocation” and “Leisure” were mixed.

Item and statement “Sexual life” revealed different opinions on sexual behavior and definitions but also cultural and social barriers in asking intimate questions. For example, team members and patients have different understanding depending on background, culture, religion, and asking the same questions in communist China and Russia, Christian Bethlehem, Arabian Muslim Gaza, secularized states like Israel, the United States, Sweden, and Norway will reflect this variety. In Arab countries or in China, you do not ask young person about sexual life and you do not expect older people to have any sexual life. This statement in particular scored as “Not Applicable.” In addition, in China, the order, when performing the interview, was changed because of this. Item “Partner relationship” was asked before “Sexual life,” so that the question was omitted if the person was single or a widower, violating analysis.

To find a test being perceived the same way at all nine clinics in seven countries would probably not be doable, but as long as you are aware of the many differences, you may be able to control the questions. In the analysis, the semi-structured interviews were matched with the LISAT-11 responses to control for major deviations. Articles for publication were distributed within the team for feedback before finalizing, and this was of particular importance on the article on LISAT-11.

This presentation gives experiences from a multinational study on stroke patients. The design was a descriptive, explorative cross-sectional study with written procedures, standardized files, and outcome measures. The recruitment period was set short and we wanted a swift follow-up. Some of the challenging experiences were the recruitment took longer time, test persons were “exchanged” in some clinics, some tests were misunderstood/misinterpreted. By implementing changes in scoring of the outcome measure LISAT-11, we made comparisons with other studies using the same scale difficult.

Positive experiences were the following: the teambuilding was very good and made it possible for us to “turn the boat” when we discovered differences and difficulties described above. From the start, the decision-making process was established, with predefined report periods and meetings. A small core team consisting of the same three persons from beginning to end ensured a smooth conduct of the study with no interruptions. We had clear, short, and close communication levels despite time differences within the team. Communication was encouraged to be open, free, and friendly. It is important with “ownership”—all clinics and staff members felt part and ownership of the project. Follow-up projects were encouraged, and the team was supportive. Financial issues were decided before start of the project.

Lessons learned are that we should have made a priori pilot to test outcomes and recruitment adherence. We should have used these experiences in preparing for a main study to avoid difficulties encountered.

It is of crucial importance to maintain an open communication and to provide guidelines on all aspects of the trial procedures, to assist in a teamwork approach to overcome barriers for a successful completion of an international multicenter trial. This study demonstrates that even nontraditional partners can succeed in research cooperation, thus giving encouragement to the planning of other complex projects.

Classroom Discussion Questions

Multiple Choice Quiz Questions

1. Which option would seriously influence the implementation of a planned multi-center trial?

Correct Answer

Feedback: Well done, correct answer.

2. What would enhance fidelity to a complex trial?

Incorrect Answer

Feedback: This is not the correct answer. The correct answer is B.

3. What would improve implementation?

Feedback: This is not the correct answer. The correct answer is A.

Declaration of Conflicting Interests

The Authors declare that there is no conflict of interest.

Further Reading

Web resources.

The website for complex interventions can be found here: http://www.mrc.ac.uk/documents/pdf/complex-interventions-guidance/

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Module 1: Introduction to Psychology & Psychology Research

Descriptive research, what you’ll learn to do: describe the strengths and weaknesses of descriptive, experimental, and correlational research.

Three researchers review data while talking around a microscope.

If you think about the vast array of fields and topics covered in psychology, you understand that in order to do psychological research, there must be a diverse set of ways to gather data and perform experiments. For example, a biological psychologist might work predominately in a lab setting or alongside a neurologist. A social scientist may set up situational experiments, a health psychologist may administer surveys, and a developmental psychologist may make observations in a classroom. In this section, you’ll learn about the various types of research methods that psychologists employ to learn about human behavior.

Psychologists use descriptive, experimental, and correlational methods to conduct research. Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.

Experiments are conducted in order to determine cause-and-effect relationships. In ideal experimental design, the only difference between the experimental and control groups is whether participants are exposed to the experimental manipulation. Each group goes through all phases of the experiment, but each group will experience a different level of the independent variable: the experimental group is exposed to the experimental manipulation, and the control group is not exposed to the experimental manipulation. The researcher then measures the changes that are produced in the dependent variable in each group. Once data is collected from both groups, it is analyzed statistically to determine if there are meaningful differences between the groups.

When scientists passively observe and measure phenomena it is called correlational research. Here, psychologists do not intervene and change behavior, as they do in experiments. In correlational research, they identify patterns of relationships, but usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

More on Research

If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the link HERE  (or on the link below) to watch a lecture given by MIT Professor John Gabrieli. Start at the 30:45 minute mark  and watch through the end to hear examples of actual psychological studies and how they were analyzed. Listen for references to independent and dependent variables, experimenter bias, and double-blind studies. In the lecture, you’ll learn about breaking social norms, “WEIRD” research, why expectations matter, how a warm cup of coffee might make you nicer, why you should change your answer on a multiple choice test, and why praise for intelligence won’t make you any smarter.

Learning Objectives

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

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

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While 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 New York Times video about their lives.

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 chapter: 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).

A photograph shows two police cars driving, one with its lights flashing.

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).

(a) A photograph shows Jane Goodall speaking from a lectern. (b) A photograph shows a chimpanzee’s face.

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 chapter 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.

A sample online survey reads, “Dear visitor, your opinion is important to us. We would like to invite you to participate in a short survey to gather your opinions and feedback on your news consumption habits. The survey will take approximately 10-15 minutes. Simply click the “Yes” button below to launch the survey. Would you like to participate?” Two buttons are labeled “yes” and “no.”

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 chapter: 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?

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students (Figure 1).

(a) A photograph shows stacks of paper files on shelves. (b) A photograph shows a computer.

Figure 1. A researcher doing archival research examines records, whether archived as a (a) hardcopy or (b) electronically. (credit “paper files”: modification of work by “Newtown graffiti”/Flickr; “computer”: modification of work by INPIVIC Family/Flickr)

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research. In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) (Figure 2).

A photograph shows pack of cigarettes and cigarettes in an ashtray. The pack of cigarettes reads, “Surgeon general’s warning: smoking causes lung cancer, heart disease, emphysema, and may complicate pregnancy.”

Figure 2. Longitudinal research like the CPS-3 help us to better understand how smoking is associated with cancer and other diseases. (credit: CDC/Debora Cartagena)

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

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Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on October 10, 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.

Descriptive research question examples

  • How has the Amsterdam housing market changed over the past 20 years?
  • Do customers of company X prefer product X or product Y?
  • 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?

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 analyzed 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 organization’s services


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 organization). 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 generalizable 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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