Longitudinal Study Design

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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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. It allows researchers to track changes and developments in the subjects over time.

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).

When designing longitudinal studies, researchers must consider issues like sample selection and generalizability, attrition and selectivity bias, effects of repeated exposure to measures, selection of appropriate statistical models, and coverage of the necessary timespan to capture the phenomena of interest.

Panel Study

  • A panel study is a type of longitudinal study design in which the same set of participants are measured repeatedly over time.
  • Data is gathered on the same variables of interest at each time point using consistent methods. This allows studying continuity and changes within individuals over time on the key measured constructs.
  • Prominent examples include national panel surveys on topics like health, aging, employment, and economics. 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 sharing a common experience or demographic trait within a defined period, such as year of birth.
  • 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.
  • Cohorts are identified and selected at a starting point (e.g. birth, starting school, entering a job field) and followed forward in time. 
  • As they age, data is collected on cohort subgroups to determine their differing trajectories. For example, investigating how health outcomes diverge for groups born in 1950s, 1960s, and 1970s.
  • Cohort studies do not require the same individuals to be assessed over time; they just require representation from the cohort.

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.
  • Appropriate when prospectively following participants from the past starting point is infeasible or unethical. For example, studying early origins of diseases emerging later in life.
  • Retrospective studies efficiently provide a “snapshot summary” of the past in relation to present status. However, quality concerns with retrospective data make careful interpretation necessary when inferring causality. Memory biases and selective retention influence quality of retrospective data.

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.

  • Data were collected for each child at three-time points: at 11 months after adoption, at 4.5 years of age and at 10.5 years of age. The first two sets of results showed that the adoptees were behind the non-institutionalised group however by 10.5 years old there was no difference between the two groups. The Romanian orphans had caught up with the children raised in normal Canadian families.
  • The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents (Marques Pais-Ribeiro, & Lopez, 2011)
  • The correlation between dieting behavior and the development of bulimia nervosa (Stice et al., 1998)
  • The stress of educational bottlenecks negatively impacting students’ wellbeing (Cruwys, Greenaway, & Haslam, 2015)
  • The effects of job insecurity on psychological health and withdrawal (Sidney & Schaufeli, 1995)
  • The relationship between loneliness, health, and mortality in adults aged 50 years and over (Luo et al., 2012)
  • The influence of parental attachment and parental control on early onset of alcohol consumption in adolescence (Van der Vorst et al., 2006)
  • The relationship between religion and health outcomes in medical rehabilitation patients (Fitchett et al., 1999)

Goals of Longitudinal Data and Longitudinal Research

The objectives of longitudinal data collection and research as outlined by Baltes and Nesselroade (1979):
  • Identify intraindividual change : Examine changes at the individual level over time, including long-term trends or short-term fluctuations. Requires multiple measurements and individual-level analysis.
  • Identify interindividual differences in intraindividual change : Evaluate whether changes vary across individuals and relate that to other variables. Requires repeated measures for multiple individuals plus relevant covariates.
  • Analyze interrelationships in change : Study how two or more processes unfold and influence each other over time. Requires longitudinal data on multiple variables and appropriate statistical models.
  • Analyze causes of intraindividual change: This objective refers to identifying factors or mechanisms that explain changes within individuals over time. For example, a researcher might want to understand what drives a person’s mood fluctuations over days or weeks. Or what leads to systematic gains or losses in one’s cognitive abilities across the lifespan.
  • Analyze causes of interindividual differences in intraindividual change : Identify mechanisms that explain within-person changes and differences in changes across people. Requires repeated data on outcomes and covariates for multiple individuals plus dynamic statistical models.

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, researchers must 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 to compare them. In this type of study design, each subject is the control.

Methodological Considerations

Important methodological considerations include testing measurement invariance of constructs across time, appropriately handling missing data, and using accelerated longitudinal designs that sample different age cohorts over overlapping time periods.

Testing measurement invariance

Testing measurement invariance involves evaluating whether the same construct is being measured in a consistent, comparable way across multiple time points in longitudinal research.

This includes assessing configural, metric, and scalar invariance through confirmatory factor analytic approaches. Ensuring invariance gives more confidence when drawing inferences about change over time.

Missing data

Missing data can occur during initial sampling if certain groups are underrepresented or fail to respond.

Attrition over time is the main source – participants dropping out for various reasons. The consequences of missing data are reduced statistical power and potential bias if dropout is nonrandom.

Handling missing data appropriately in longitudinal studies is critical to reducing bias and maintaining power.

It is important to minimize attrition by tracking participants, keeping contact info up to date, engaging them, and providing incentives over time.

Techniques like maximum likelihood estimation and multiple imputation are better alternatives to older methods like listwise deletion. Assumptions about missing data mechanisms (e.g., missing at random) shape the analytic approaches taken.

Accelerated longitudinal designs

Accelerated longitudinal designs purposefully create missing data across age groups.

Accelerated longitudinal designs strategically sample different age cohorts at overlapping periods. For example, assessing 6th, 7th, and 8th graders at yearly intervals would cover 6-8th grade development over a 3-year study rather than following a single cohort over that timespan.

This increases the speed and cost-efficiency of longitudinal data collection and enables the examination of age/cohort effects. Appropriate multilevel statistical models are required to analyze the resulting complex data structure.

In addition to those considerations, optimizing the time lags between measurements, maximizing participant retention, and thoughtfully selecting analysis models that align with the research questions and hypotheses are also vital in ensuring robust longitudinal research.

So, careful methodology is key throughout the design and analysis process when working with repeated-measures data.

Cohort effects

A cohort refers to a group born in the same year or time period. Cohort effects occur when different cohorts show differing trajectories over time.

Cohort effects can bias results if not accounted for, especially in accelerated longitudinal designs which assume cohort equivalence.

Detecting cohort effects is important but can be challenging as they are confounded with age and time of measurement effects.

Cohort effects can also interfere with estimating other effects like retest effects. This happens because comparing groups to estimate retest effects relies on cohort equivalence.

Overall, researchers need to test for and control cohort effects which could otherwise lead to invalid conclusions. Careful study design and analysis is required.

Retest effects

Retest effects refer to gains in performance that occur when the same or similar test is administered on multiple occasions.

For example, familiarity with test items and procedures may allow participants to improve their scores over repeated testing above and beyond any true change.

Specific examples include:

  • Memory tests – Learning which items tend to be tested can artificially boost performance over time
  • Cognitive tests – Becoming familiar with the testing format and particular test demands can inflate scores
  • Survey measures – Remembering previous responses can bias future responses over multiple administrations
  • Interviews – Comfort with the interviewer and process can lead to increased openness or recall

To estimate retest effects, performance of retested groups is compared to groups taking the test for the first time. Any divergence suggests inflated scores due to retesting rather than true change.

If unchecked in analysis, retest gains can be confused with genuine intraindividual change or interindividual differences.

This undermines the validity of longitudinal findings. Thus, testing and controlling for retest effects are important considerations in longitudinal research.

Data Analysis

Longitudinal data involves repeated assessments of variables over time, allowing researchers to study stability and change. A variety of statistical models can be used to analyze longitudinal data, including latent growth curve models, multilevel models, latent state-trait models, and more.

Latent growth curve models allow researchers to model intraindividual change over time. For example, one could estimate parameters related to individuals’ baseline levels on some measure, linear or nonlinear trajectory of change over time, and variability around those growth parameters. These models require multiple waves of longitudinal data to estimate.

Multilevel models are useful for hierarchically structured longitudinal data, with lower-level observations (e.g., repeated measures) nested within higher-level units (e.g., individuals). They can model variability both within and between individuals over time.

Latent state-trait models decompose the covariance between longitudinal measurements into time-invariant trait factors, time-specific state residuals, and error variance. This allows separating stable between-person differences from within-person fluctuations.

There are many other techniques like latent transition analysis, event history analysis, and time series models that have specialized uses for particular research questions with longitudinal data. The choice of model depends on the hypotheses, timescale of measurements, age range covered, and other factors.

In general, these various statistical models allow investigation of important questions about developmental processes, change and stability over time, causal sequencing, and both between- and within-person sources of variability. However, researchers must carefully consider the assumptions behind the models they choose.

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 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.

1. Are longitudinal studies qualitative or quantitative?

Longitudinal studies are typically quantitative. They collect numerical data from the same subjects to track changes and identify trends or patterns.

However, they can also include qualitative elements, such as interviews or observations, to provide a more in-depth understanding of the studied phenomena.

2. What’s the difference between a longitudinal and case-control study?

Case-control studies compare groups retrospectively and cannot be used to calculate relative risk. Longitudinal studies, though, can compare groups either retrospectively or prospectively.

In case-control studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies look at a single subject or a single case, whereas longitudinal studies are conducted on a large group of subjects.

3. Does a longitudinal study have a control group?

Yes, a longitudinal study can have a control group . In such a design, one group (the experimental group) would receive treatment or intervention, while the other group (the control group) would not.

Both groups would then be observed over time to see if there are differences in outcomes, which could suggest an effect of the treatment or intervention.

However, not all longitudinal studies have a control group, especially observational ones and not testing a specific intervention.

Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade & P. B. Baltes (Eds.), (pp. 1–39). Academic Press.

Cook, N. R., & Ware, J. H. (1983). Design and analysis methods for longitudinal research. Annual review of public health , 4, 1–23.

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. Pediatrics & 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.

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.

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.

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.

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.

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.

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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Developing longitudinal qualitative designs: lessons learned and recommendations for health services research

Lynn calman.

1 University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL, UK

Lisa Brunton

Alex molassiotis.

2 Hong Kong Polytechnic University, Hung Hom, Hong Kong

Longitudinal qualitative methods are becoming increasingly used in the health service research, but the method and challenges particular to health care settings are not well described in the literature.We reflect on the strategies used in a longitudinal qualitative study to explore the experience of symptoms in cancer patients and their carers, following participants from diagnosis for twelve months; we highlight ethical, practical, theoretical and methodological issues that need to be considered and addressed from the outset of a longitudinal qualitative study.

Key considerations in undertaking longitudinal qualitative projects in health research, include the use of theory, utilizing multiple methods of analysis and giving consideration to the practical and ethical issues at an early stage. These can include issues of time and timing; data collection processes; changing the topic guide over time; recruitment considerations; retention of staff; issues around confidentiality; effects of project on staff and patients, and analyzing data within and across time.


As longitudinal qualitative methods are becoming increasingly used in health services research, the methodological and practical challenges particular to health care settings need more robust approaches and conceptual improvement. We provide recommendations for the use of such designs. We have a particular focus on cancer patients, so this paper will have particular relevance for researchers interested in chronic and life limiting conditions.

Longitudinal qualitative research (LQR) has been an emerging methodology over the last decade with methodological discussion and debate taking place within social research [ 1 ]. Longitudinal qualitative research is distinguished from other qualitative approaches by the way in which time is designed into the research process, making change a key focus for analysis [ 1 ]. LQR answers qualitative questions about the lived experience of change, or sometimes stability, over time. Findings can establish the processes by which this experience is created and illuminates the causes and consequences of change. Qualitative research is about why and how health care is experienced and LQR focuses on how and why these experiences change over time. In contrast to longitudinal quantitative methodologies LQR focuses on individual narratives and trajectories and can capture critical moments and processes involved in change. LQR is also particularly helpful in capturing “transitions” in care; for example, while researchers are beginning to more clearly map the cancer journey or pathway [ 2 ] we less clearly understand the processes involved in the experience of transition along this pathway whether that be to long term survivor or living with active or advanced disease. Saldana [ 3 ] identifies the principles that underpin LQR as duration, time and change and emphasizes that time and change are contextual and may transform during the course of a study.

Holland [ 4 ] identifies four methodological models of LQR.

• Mixed methods approaches. LQR may be imbedded within case studies, ethnographies and within quantitative longitudinal studies such as cohort studies and randomized controlled trials. Mixed methods studies are the context of most LQR studies in healthcare [ 5 ].

• Planned prospective longitudinal studies. Where the analysis can be the individual or the family or an organization.

• Follow-up studies, where an original study of participants are followed up after a period of time.

• Evaluation studies, for policy evaluation.

LQR methodologies can be particularly useful in assessing interventions. LQR studies embedded within randomized controlled trials or evaluation studies, of often complex interventions, are used as part of process evaluation. This can help us to understand not just whether an intervention may work but the mechanisms through which it works and if it is feasible and acceptable to the population under study [ 6 ].

LQR is becoming more frequently used in health research. LQR has been used, for example, to explore the prospect of dying [ 7 ], journeys to the diagnosis of cancer [ 8 ] and living with haemodialysis [ 9 ]. Published papers report mainly interview based studies, sometimes called serial interviews [ 10 , 11 ] to explore change over time, although other data collection methods are used. Different approaches have been taken to collection and analysis of data, for example, the use of longitudinal data to fully develop theoretical saturation of a category in a grounded theory study [ 12 , 13 ]. Data is not presented as a longitudinal narrative but as contributing to the properties of a category.

There are limitations in the published literature. Analysis is complex and multidimensional and can be tackled both cross-sectionally at each time point to allow analysis between individuals at the same time as well as longitudinally capturing each individual’s narrative. Thematic analysis is widely used [ 13 - 15 ] but can lead to cross-sectional descriptive accounts (what is happening at this time point) rather than focusing on causes and consequences of change. Research founded on explicit theoretical perspectives can move beyond descriptive analysis to further explore the complexities of experience over time [ 16 ]. LQR generates a rich source of data which has been used successfully for secondary analysis of data [ 11 , 17 ].

How analysis with this multidimensional data can be integrated is a particular challenge and is not well described or reported in the literature [ 4 ]. Papers tend to focus on either the cross-sectional or longitudinal (narrative) data. This means that the longitudinal aspects of the study, time and change, are often poorly captured. In particular the reporting of cross-sectional data alone can lead to descriptions of each time point rather than focusing on the changes between time points. Studies may have the explicit aim to focus on one or other aspect of analysis and this will achieve different analysis and reporting. The addition of a theoretical framework can help to guide researchers during analysis to move beyond description.

The purpose of this paper is to reflect on the strategies used in an LQR programme and highlight ethical, practical, theoretical and methodological issues that need to be considered and addressed from the outset of a study, giving researchers in the field some direction and raising the debate and discussion among researchers on ways to develop and carry out LQR projects.

We have carried out over the past six years a large LQR programme of research about experiences of symptoms in cancer patients [ 18 - 25 ]. This included interviews with patients from eight cancer diagnostic groups (and their caregivers) from diagnosis to three, six and 12 months later. As researchers working for the first time with longitudinal qualitative data we developed our research design and analysis strategy iteratively throughout the project. We have a particular focus on cancer patients, so this paper will have particular relevance for researchers interested in chronic and life limiting conditions.

As we were completing the analysis and dissemination of this large programme of research we wished to reflect on our experience of a health services research LQR project. As members of the core research team we felt that we had developed a great deal of experience in the development and management of such a project. We felt that if we pooled our knowledge we could suggest some important lessons learned from our experience. The authors met at regular intervals to identify the key aspects of the researchers’ experience of conducting this LQR project that we considered were not well addressed within the current literature. Issues were identified through brainstorming sessions among the investigators and consideration of past formal discussions (recorded or not) during the project duration. A final complete list was presented and discussed in an open meeting with a group of qualitative researchers from a supportive care research team and further discussions took place. Common issues that are relevant to any qualitative research and for which there is significant literature where left out, and only issues that were closely linked with LQR remained in the list for further discussion. Alongide our experience and consultation with experienced qualitative researchers, we have also searched the literature to find out if there is any clear information on each issues/topic. Recommendations, thus, were both experience-based and literature based, although due to lack of or limited literature around some of the issues discussed, experience-based recommendations were more common. This paper was developed to give examples of how specific ethical and practical issues in the project were tackled so they might stimulate debate and discussion amongst LQR researchers.

We present the results of our discussions and suggested solutions below and these are summarized in Table ​ Table1 1 .

Summary of themes and suggested solutions

Ethical issues: participant Recruitment shortly after significant diagnosis Treating doctor assessed participant prior to approach by researcher.
Approached participants sensitively in order to build trust and develop relationships over long term
  Blurring of boundaries as relationships develop Agreed plans to manage participant initiated contact about e.g. their treatment or health status (researchers did not give advice but referred participant to relevant health professional)
  Potential for patients to become unwell or die during study Written distress policy for participants and the research team in place
Ongoing consent recorded over the life of the project
Ethical issues: researcher Developing relationships over time Prepared researchers to manage difficult topics and emotions during the interview, and how management might change as relationships deepen
Closure of relationships
Developed a supportive network for researchers (e.g. debriefing sessions post interview)
  Confidentiality – and sharing data over large research teams Written procedures for managing ad-hoc or informal contacts with participants.
Developed clear data transfer and management plans
  Management of participant fatigue in interviews Ensure as the interview schedule changes due to new emerging topics that it is not over burdensome. Find new ways to ask questions to avoid repetition (do not merely add more questions)
Involvement of service users in study design
Recruitment and retention of participants Some groups of patients had high levels of attrition due to natural history of disease Checked health status of participants before contacting them prior to next interview to ensure this was done sensitively
Careful thought should be given to heterogeneity of the sample. The time points at which data is collected may have to be managed differently for sub-groups
Time At what time points should data be collected? We made a pragmatic decision about this and time points were the same for all participants.
It may be more relevant to identify time points by key transitions in the patient’s journey or by consideration of previous literature or informed by theory
  Time should be explicitly included in the interview – to include changing illness perceptions Looking forwards and backwards in interviews moves away from linear notions of time
Encourage reflexivity in the participant as well as the researcher
Asking participants to reflect on their experience from the previous interview
Data collection and management of resources Management of time and resources – when working with a large data set Ensure adequate time is included in project plans for project management and communication with participants
  Funding for LQR Work with the funding bodies to consider LQR
  Research focus and topic guide evolves over time Flexibility, openness and responsiveness to the data and emerging analysis and interpretation is a key skill for the LQR researcher
Ask for advice about how to manage this from an ethics committee
Analyzing dataLQR data sets are large and complex and can be analyzed in multiple ways from different perspectivesEnsure adequate time to analyze data between interviews – even if analysis is preliminary
Consider analysis of data within each case and as comparison between cases
Consider if and how subgroups should be analysed – is there a strong theoretical or practical reason why some groups should be analysed separately?
Consider the contribution of a number of different analysis strategies to the data and their strengths and weaknesses
Consider analysing data in a number of different ways, to add alternative understandings of longitudinal data

Ethical issues: participant related

Patients with cancer may be vulnerable, with a high symptom burden and poor prognosis, but patients still value being able to contribute their views [ 10 , 26 ]. Longitudinal research with this patient group is important but some ethical issues are amplified by collecting in-depth data from the same participants over time. Particular issues have been identified as intrusion (into people’s lives), distortion (of experience due to repeated contact, personal involvement and closure of relationships) and dependency [ 4 ].

We wished to interview patients shortly after diagnosis, which is a critical point in the patient pathway. Sensitive recruitment of participants soon after a life changing diagnosis, such as cancer, is important in building relationships and establishing a long term commitment to a study. Although building relationships and developing trust is essential this adds complexity to the role of the researcher involved in longitudinal research. Both the researcher and the researched can be affected by their involvement over time [ 27 ]. We found that on occasion patients did contact the research team for advice or information relating to their diagnosis. It is important that a research team have plans in place to manage this sort of situation without detriment to the relationship with the participant. There was a clear written distress policy for interviews and participants were given information about local support in case they wanted this after the interview.

There was a significant risk in our research that patients would become too unwell to participate or die between interviews. We sought consent from participants to access medical records and were able to check the health status of participants prior to contacting the participants to make arrangements for the next interview to ensure this was done sensitively. Consent was an ongoing process and was given in writing prior to the first interview and consent was checked verbally prior to each subsequent interview and also during the interview if a participant became upset or was talking about a particularly sensitive issue. The participant would be reminded that the tape recorder could be switched off at any time and the interview could be terminated at any time. If upset the participant would be given time to recover before the researcher asked if it was acceptable to continue with the interview. These procedures were built into the study protocol and the application for ethical approval.

Ethical issues: researcher related

Researchers too can be affected by their role [ 27 ]. Despite good training and support protocols for researchers qualitative research can be emotionally challenging [ 27 ]. Building a relationship over time, hearing about distressing situations and the impact that diagnosis can have on everyday life and relationships is hard. Information may be disclosed to the researcher that has not been discussed with anyone else; this builds a bond between those involved. Researchers may see participants deteriorate and die. The research team needs to build a supportive network and procedures to ensure that researchers are well supported in their role. In our study we used debriefing for very stressful events and researchers had regular supervision with the study team. Peer support within the research team also proved important on a day to day basis. It has been suggested that professional counseling is made available for researchers for whom debriefing is not sufficient support [ 27 ].

Staff retention may be an issue over time. There is a tension between the need to build relationships with participants in difficult circumstances and researcher burn out. It is ideal that one researcher builds a relationship with a participant over time but due to staff turnover or sickness this may not always be possible. Changes in staffing on LQR projects need to be well managed; the participant should be made aware that a different researcher will interview them and the researcher should read through previous transcripts so that participants feel there is some continuity and they do not have to repeat their story.

“Escaping the field” [ 4 ] or closure of relationships that have been built over time requires thought. Participants in our studies were prepared for the longitudinal element and the closure of the relationships. Study information was clear so participants knew that they were going to be interviewed 4 times over the year, and researchers prepared participants for the last interview: when ringing to arrange last interview participants were reminded that it was the final visit. At the end of the last interview we asked participants how they had found the process of being involved in research and had an informal “debriefing” session with them. If patients died whilst on the study a card would be sent on behalf of the research team to offer condolences.

It is important to ensure the confidentiality is maintained throughout the project as personal details, such as addresses, may be kept for longer than in studies with a single data collection point. Any ad hoc correspondence, phone messages or emails, for example, from participants to update researchers on their condition, should be handled in line with ethical approval requirements. As data is collected over time and experiences may be bound in particular circumstances and contexts ensuring that participants are not identifiable becomes more pertinent. The “blurred boundaries” for example taking your “emotional work” home with you [ 27 ] may also need special attention in LQR. Wray et al. [ 27 ] report, in their study, taking telephone calls from participants at home and ensuring women got evidence based care. These are complex, grey areas in LQR and it may become harder to separate, or manage ethically, empathy as a human being and a wish to help people who are suffering, with the role of a researcher when relationships deepen over time. These issues may have implications for the confidentiality of participants’ identities and data.

Data may have to be shared across large teams; this may mean that the core research team loses control of the data set and it is important to ensure that all team members are working to the ethical principles agreed with the relevant ethics committee. Large volumes of data may be generated from LQR and consideration should be given to how this data is archived and stored for the required length of time stipulated by the university, hospital or other regulatory body. LQR data is a valuable resource for archiving, data sharing and secondary data analysis, and may be a requirement of some funding bodies. To date this has been more common for large qualitative population data sets and is a specialist service offered by some Universities. The correct ethical approval, and participant consent to this, should be sought at the outset.

It is important to consider how researchers will deal with participant fatigue; within quantitative studies much thought is given to the burden of lengthy repeated questionnaires, the same consideration should be made for LQR, particularly as new topics of interest may emerge during the course of the study and it is tempting just to add a few more questions to the interview. Focusing on the purpose of the research, finding different ways to ask questions can avoid repetition and participants anticipating questions and giving the “right” response [ 28 ]. It is also wise to involve patients or service users in the design of the research and ongoing management to get the participants’ perspective of burden and balance research interest with participants’ well being.

Recruitment and retention of participants

We were successful in the recruitment of participants to the study. Patients were identified by the clinical team at the research site and then approached by a member of the research team to give information about the study. Once participants were recruited to the study retention was satisfactory. Recruitment and retention are important in all longitudinal studies. In qualitative studies sufficient participants are required at the last time point to ensure data saturation particularly if any new themes become evident at this point. We also wished to interview carers and this created a significant number of interviews at follow-up. We eventually made the decision not to interview some carers at follow-up as data was saturated. This created some difficulty with carer participants who valued this ongoing opportunity to ventilate feelings. The oversampling at the beginning (in order to have an adequate number of subjects at the last interview) was not a successful technique and overstretched the researchers and the data collection process unnecessarily.

There were two groups of patients where attrition was particularly poor: lung cancer patients (where 18 were recruited and four finished the study) and brain cancer patients (where 11 started and only one patient completed the fourth interview). For both of these groups there was a significant drop off after the third time point at six months. These attrition rates were not unexpected and almost all of these participants withdrew because they were too unwell or had died; this type of attrition may be unavoidable in some patient groups. All breast and gynecology patients completed all four interviews. Hence, a more selective approach to over-recruitment at the beginning of a LQR project is advocated, basing such decision on the outlook of participants over the timeline of the project. In some LQR studies it might be appropriate to develop newsletters or a web site with news of the study for participants to sustain interest. Good researcher communication skills are required to develop trust and convey the importance of the project to participants in the initial stages of the project. We have field notes that suggest that participants found participation in the study beneficial and this may also have contributed to our successful retention rates in populations with better health and survival.

The attrition in the sample highlights the complexity of having a heterogeneous sample in longitudinal research. We were well aware at the outset of the different disease trajectories of the tumor groups but for the purposes of analysis we designed the data collection points to be the same for all patients. In retrospect this was not entirely appropriate as there were different disease and treatment trajectories within each diagnostic group. In future research we would think differently about timing of interviews and link it to, for example, critical incidents rather than having set time points. Careful thought should be given to heterogeneity of the sample; by sampling over a number of cancer diagnostic groups we complicated our analysis making it difficult to draw together the experiences of patients with different disease trajectories. It may have been a better strategy to sample for heterogeneity within, for example, patients with advanced cancer. While heterogeneity in qualitative research is a desirable sampling feature, in LQR it is the “change” in events that is of more importance, and depicting change in very heterogeneous populations may not be so meaningful. Hence, defining clearly what an appropriate sample is for a given LQR study and understanding the trajectory of this sample over time are highly important considerations.

Issues of time and timing are of importance. Longitudinal research often focuses on change: how does coping or experience change? or how do participants manage change over time? [ 1 ]. Quantitative longitudinal research, such as cohort studies, assumes linearity of experiences and that people may experience time in the same way. However, the notion of time in a disease trajectory is complex. The difference between clock time and embodied time (or the experience of time) of the cancer patient has been recently illustrated in lung cancer, and this research highlights the lack of relationship between these two conceptualizations of time [ 29 ]. The differences between research time and biographical time have been explored elsewhere too [ 1 ]. Thus, consideration needs to be given to how time is defined in the study by the participants and by the research team.

One of the central issues we faced in this study was about the nature of time. As discussed above we identified set time points for data collection at the outset. However, we discovered that it is important to balance the pragmatics of a research design with flexible notions of time. We had significant attrition after the data collection point at six months and in retrospect we had not factored in the short disease trajectories of some patients or that some patients may have different notions of time. It may have been more useful to identify potential turning points or defining moments, from initial interviews, previously published research or clinical understanding of disease and focus on those rather than identifying set time points. For example, we know that the end of treatment, be that palliative or curative, is a significant time for patients [ 30 , 31 ] but treatment duration may not fall neatly into the first three months after diagnosis. That said, the focus of interviews should not be about “concrete events, practices, relationships and transitions which can be measured in precise ways, but with the agency of individuals in crafting these processes [ 32 ], p 192.” However, defining moments do often lead to change, in experience, coping or relationships and are useful points to tap into participants’ experiences. However, on a practical level, it would have been very difficult with our large data set to keep track of these critical incidents for every participant and to be able to organize researcher appointments to conduct interviews.

Issues of time need to be explicitly placed within the interview, an aspect we could have strengthened in our study. Looking both forwards and backwards in time moves away from linear notions of time as discussed above, asking participants to reflect on the content of their previous interviews. One way of doing this may be to encourage participants to approach the interview with reflexivity [ 33 ], a concept we are familiar with as researchers but in longitudinal research may be as important for the participant. For example, an issue that seems important for participants in the short term may not prove to be as important in the long term with the benefit of hindsight or increased understanding of the context [ 34 ]. This tentative or provisional, often contradictory, understanding makes analysis complex. As researchers we must endeavour to understand these complexities and make sense of them.

McLeod [ 33 ] suggests that reflexivity within the interview did not work for all of her research participants (in a study of school children) and is a point worth pursuing as we further develop our understanding of this methodology with patients. Reflexivity on a health state is complex for patients and it has been suggested that interviewing the ill may pose particular difficulties for the researcher [ 35 , 36 ], [a]s sick people, participants are unfamiliar with their everyday worlds, and they are often incapable of describing their condition and perceptions, so that researchers have difficulty in obtaining data to comprehend, interpret and generally conduct their research. … When researching participants who are sick, these methodological problems result in decisions about the timing of data collection, challenges to validity and reliability, and debates about who should be conducting the research [ 35 ], p 538.

Longitudinal qualitative research may in some way solve some of these issues as researchers will have the chance to incorporate changing illness perceptions into data collection and analysis. Patients whose illness has a long term impact will develop vocabulary and a way of expressing their illness experience in a way that patients with an acute episode will not. These changing perceptions, often moving from a lay perspective to one of the patients managing and controlling their illness [ 37 ], needs to be factored into analysis.

Data collection and management of resources

One of the main difficulties with LQR is the time and resources that are required to undertake a study. Dealing with a large data set can bring logistical challenges and there is a significant amount of time spent on project management, keeping up to date with participants, sending reminders and checking on a patient’s status. Analysis between interviews, across the participants and longitudinally within the individual narrative, can be a significant challenge in LQR.

There are no guidelines about how long a longitudinal study should be (although at least 2 points are necessary to examine change [ 3 ]) or how often data needs to be collected; this should be determined by the processes and population under investigation and the research question. Many health/patient related studies are short in duration, one to two years, in comparison to LQR in the social sciences where issues, such as transitions in identity from child to adult, are investigated over decades. This may of course be because of differences in the issues/processes under investigation but may also reflect research funding in health care which is often limited to a fixed duration. This poses problems for a research team who wish to follow a population for a number of years and requires ongoing generation of funds to complete the research.

The topic guide and the focus of the interview may change over time, this may prove challenging when seeking ethical approval for a study. Ethics committees usually ask for all documentation including topic guides prior to giving an opinion. Our interview schedule had broad questions both to comply with ethical approval procedures and to allow participants to talk about what is important for them at the time of each interview. Example opening questions include “How have you been feeling physically this past month” or “How have you been feeling emotionally this past month”. Developing a relationship with an ethics committee and seeking guidance about how to approach this with the committee is advisable.

LQR is a prospective approach and therefore can give a different perspective on processes. Issues that seem very important at one time point may change with the perspective of time and processes may change the way experiences are viewed. One off qualitative interviews rely on recall, for example, asking about symptom experience at diagnosis when a patient is several months away from that point. There will always be some element of retrospective discussion in an LQR interview but with a focus on change over time, this can be aided by summarizing or reflecting on the previous interview. As data is collected prospectively, causation, the temporality of cause and effect, and the processes or conditions by which this happens can also be explored in the data [ 4 ].

As we describe below, the richness of the interview content and overwhelming amount of data made it difficult to analyze in-depth each interview before the next one, an issue also been reported in other studies [ 27 ]. When this is the case we would propose that a preliminary analysis and summary of the interview is made so that the next interview can commence with a recap of what was previously discussed. Subsequent interviews could start by the interviewer providing a short summary of themes they have identified from the last interview and asking the participant to reflect on this summary of experiences before moving on to ask how the participant is feeling now and what has changed for them since the last interview. This more selective interview approach in subsequent interviews may also decrease the amount of data collected, easing the analysis and making the data collected more focused and less overwhelming for the researcher. Indeed we have noticed that often subsequent interviews tended to be shorter than the initial one. This helps the researcher and participant to keep the focus on longitudinal elements, what has changed since last time, why has this happened? Preliminary analysis will also highlight emerging themes to be further pursued in later interviews.

Using LQR researchers can respond to a change in focus and interviews can be adapted to the individual narratives. This is particularly useful as at the outset it is often not clear what the important processes are over time. Thus much data collected in the initial stages may not be relevant in the emerging processes over time, and data collection necessarily will become more focused at later time points. Flexibility and responsiveness to the data and emerging analysis and interpretation is a key skill for the LQR researcher.

Analyzing data

Longitudinal qualitative data analysis is complex and time consuming. A longitudinal analysis occurs within each case and as comparison between cases. The focus is not on snapshots across time (a cross-sectional design will achieve this) but “to ground the interviews in an exploration of processes and changes which look both backwards and forwards in time [ 32 ], p194.”

Holland [ 4 ] synthesizes two approaches to analyzing data and suggests some questions to guide analysis. Firstly, framing questions focus on the contexts and conditions that influence changes over time, she gives the example, “what contextual and intervening conditions appear to influence and affect participant changes over time? [ 4 ].” Descriptive questions generate descriptive information about what kinds of changes occur, for example, “what increases or emerges through time? [ 4 ].” These two types of questions move the researcher forward to develop deeper levels of analysis and interpretation.

Data collection and analysis should be informed by the research question, data collection methods and theoretical perspective, if one is being used from the outset. It may be possible to anticipate whether cross-sectional or longitudinal analysis would be the most helpful method of answering the research question. Considering these issues at the outset may allow the researcher to be alert to themes in the data during analysis whilst keeping an open mind to emerging issues.

As described above we planned to analyze each interview before moving onto the next interview with each participant to allow reflexivity of the researcher and participant and to focus on “processes and changes” rather than snapshots. Due to the volume of data it was not always possible to do this and this is certainly a limitation of our work and may reflect the predominance of cross-sectional data in our reporting of the studies.

We decided to analyze each tumor group separately rather than across the whole sample as it was clear that there were significant differences in these populations due to different disease trajectories and symptom experience. There was a different analysis and theoretical perspective taken in each analysis reflecting that data from each tumor group. McLeod [ 33 ] suggests that the nature of longitudinal data means that multiple theoretical frameworks may be useful to analyses and interpretation and the use of different paradigms may lead to new insights and interpretations.

Interpretative Phenomenological Analysis was used in lung cancer analysis [ 21 ], Interpretative Description with lymphoma data [ 20 ], content or thematic analysis using Leventhal’s self-regulation theory, the theoretical framework for the study, was used for gynecological, brain, and head and neck cancer data analysis [ 18 , 22 , 23 ], and thematic narrative analysis for breast cancer patients, The above approach took into consideration the data analysis experience of the researchers involved or the type of information collected through the interviews. For example, the analysis of breast cancer patients’ accounts [ 25 ] lead itself to narrative analysis because the women expressed their feelings much more than other groups and we analysed the data through patient stories about their cancer journey; this fitted well with the approach to data generation and Frank’s [ 38 ] concept of the cancer journey was used as the theoretical lens though which data were analyzed. In data from other diagnostic groups the unit of analysis was often the whole interview, as in the case of patients with head and neck cancer, where coding units in the first interview were assessed for presence and information in subsequent interviews. This captured well some experiences over time, such as the continuous nature of fatigue and tiredness over time, or the attempts for maintaining normality which were evident only after T2, increasing in complexity at T3 and T4 [ 22 ]. Detailed practical examples are presented in the respective papers [ 18 - 25 ] and a summary of the themes alongside other qualitative research related to symptom experience of cancer patients is presented in a meta-synthesis of these data [ 39 ].

Our analyses have highlighted new insights into the symptom experiences of patients with cancer. Utilizing multiple analysis strategies and theoretical perspectives has its strengths and allows comparison and gives direction for reanalysis and further interpretation of this important research resource.


Through reflecting on and describing our experiences we have identified broad recommendations for undertaking LQR projects in health research which we hope will stimulate debate amongst qualitative researchers.

• We would recommend incorporating a theoretical perspective (if appropriate to the methodology), that encompasses concepts such as time or the experience of change. This may help researchers keep the analysis “alive” to longitudinal aspects of analysis and move beyond descriptions of experience at each time point to explore change between time points.

• Qualitative researchers are familiar with complex ethical issues involved in being in the field. However, there are some ethical issues that are amplified whilst undertaking LQR, and require careful consideration and planning, such as how relationships are built and sustained over time whilst adhering to ethical practices, how relationships are ended, maintaining confidentiality over time and managing distress in participant and researchers.

• Good project management is essential when working with large data sets. Ensure adequate time is included in project plans for project management and communication with participants.

• Developing good team working is important; there are advantages to working with large teams which may be an unfamiliar way of working for qualitative researchers. Different perspectives can be brought to bear on the analysis making it richer and generating new insights. Communication is particularly important when analysis is undertaken by researchers who have not been involved in collecting data.

• We would encourage researchers to consider multiple methods of analysis and secondary analysis within the same data set to explore the rich data that is generated.

• We have clearly identified that longitudinal research with patients with a poor prognosis and experiencing long term challenges is worthwhile. However, thought needs to be given to the timing of data collection and the heterogeneity of the sample. Support for participants and researchers, and any additional ethical considerations, should be built into protocols as there is an increased burden for all involved in LQR.

• We recommend that from the outset the research team should consider how the volume of data can be managed and consider practical issues such as timing of interviews so data can be transcribed and analyzed in time for the next round of interviews. This early analysis may help keep the focus on change and transitions rather than description of events.

•Funders of research may be unfamiliar to funding longitudinal qualitative research and recommend that a strong case for the added value of this method should be made.

This paper has explored our experience of LQR and highlighted areas where we have learned a great deal about the methodology. During this longitudinal project we developed expertise in managing practical and ethical issues, tried different analysis strategies to look for alternative ways of examining data and understanding the experience of participants. There have been successes in the strategies we have used and areas in retrospect that we could have worked differently. For example, ensuring sensitivity during initial recruitment and subsequent contacts, putting procedures in place from the outset of the study to manage issues such as patient distress during interviews and patient initiated contact regarding health issues during data collection all helped the researchers to build trusting relationships with participants. These factors, together with researcher continuity, were important in helping to maintain good recruitment rates for participants with better health and survival rates throughout the study.

It is important to note that findings were generated from one particular study and issues highlighted here reflect the conduct of this study. There are other methodological issues that may be illustrated better through other examples of LQR research and we would encourage researchers to publish methodological issues highlighted by their studies to strengthen debate in this area. Although we consider that there are general lessons to be learned from our experience, which can be usefully considered by other researchers, we acknowledge that there may be aspects of the study, particularly the heath status of the participants that will not necessarily be broadly relevant. For this reason we do consider that this paper will have particular relevance for researchers interested in chronic and life limiting conditions.

We found that when seeking guidance for the project published literature was limited in highlighting debates about LQR focusing on the reporting of findings rather than developing debate about this emerging methodology. Much of the methodological literature cited in this paper comes from the social science literature where there is a long standing tradition of LQR and where debates about LQR with schoolchildren or other healthy populations in society are well rehearsed. There is little literature that examines the methodology in the context of health services research and whether there are particular issues about following participants through the trajectory of their illness to recovery, living with impairments or death. This paper has started to highlight some of the areas where further methodological exploration would be valuable.

One of the ongoing debates in qualitative methodology is how quality and credibility are evaluated [ 40 , 41 ]. There is little debate about whether LQR poses additional questions about quality. We have highlighted where, for example, there may be heightened concerns about ethical conduct, and using multiple methods of analysis. Longitudinal analysis is complex and is often reported a-theoretically and descriptively [ 13 - 15 ] and this also has implications for the quality and credibility of LQR. It may be that established guidance for the evaluation of qualitative research can be utilised with LQR but little exploration of this can be found in the published literature. Summaries of the researcher’s interpretation of a data collected in a previous interview when discussed with participants at a subsequent interview can enhance the credibility of the data. We have highlighted some ways in which these aspects of LQR can be enhanced, and by providing a record of our experiences it can help to start standardising a process by which QLR can be conducted which can enhance the credibility of research and quality of data collected.

LQR is an increasingly utilised methodology in health services research, for example in the development and evaluation of complex health interventions or to study transitions in recovery or long term illness. The findings presented in this paper are important as they begin to identify areas of LQR where there is potential for debate and multiple perspectives on these would be valuable.

Additional research and inquiry is also essential to further develop the methodology. There is little published work about rigour in LQR, and it would be worth investigating whether additional elements should be added to accepted conceptualizations of the quality of qualitative research so judgments can be made about the rigour of research. Research to explore participants’ perspectives of being in a longitudinal study would be valuable as there may be additional burden to the participant, emotional and practical, of being involved in LQR. Eliciting participants’ insights into their experiences of participation may give us greater insight into the method itself.

This paper has highlighted specific methodological, practical and ethical issues identified in an LQR programme of research about experiences of symptoms in cancer patients in the first year after diagnosis. The study itself has highlighted useful insights into these experiences and allowed examination of data from multiple perspectives, but importantly has been an important learning opportunity of the research team. Next steps may include agreement among the qualitative research community about standardization of the process, identification of LQR research questions that would be distinct from what can be achieved from cross-sectional work, and influencing funders for the value and uniqueness of this methodological approach.

Competing interests

The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.

Authors' contributions

Conception of paper: AM, LC. Acquisition of original data: AM, LB. Interpretation of data: All authors. Drafting paper: LC. Critical revisions: AM, LB. Final approval: all authors.

Pre-publication history

The pre-publication history for this paper can be accessed here:


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Longitudinal Research Design

  • First Online: 04 January 2024

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descriptive longitudinal case study

  • Stefan Hunziker 3 &
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This chapter addresses longitudinal research designs’ peculiarities, characteristics, and significant fallacies. Longitudinal studies represent an examination of correlated phenomena over a period, and their analysis stresses changes over time. A longitudinal research design aims 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 by referring to overlapping and adjacent research designs.

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  • Longitudinal Study | Definition, Approaches & Examples

Longitudinal Study | Definition, Approaches & Examples

Published on 5 May 2022 by Lauren Thomas . Revised on 24 October 2022.

In a longitudinal study, researchers repeatedly examine the same individuals to detect any changes that might occur over a period of time.

Longitudinal studies are a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.

While they are most commonly used in medicine, economics, and epidemiology, longitudinal studies can also be found in the other social or medical sciences.

Table of contents

How long is a longitudinal study, longitudinal vs cross-sectional studies, how to perform a longitudinal study, advantages and disadvantages of longitudinal studies, frequently asked questions about longitudinal studies.

No set amount of time is required for a longitudinal study, so long as the participants are repeatedly observed. They can range from as short as a few weeks to as long as several decades. However, they usually last at least a year, oftentimes several.

One of the longest longitudinal studies, the Harvard Study of Adult Development , has been collecting data on the physical and mental health of a group of men in Boston, in the US, for over 80 years.

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The opposite of a longitudinal study is a cross-sectional study. While longitudinal studies repeatedly observe the same participants over a period of time, cross-sectional studies examine different samples (or a ‘cross-section’) of the population at one point in time. They can be used to provide a snapshot of a group or society at a specific moment.

Cross-sectional vs longitudinal studies

Both types of study can prove useful in research. Because cross-sectional studies are shorter and therefore cheaper to carry out, they can be used to discover correlations that can then be investigated in a longitudinal study.

If you want to implement a longitudinal study, you have two choices: collecting your own data or using data already gathered by somebody else.

Using data from other sources

Many governments or research centres carry out longitudinal studies and make the data freely available to the general public. For example, anyone can access data from the 1970 British Cohort Study, which has followed the lives of 17,000 Brits since their births in a single week in 1970, through the UK Data Service website .

These statistics are generally very trustworthy and allow you to investigate changes over a long period of time. However, they are more restrictive than data you collect yourself. To preserve the anonymity of the participants, the data collected is often aggregated so that it can only be analysed on a regional level. You will also be restricted to whichever variables the original researchers decided to investigate.

If you choose to go down this route, you should carefully examine the source of the dataset as well as what data are available to you.

Collecting your own data

If you choose to collect your own data, the way you go about it will be determined by the type of longitudinal study you choose to perform. You can choose to conduct a retrospective or a prospective study.

  • In a retrospective study , you collect data on events that have already happened.
  • In a prospective study , you choose a group of subjects and follow them over time, collecting data in real time.

Retrospective studies are generally less expensive and take less time than prospective studies, but they are more prone to measurement error.

Like any other research design , longitudinal studies have their trade-offs: they provide a unique set of benefits, but also come with some downsides.

Longitudinal studies allow researchers to follow their subjects in real time. This means you can better establish the real sequence of events, allowing you insight into cause-and-effect relationships.

Longitudinal studies also allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals.

Prospective longitudinal studies eliminate the risk of recall bias , or the inability to correctly recall past events.


Longitudinal studies are time-consuming and often more expensive than other types of studies, so they require significant commitment and resources to be effective.

Since longitudinal studies repeatedly observe subjects over a period of time, any potential insights from the study can take a while to be discovered.

Attrition, which occurs when participants drop out of a study, is common in longitudinal studies and may result in invalid conclusions.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study Cross-sectional study
observations Observations at a in time
Observes the multiple times Observes (a ‘cross-section’) in the population
Follows in participants over time Provides of society at a given point

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

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Lauren Thomas

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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

  • Differentiate between descriptive, experimental, and correlational research
  • Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
  • Describe the strength and weaknesses of archival research
  • Compare longitudinal and cross-sectional approaches to research

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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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas


Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

  • Describes specific characteristics in a population of interest
  • The most common forms are case reports and case series
  • In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
  • In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

  • This design is transverse where we take a specific sample at a specific time without any follow-up
  • It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
  • This design is easy to conduct
  • For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

  • We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
  • It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
  • Prospective : we follow the individuals in the future to know who will develop the disease
  • Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
  • This design is the strongest among the observational studies
  • For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

  • We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
  • This design is always retrospective
  •  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
  •  Relatively easy to conduct
  • For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

  • Also known as interventional studies
  • Can involve animals and humans
  • Pre-clinical trials involve animals
  • Clinical trials are experimental studies involving humans
  • In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

  • In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

A visual diagram describing the following. Two types of epidemiological studies are descriptive and analytical. Types of descriptive studies are case reports, case series, descriptive surveys. Types of analytical studies are observational or experimental. Observational studies can be cross-sectional, case-control or cohort studies. Types of experimental studies can be lab trials or field trials.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

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Very informative and easy understandable

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You are my kind of doctor. Do not lose sight of your objective.

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Wow very erll explained and easy to understand

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I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

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well understood,thank you so much

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Well understood…thanks

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Simply explained. Thank You.

' src=

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

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That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

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it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

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Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

' src=

Very helpful article!! U have simplified everything for easy understanding

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I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

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Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

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Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

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You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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  • Open access
  • Published: 01 October 2022

Qualitative longitudinal research in health research: a method study

  • Åsa Audulv 1 ,
  • Elisabeth O. C. Hall 2 , 3 ,
  • Åsa Kneck 4 ,
  • Thomas Westergren 5 , 6 ,
  • Liv Fegran 5 ,
  • Mona Kyndi Pedersen 7 , 8 ,
  • Hanne Aagaard 9 ,
  • Kristianna Lund Dam 3 &
  • Mette Spliid Ludvigsen 10 , 11  

BMC Medical Research Methodology volume  22 , Article number:  255 ( 2022 ) Cite this article

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Qualitative longitudinal research (QLR) comprises qualitative studies, with repeated data collection, that focus on the temporality (e.g., time and change) of a phenomenon. The use of QLR is increasing in health research since many topics within health involve change (e.g., progressive illness, rehabilitation). A method study can provide an insightful understanding of the use, trends and variations within this approach. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

This method study used an adapted scoping review design. Articles were eligible if they were written in English, published between 2017 and 2019, and reported results from qualitative data collected at different time points/time waves with the same sample or in the same setting. Articles were identified using EBSCOhost. Two independent reviewers performed the screening, selection and charting.

A total of 299 articles were included. There was great variation among the articles in the use of methodological traditions, type of data, length of data collection, and components of longitudinal data collection. However, the majority of articles represented large studies and were based on individual interview data. Approximately half of the articles self-identified as QLR studies or as following a QLR design, although slightly less than 20% of them included QLR method literature in their method sections.


QLR is often used in large complex studies. Some articles were thoroughly designed to capture time/change throughout the methodology, aim and data collection, while other articles included few elements of QLR. Longitudinal data collection includes several components, such as what entities are followed across time, the tempo of data collection, and to what extent the data collection is preplanned or adapted across time. Therefore, there are several practices and possibilities researchers should consider before starting a QLR project.

Peer Review reports

Health research is focused on areas and topics where time and change are relevant. For example, processes such as recovery or changes in health status. However, relating time and change can be complicated in research, as the representation of reality in research publications is often collected at one point in time and fixed in its presentation, although time and change are always present in human life and experiences. Qualitative longitudinal research (QLR; also called longitudinal qualitative research, LQR) has been developed to focus on subjective experiences of time or change using qualitative data materials (e.g., interviews, observations and/or text documents) collected across a time span with the same participants and/or in the same setting [ 1 , 2 ]. QLR within health research may have many benefits. Firstly, human experiences are not fixed and consistent, but changing and diverse, therefore people’s experiences in relation to a health phenomenon may be more comprehensively described by repeated interviews or observations over time. Secondly, experiences, behaviors, and social norms unfold over time. By using QLR, researchers can collect empirical data that represents not only recalled human conceptions but also serial and instant situations reflecting transitions, trajectories and changes in people’s health experiences, personal development or health care organizations [ 3 , 4 , 5 ].

Key features of QLR

Whether QLR is a methodological approach in its own right or a design element of a particular study within a traditional methodological approach (e.g., ethnography or grounded theory) is debated [ 1 , 6 ]. For example, Bennett et al. [ 7 ] describe QLR as untied to methodology, giving researchers the flexibility to develop a suitable design for each study. McCoy [ 6 ] suggests that epistemological and ontological standpoints from interpretative phenomenological analysis (IPA) align with QLR traditions, thus making longitudinal IPA a suitable methodology. Plano-Clark et al. [ 8 ] described how longitudinal qualitative elements can be used in mixed methods studies, thus creating longitudinal mixed methods. In contrast, several researchers have argued that QLR is an emerging methodology [ 1 , 5 , 9 , 10 ]. For example, Thomson et al. [ 9 ] have stated “What distinguishes longitudinal qualitative research is the deliberate way in which temporality is designed into the research process, making change a central focus of analytic attention” (p. 185). Tuthill et al. [ 5 ] concluded that some of the confusion might have arisen from the diversity of data collection methods and data materials used within QLR research. However, there are no investigations showing to what extent QLR studies use QLR as a distinct methodology versus using a longitudinal data collection as a more flexible design element in combination with other qualitative methodologies.

QLR research should focus on aspects of temporality, time and/or change [ 11 , 12 , 13 ]. The concepts of time and change are seen as inseparable since change is happening with the passing of time [ 13 ]. However, time can be conceptualized in different ways. Time is often understood from a chronological perspective, and is viewed as fixed, objective, continuous and measurable (e.g., clock time, duration of time). However, time can also be understood from within, as the experience of the passing of time and/or the perspective from the current moment into the constructed conception of a history or future. From this perspective, time is seen as fluid, meaning that events, contexts and understandings create a subjective experience of time and change. Both the chronological and fluid understanding of time influence QLR research [ 11 ]. Furthermore, there is a distinction between over-time, which constitutes a comparison of the difference between points in time, often with a focus on the latter point or destination, and through-time, which means following an aspect across time while trying to understand the change that occurs [ 11 ]. In this article, we will mostly use the concept of across time to include both perspectives.

Some authors assert that QLR studies should include a qualitative data collection with the same sample across time [ 11 , 13 ], whereas Thomson et al. [ 9 ] also suggest the possibility of returning to the same data collection site with the same or different participants. When a QLR study involves data collection in shorter engagements, such as serial interviews, these engagements are often referred to as data collection time points. Data collection in time waves relates to longer engagements, such as field work/observation periods. There is no clear-cut definition for the minimum time span of a QLR study; instead, the length of the data collection period must be decided based upon what processes or changes are the focus of the study [ 13 ].

Most literature describing QLR methods originates from the social sciences, where the approach has a long tradition [ 1 , 10 , 14 ]. In health research, one-time-data collection studies have been the norm within qualitative methods [ 15 ], although health research using QLR methods has increased in recent years [ 2 , 5 , 16 , 17 ]. However, collecting and managing longitudinal data has its own sets of challenges, especially regarding how to integrate perspectives of time and/or change in the data collection and subsequent analysis [ 1 ]. Therefore, a study of QLR articles from the health research literature can provide an insightful understanding of the use, trends and variations of how methods are used and how elements of time/change are integrated in QLR studies. This could, in turn, provide inspiration for using different possibilities of collecting data across time when using QLR in health research. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

More specifically, the research questions were:

What methodological approaches are described to inform QLR research?

What methodological references are used to inform QLR research?

How are longitudinal perspectives articulated in article aims?

How is longitudinal data collection conducted?

In this method study, we used an adapted scoping review method [ 18 , 19 , 20 ]. Method studies are research conducted on research studies to investigate how research design elements are applied across a field [ 21 ]. However, since there are no clear guidelines for method studies, they often use adapted versions of systematic reviews or scoping review methods [ 21 ]. The adaptations of the scoping review method consisted of 1) using a large subsample of studies (publications from a three-year period) instead of including all QLR articles published, and 2) not including grey literature. The reporting of this study was guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist [ 20 , 22 ] (see Additional file 1 ). A (unpublished) protocol was developed by the research team during the spring of 2019.

Eligibility criteria

In line with method study recommendations [ 21 ], we decided to draw on a manageable subsample of published QLR research. Articles that were eligible for inclusion were health research primary studies written in English, published between 2017 and 2019, and with a longitudinal qualitative data collection. Our operating definition for qualitative longitudinal data collection was data collected at different time points (e.g., repeated interviews) or time waves (e.g., periods of field work) involving the same sample or conducted in the same setting(s). We intentionally selected a broad inclusion criterion for QLR since we wanted a wide variety of articles. The selected time period was chosen because the first QLR method article directed towards health research was published in 2013 [ 1 ] and during the following years the methodological resources for QLR increased [ 3 , 8 , 17 , 23 , 24 , 25 ], thus we could expect that researchers publishing QLR in 2017–2019 should be well-grounded in QLR methods. Further, we found that from 2012 to 2019 the rate of published QLR articles were steady at around 100 publications per year, so including those from a three-year period would give a sufficient number of articles (~ 300 articles) for providing an overview of the field. Published conference abstracts, protocols, articles describing methodological issues, review articles, and non-research articles (e.g., editorials) were excluded.

Search strategy

Relevant articles were identified through systematic searches in EBSCOhost, including biomedical and life science research and nursing and allied health literature. A librarian who specialized in systematic review searches developed and performed the searches, in collaboration with the author team (LF, TW & ÅA). In the search, the term “longitudinal” was combined with terms for qualitative research (for the search strategy see Additional file 2 ). The searches were conducted in the autumn of 2019 (last search 2019-09-10).

Study selection

All identified citations were imported into EndNote X9 ( www.endnote.com ) and further imported into Rayyan QCRI online software [ 26 ], and duplicates were removed. All titles and abstracts were screened against the eligibility criteria by two independent reviewers (ÅA & EH), and conflicting decisions were discussed until resolved. After discussions by the team, we decided to include articles published between 2017 and 2019, that selection alone included 350 records with diverse methods and designs. The full texts of articles that were eligible for inclusion were retrieved. In the next stage, two independent reviewers reviewed each full text article to make final decisions regarding inclusion (ÅA, EH, Julia Andersson). In total, disagreements occurred in 8% of the decisions, and were resolved through discussion. Critical appraisal was not assessed since the study aimed to describe the range of how QLR is applied and not aggregate research findings [ 21 , 22 ].

Data charting and analysis

A standardized charting form was developed in Excel (Excel 2016). The charting form was reviewed by the research team and pretested in two stages. The tests were performed to increase internal consistency and reduce the risk of bias. First, four articles were reviewed by all the reviewers, and modifications were made to the form and charting instructions. In the next stage, all reviewers used the charting form on four other articles, and the convergence in ratings was 88%. Since the convergence was under 90%, charting was performed in duplicate to reduce errors in the data. At the end of the charting process, the convergence among the reviewers was 95%. The charting was examined by the first author, who revised the charting in cases of differences.

Data items that were charted included 1) the article characteristics (e.g., authors, publication year, journal, country), 2) the aim and scope (e.g., phenomenon of interest, population, contexts), 3) the stated methodology and analysis method, 4) text describing the data collection (e.g., type of data material, number of participants, time frame of data collection, total amount of data material), and 5) the qualitative methodological references used in the methods section. Extracted text describing data collection could consist of a few sentences or several sections from the articles (and sometimes figures) concerning data collection practices, rational for time periods and research engagement in the field. This was later used to analyze how the longitudinal data collection was conducted and elements of longitudinal design. To categorize the qualitative methodology approaches, a framework from Cresswell [ 27 ] was used (including the categories for grounded theory, phenomenology, ethnography, case study and narrative research). Overall, data items needed to be explicitly stated in the articles in order to be charted. For example, an article was categorized as grounded theory if it explicitly stated “in this grounded theory study” but not if it referred to the literature by Glaser and Strauss without situating itself as a grounded theory study (See Additional file 3 for the full instructions for charting).

All charting forms were compiled into a single Microsoft Excel spreadsheet (see Supplementary files for an overview of the articles). Descriptive statistics with frequencies and percentages were calculated to summarize the data. Furthermore, an iterative coding process was used to group the articles and investigate patterns of, for example, research topics, words in the aims, or data collection practices. Alternative ways of grouping and presenting the data were discussed by the research team.

Search and selection

A total of 2179 titles and abstracts were screened against the eligibility criteria (see Fig.  1 ). The full text of one article could not be found and the article was excluded [ 28 ]. Fifty full text articles were excluded. Finally, 299 articles, representing 271 individual studies, were included in this study (see additional files 4 and 5 respectively for tables of excluded and included articles).

figure 1

PRISMA diagram of study selection]

General characteristics and research areas of the included articles

The articles were published in many journals ( n  = 193), and 138 of these journals were represented with one article each. BMJ Open was the most prevalent journal ( n  = 11), followed by the Journal of Clinical Nursing ( n  = 8). Similarly, the articles represented many countries ( n  = 41) and all the continents; however, a large part of the studies originated from the US or UK ( n  = 71, 23.7% and n  = 70, 23.4%, respectively). The articles focused on the following types of populations: patients, families−/caregivers, health care providers, students, community members, or policy makers. Approximately 20% ( n  = 63, 21.1%) of the articles collected data from two or more of these types of population(s) (see Table  1 ).

Approximately half of the articles ( n  = 158, 52.8%) articulated being part of a larger research project. Of them, 95 described a project with both quantitative and qualitative methods. They represented either 1) a qualitative study embedded in an intervention, evaluation or implementation study ( n  = 66, 22.1%), 2) a longitudinal cohort study collecting both quantitative and qualitative material ( n  = 23, 7.7%), or 3) qualitative longitudinal material collected together with a cross sectional survey (n = 6, 2.0%). Forty-eight articles (16.1%) described belonging to a larger qualitative project presented in several research articles.

Methodological traditions

Approximately one-third ( n  = 109, 36.5%) of the included articles self-identified with one of the qualitative traditions recognized by Cresswell [ 27 ] (case study: n  = 36, 12.0%; phenomenology: n  = 35, 11.7%; grounded theory: n  = 22, 7.4%; ethnography: n  = 13, 4.3%; narrative method: n = 3, 1.0%). In nine articles, the authors described using a mix of two or more of these qualitative traditions. In addition, 19 articles (6.4%) self-identified as mixed methods research.

Every second article self-identified as having a qualitative longitudinal design ( n  = 156, 52.2%); either they self-identified as “a longitudinal qualitative study” or “using a longitudinal qualitative research design”. However, in some articles, this was stated in the title and/or abstract and nowhere else in the article. Fifty-two articles (17.4%) self-identified both as having a QLR design and following one of the methodological approaches (case study: n  = 8; phenomenology: n  = 23; grounded theory: n  = 9; ethnography: n  = 6; narrative method: n  = 2; mixed methods: n  = 4).

The other 143 articles used various terms to situate themselves in relation to a longitudinal design. Twenty-seven articles described themselves as a longitudinal study (9.0%) or a longitudinal study within a specific qualitative tradition (e.g., a longitudinal grounded theory study or a longitudinal mixed method study) ( n  = 64, 21.4%). Furthermore, 36 articles (12.0%) referred to using longitudinal data materials (e.g., longitudinal data or longitudinal interviews). Nine of the articles (3.0%) used the term longitudinal in relation to the data analysis or aim (e.g., the aim was to longitudinally describe), used terms such as serial or repeated in relation to the data collection design ( n  = 2, 0.7%), or did not use any term to address the longitudinal nature of their design ( n  = 5, 1.7%).

Use of methodological references

The mean number of qualitative method references in the methods sections was 3.7 (range 0 to 16), and 20 articles did not have any qualitative method reference in their methods sections. Footnote 1 Commonly used method references were generic books on qualitative methods, seminal works within qualitative traditions, and references specializing in qualitative analysis methods (see Table  2 ). It should be noted that some references were comprehensive books and thus could include sections about QLR without being focused on the QLR method. For example, Miles et al. [ 31 ] is all about analysis and coding and includes a chapter regarding analyzing change.

Only approximately 20% ( n  = 58) of the articles referred to the QLR method literature in their methods sections. Footnote 2 The mean number of QLR method references (counted for articles using such sources) was 1.7 (range 1 to 6). Most articles using the QLR method literature also used other qualitative methods literature (except two articles using one QLR literature reference each [ 39 , 40 ]). In total, 37 QLR method references were used, and 24 of the QLR method references were only referred to by one article each.

Longitudinal perspectives in article aims

In total, 231 (77.3%) articles had one or several terms related to time or change in their aims, whereas 68 articles (22.7%) had none. Over one hundred different words related to time or change were identified. Longitudinally oriented terms could focus on changes across time (process, trajectory, transition, pathway or journey), patterns of how something changed (maintenance, continuity, stability, shifts), or phenomena that by nature included change (learning or implementation). Other types of terms emphasized the data collection time period (e.g., over 6 months) or a specific changing situation (e.g., during pregnancy, through the intervention period, or moving into a nursing home). The most common terms used for the longitudinal perspective were change ( n  = 63), over time ( n  = 52), process ( n  = 36), transition ( n  = 24), implementation ( n  = 14), development ( n  = 13), and longitudinal (n = 13). Footnote 3

Furthermore, the articles varied in what ways their aims focused on time/change, e.g., the longitudinal perspectives in the aims (see Table  3 ). In 71 articles, the change across time was the phenomenon of interest of the article : for example, articles investigating the process of learning or trajectories of diseases. In contrast, 46 articles investigated change or factors impacting change in relation to a defined outcome : for example, articles investigating factors influencing participants continuing in a physical activity trial. The longitudinal perspective could also be embedded in an article’s context . In such cases, the focus of the article was on experiences that happened during a certain time frame or in a time-related context (e.g., described experiences of the patient-provider relationship during 6 months of rehabilitation).

Types of data and length of data collection

The QLR articles were often large and complex in their data collection methods. The median number of participants was 20 (range from one to 1366, the latter being an article with open-ended questions in questionnaires [ 46 ]). Most articles used individual interviews as the data material ( n  = 167, 55.9%) or a combination of data materials ( n  = 98, 32.8%) (e.g., interviews and observations, individual interviews and focus group interviews, or interviews and questionnaires). Forty-five articles (15.1%) presented quantitative and qualitative results. The median number of interviews was 46 (range three to 507), which is large in comparison to many qualitative studies. The observation materials were also comprehensive and could include several hundred hours of observations. Documents were often used as complementary material and included official documents, newspaper articles, diaries, and/or patient records.

The articles’ time spans Footnote 4 for data collection varied between a few days and over 20 years, with 60% of the articles’ time spans being 1 year or shorter ( n  = 180) (see Fig.  2 ). The variation in time spans might be explained by the different kinds of phenomena that were investigated. For example, Jensen et al. [ 47 ] investigated hospital care delivery and followed each participant, with observations lasting between four and 14 days. Smithbattle [ 48 ] described the housing trajectories of teen mothers, and collected data in seven waves over 28 years.

figure 2

Number of articles in relation to the time span of data collection. The time span of data collection is given in months

Three components of longitudinal data collection

In the articles, the data collection was conducted in relation to three different longitudinal data collection components (see Table  4 ).

Entities followed across time

Four different types of entities were followed across time: 1) individuals, 2) individual cases or dyads, 3) groups, and 4) settings. Every second article ( n  = 170, 56.9%) followed individuals across time, thus following the same participants through the whole data collection period. In contrast, when individual cases were followed across time, the data collection was centered on the primary participants (e.g., people with progressive neurological conditions) who were followed over time, and secondary participants (e.g., family caregivers) might provide complementary data at several time points or only at one-time point. When settings were followed over time, the participating individuals were sometimes the same, and sometimes changed across the data collection period. Typical settings were hospital wards, hospitals, smaller communities or intervention trials. The type of collected data corresponded with what kind of entities were followed longitudinally. Individuals were often followed with serial interviews, whereas groups were commonly followed with focus group interviews complemented with individual interviews, observations and/or questionnaires. Overall, the lengths of data collection periods seemed to be chosen based upon expected changes in the chosen entities. For example, the articles following an intervention setting were structured around the intervention timeline, collecting data before, after and sometimes during the intervention.

Tempo of data collection

The data collection tempo differed among the articles (e.g., the frequency and mode of the data collection). Approximately half ( n  = 154, 51.5%) of the articles used serial time points, collecting data at several reoccurring but shorter sequences (e.g., through serial interviews or open-ended questions in questionnaires). When data were collected in time waves ( n  = 50, 16.7%), the periods of data collection were longer, usually including both interviews and observations; often, time waves included observations of a setting and/or interviews at the same location over several days or weeks.

When comparing the tempo with the type of entities, some patterns were detected (see Fig.  3 ). When individuals were followed, data were often collected at time points, mirroring the use of individual interviews and/or short observations. For research in settings, data were commonly collected in time waves (e.g., observation periods over a few weeks or months). In studies exploring settings across time, time waves were commonly used and combined several types of data, particularly from interviews and observations. Groups were the least common studied entity ( n  = 9, 3.0%), so the numbers should be interpreted with caution, but continuous data collection was used in five of the nine studies. The continuous data collection mode was, for example, collecting electronic diaries [ 62 ] or minutes from committee meetings during a time period [ 63 ].

figure 3

Tempo of data collection in relation to entities followed over time

Preplanned or adapted data collection

A large majority ( n  = 224, 74.9%) of the articles used preplanned data collection (e.g., in preplanned data collection, all participants were followed across time according to the same data collection plan). For example, all participants were interviewed one, six and twelve months’ post-diagnosis. In contrast to the preplanned data collection approach, 44 articles had a participant-adapted data collection (14.7%), and participants were followed at different frequencies and/or over various lengths of time depending on each participant’s situation. Participant-adapted data collection was more common among articles following individuals or individual cases (see Fig.  4 ). To adapt the data collection to the participants, the researchers created strategies to reach participants when crucial events were happening. Eleven articles used a participant entry approach to data collection ( n  = 11, 6.7%), and the whole or parts of the data were independently sent in by participants in the form of diaries, questionnaires, or blogs. Another approach to data collection was using theoretical or analysis-driven ideas to guide the data collection ( n  = 19, 6.4%). In these articles, the analysis and data collection were conducted simultaneously, and ideas arising in the analysis could be followed up, for example, returning to some participants, recruiting participants with specific experiences, or collecting complementary types of data materials. This approach was most common in the articles following settings across time, which often included observations and interviews with different types of populations. Articles using theoretical or analysis driven data collection were not associated with grounded theory to a greater extent than the other articles in the sample (e.g., did not self-identify as grounded theory or referred to methodological literature within grounded theory traditions to a greater proportion).

figure 4

Preplanned or adapted data collection in relation to entities followed over time

According to our results, some researchers used QLR as a methodological approach and other researchers used a longitudinal qualitative data collection without aiming to investigate change. Adding to the debate on whether QLR is a methodological approach in its own right or a design element in a particular study we suggest that the use of QLR can be described as layered (see Fig.  5 ). Namely, articles must fulfill several criteria in order to use QLR as a methodological approach, and that is done in some articles. In those articles QLR method references were used, the aim was to investigate change of a phenomenon and the longitudinal elements of the data collection were thoroughly integrated into the method section. On the other hand, some articles using a longitudinal qualitative data collection were just collecting data over time, without addressing time and/or change in the aim. These articles can still be interesting research studies with valuable results, but they are not using the full potential of QLR as a methodological approach. In all, around 40% of the articles had an aim that focused on describing or understanding change (either as phenomenon or outcome); but only about 24% of the articles set out to investigate change across time as their phenomenon of interest.

figure 5

The QLR onion. The use of QLR design can be described as layered, where researchers use more or less elements of a QLR design. The two inmost layers represents articles using QLR as a methodological approach

Regarding methodological influences, about one-third of the articles self-identify with any of the traditional qualitative methodologies. Using a longitudinal qualitative data collection as an element integrated with another methodological tradition can therefore be seen as one way of working with longitudinal qualitative materials. In our results, the articles referring to methodologies other than QLR preferably used case study, phenomenology and grounded theory methodologies. This was surprising since Neale [ 10 ] identified ethnography, case studies and narrative methods as the main methodological influences on QLR. Our findings might mirror the profound impacts that phenomenology and grounded theory have had on the qualitative field of health research. Regarding phenomenology, the findings can also be influenced by more recent discussions of combining interpretative phenomenological analysis with QLR [ 6 ].

Half of the articles self-identified as QLR studies, but QLR method references were used in less than 20% of the identified articles. This is both surprising and troublesome since use of appropriate method literature might have supported researchers who were struggling with for example a large quantity of materials and complex analysis. A possible explanation for the lack of use of QLR method literature is that QLR as a methodological approach is not well known, and authors might not be aware that method literature exists. It is quite understandable that researchers can describe a qualitative project with longitudinal data collection as a qualitative longitudinal study, without being aware that QLR is a specific form of study. Balmer [ 64 ] described how their group conducted serial interviews with medical students over several years before they became aware of QLR as a method of study. Within our networks, we have met researchers with similar experiences. Likewise, peer reviewers and editorial boards might not be accustomed to evaluating QLR manuscripts. In our results, 138 journals published one article between 2017 and 2019, and that might not be enough for editorial boards and peer reviewers to develop knowledge to enable them to closely evaluate manuscripts with a QLR method.

In 2007, Holland and colleagues [ 65 ] mapped QLR in the UK and described the following four categories of QLR: 1) mixed methods approaches with a QLR component; 2) planned prospective longitudinal studies; 3) follow-up studies complementing a previous data collection with follow-up; and 4) evaluation studies. Examples of all these categories can be found among the articles in this method study; however, our results do paint a more complex picture. According to our results, Holland’s categories are not multi-exclusive. For example, studies with intentions to evaluate or implement practices often used a mixed methods design and were therefore eligible for both categories one and four described above. Additionally, regarding the follow-up studies, it was seldom clearly described if they were planned as a two-time-point study or if researchers had gained an opportunity to follow up on previous data collection. When we tried to categorize QLR articles according to the data collection design, we could not identify multi-exclusive categories. Instead, we identified the following three components of longitudinal data collection: 1) entities followed across time; 2) tempo; and 3) preplanned or adapted data collection approaches. However, the most common combination was preplanned studies that followed individuals longitudinally with three or more time points.

The use of QLR differs between disciplines [ 14 ]. Our results show some patterns for QLR within health research. Firstly, the QLR projects were large and complex; they often included several types of populations and various data materials, and were presented in several articles. Secondly, most studies focused upon the individual perspective, following individuals across time, and using individual interviews. Thirdly, the data collection periods varied, but 53% of the articles had a data collection period of 1 year or shorter. Finally, patients were the most prevalent population, even though topics varied greatly. Previously, two other reviews that focused on QLR in different parts of health research (e.g., nursing [ 4 ] and gerontology [ 66 ]) pointed in the same direction. For example, individual interviews or a combination of data materials were commonly used, and most studies were shorter than 1 year but a wide range existed [ 4 , 66 ].

Considerations when planning a QLR project

Based on our results, we argue that when health researchers plan a QLR study, they should reflect upon their perspective of time/change and decide what part change should play in their QLR study. If researchers decide that change should play the main role in their project, then they should aim to focus on change as the phenomenon of interest. However, in some research, change might be an important part of the plot, without having the main role, and change in relation to the outcomes might be a better perspective. In such studies, participants with change, no change or different kinds of change are compared to explore possible explanations for the change. In our results, change in relation to the outcomes was often used in relation to intervention studies where participants who reached a desired outcome were compared to individuals who did not. Furthermore, for some research studies, change is part of the context in which the research takes place. This can be the case when certain experiences happen during a period of change; for example, when the aim is to explore the experience of everyday life during rehabilitation after stroke. In such cases a longitudinal data collection could be advisable (e.g., repeated interviews often give a deep relationship between interviewer and participants as well as the possibility of gaining greater depth in interview answers during follow-up interviews [ 15 ]), but the study might not be called a QLR study since it does not focus upon change [ 13 ]. We suggest that researchers make informed decisions of what kind of longitudinal perspective they set out to investigate and are transparent with their sources of methodological inspiration.

We would argue that length of data collection period, type of entities, and data materials should be in accordance with the type of change/changing processes that a study focuses on. Individual change is important in health research, but researchers should also remember the possibility of investigating changes in families, working groups, organizations and wider communities. Using these types of entities were less common in our material and could probably grant new perspectives to many research topics within health. Similarly, using several types of data materials can complement the insights that individual interviews can give. A large majority of the articles in our results had a preplanned data collection. Participant-adapted data collection can be a way to work in alignment with a “time-as-fluid” conceptualization of time because the events of subjective importance to participants can be more in focus and participants (or other entities) change processes can differ substantially across cases. In studies with lengthy and spaced-out data collection periods and/or uncertainty in trajectories, researchers should consider participant-adapted or participant entry data collection. For example, some participants can be followed for longer periods and/or with more frequency.

Finally, researchers should consider how to best publish and disseminate their results. Many QLR projects are large, and the results are divided across several articles when they are published. In our results, 21 papers self-identified as a mixed methods project or as part of a larger mixed methods project, but most of these did not include quantitative data in the article. This raises the question of how to best divide a large research project into suitable pieces for publication. It is an evident risk that the more interesting aspects of a mixed methods project are lost when the qualitative and quantitative parts are analyzed and published separately. Similar risks occur, for example, when data have been collected from several types of populations but are then presented per population type (e.g., one article with patient data and another with caregiver data). During the work with our study, we also came across studies where data were collected longitudinally, but the results were divided into publications per time point. We do not argue that these examples are always wrong, there are situations when these practices are appropriate. However, it often appears that data have been divided without much consideration. Instead, we suggest a thematic approach to dividing projects into publications, crafting the individual publications around certain ideas or themes and thus using the data that is most suitable for the particular research question. Combining several types of data and/or several populations in an analysis across time is in fact what makes QLR an interesting approach.

Strengths and limitations

This method study intended to paint a broad picture regarding how longitudinal qualitative methods are used within the health research field by investigating 299 published articles. Method research is an emerging field, currently with limited methodological guidelines [ 21 ], therefore we used scoping review method to support this study. In accordance with scoping review method we did not use quality assessment as a criterion for inclusion [ 18 , 19 , 20 ]. This can be seen as a limitation because we made conclusions based upon a set of articles with varying quality. However, we believe that learning can be achieved by looking at both good and bad examples, and innovation may appear when looking beyond established knowledge, or assessing methods from different angles. It should also be noted that the results given in percentages hold no value for what procedures that are better or more in accordance with QLR, the percentages simply state how common a particular procedure was among the articles.

As described, the included articles showed much variation in the method descriptions. As the basis for our results, we have only charted explicitly written text from the articles, which might have led to an underestimation of some results. The researchers might have had a clearer rationale than described in the reports. Issues, such as word restrictions or the journal’s scope, could also have influenced the amount of detail that was provided. Similarly, when charting how articles drew on a traditional methodology, only data from the articles that clearly stated the methodologies they used (e.g., phenomenology) were charted. In some articles, literature choices or particular research strategies could implicitly indicate that the researchers had been inspired by certain methodologies (e.g., referring to grounded theory literature and describing the use of simultaneous data collection and analysis could indicate that the researchers were influenced by grounded theory), but these were not charted as using a particular methodological tradition. We used the articles’ aims and objectives/research questions to investigate their longitudinal perspectives. However, as researchers have different writing styles, information regarding the longitudinal perspectives could have been described in surrounding text rather than in the aim, which might have led to an underestimation of the longitudinal perspectives.

The experience and diversity of the research team in our study was a strength. The nine authors on the team represent ten universities and three countries, and have extensive experience in different types of qualitative research, QLR and review methods. The different level of experiences with QLR within the team (some authors have worked with QLR in several projects and others have qualitative experience but no experience in QLR) resulted in interesting discussions that helped drive the project forward. These experiences have been useful for understanding the field.

Based on a method study of 299 articles, we can conclude that QLR in health research articles published between 2017 and 2019 often contain comprehensive complex studies with a large variation in topics. Some research was thoroughly designed to capture time/change throughout the methodology, focus and data collection, while other articles included a few elements of QLR. Longitudinal data collection included several components, such as what entities were followed across time, the tempo of data collection, and to what extent the data collection was preplanned or adapted across time. In sum, health researchers need to be considerate and make informed choices when designing QLR projects. Further research should delve deeper into what kind of research questions go well with QLR and investigate the best practice examples of presenting QLR findings.

Availability of data and materials

The datasets used and analyzed in this current study are available in supplementary file  6 .

Qualitative method references were defined as a journal article or book with a title that indicated an aim to guide researchers in qualitative research methods and/or research theories. Primary studies, theoretical works related to the articles’ research topics, protocols, and quantitative method literature were excluded. References written in a language other than English was also excluded since the authors could not evaluate their content.

QLR method references were defined as a journal article or book that 1) focused on qualitative methodological questions, 2) used terms such as ‘longitudinal’ or ‘time’ in the title so it was evident that the focus was on longitudinal qualitative research. Referring to another original QLR study was not counted as using QLR method literature.

Words were charted depending on their word stem, e.g., change, changes and changing were all charted as change.

It should be noted that here time span refers to the data collection related to each participant or case. Researchers could collect data for 2 years but follow each participant for 6 months.

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The authors wish to acknowledge Ellen Sejersted, librarian at the University of Agder, Kristiansand, Norway, who conducted the literature searches and Julia Andersson, research assistant at the Department of Nursing, Umeå University, Sweden, who supported the data management and took part in the initial screening phases of the project.

Open access funding provided by Umea University. This project was conducted within the authors’ positions and did not receive any specific funding.

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ÅA conceived the study. ÅA, EH, TW, LF, MKP, HA, and MSL designed the study. ÅA, TW, and LF were involved in literature searches together with the librarian. ÅA and EH performed the screening of the articles. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) took part in the data charting. ÅA performed the data analysis and discussed the preliminary results with the rest of the team. ÅA wrote the 1st manuscript draft, and ÅK, MSL and EH edited. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) contributed to editing the 2nd draft. MSL and LF provided overall supervision. All authors read and approved the final manuscript.

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All authors represent the nursing discipline, but their research topics differ. ÅA and ÅK have previously worked together with QLR method development. ÅA, EH, TW, LF, MKP, HA, KLD and MSL work together in the Nordic research group PRANSIT, focusing on nursing topics connected to transition theory using a systematic review method, preferably meta synthesis. All authors have extensive experience with qualitative research but various experience with QLR.

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Correspondence to Åsa Audulv .

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Audulv, Å., Hall, E.O.C., Kneck, Å. et al. Qualitative longitudinal research in health research: a method study. BMC Med Res Methodol 22 , 255 (2022). https://doi.org/10.1186/s12874-022-01732-4

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  • Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

  • The type of knowledge you aim to produce
  • The type of data you will collect and analyze
  • The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

Type of research What’s the difference? What to consider
Basic vs. applied Basic research aims to , while applied research aims to . Do you want to expand scientific understanding or solve a practical problem?
vs. Exploratory research aims to , while explanatory research aims to . How much is already known about your research problem? Are you conducting initial research on a newly-identified issue, or seeking precise conclusions about an established issue?
aims to , while aims to . Is there already some theory on your research problem that you can use to develop , or do you want to propose new theories based on your findings?

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Type of research What’s the difference? What to consider
Primary research vs secondary research Primary data is (e.g., through or ), while secondary data (e.g., in government or scientific publications). How much data is already available on your topic? Do you want to collect original data or analyze existing data (e.g., through a )?
, while . Is your research more concerned with measuring something or interpreting something? You can also create a research design that has elements of both.
vs Descriptive research gathers data , while experimental research . Do you want to identify characteristics, patterns and or test causal relationships between ?

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Type of research What’s the difference? What to consider
allows you to , while allows you to draw conclusions . Do you want to produce  knowledge that applies to many contexts or detailed knowledge about a specific context (e.g. in a )?
vs Cross-sectional studies , while longitudinal studies . Is your research question focused on understanding the current situation or tracking changes over time?
Field research vs laboratory research Field research takes place in , while laboratory research takes place in . Do you want to find out how something occurs in the real world or draw firm conclusions about cause and effect? Laboratory experiments have higher but lower .
Fixed design vs flexible design In a fixed research design the subjects, timescale and location are begins, while in a flexible design these aspects may . Do you want to test hypotheses and establish generalizable facts, or explore concepts and develop understanding? For measuring, testing and making generalizations, a fixed research design has higher .

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

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McCombes, S. (2023, June 22). Types of Research Designs Compared | Guide & Examples. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/methodology/types-of-research/

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Descriptive Research Design – Types, Methods and Examples

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Descriptive Research Design

Descriptive Research Design


Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).


This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.


This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

  • Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
  • Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
  • Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
  • Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
  • Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
  • Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

  • A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
  • A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
  • An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
  • A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
  • An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

  • Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
  • Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
  • Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
  • Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
  • Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
  • I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
  • Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

  • Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
  • Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
  • Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
  • Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
  • Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
  • Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

  • To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
  • To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
  • To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
  • To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

  • Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
  • Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
  • Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
  • Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
  • Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
  • Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

  • Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
  • Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
  • Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
  • Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
  • Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
  • Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

  • Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
  • Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
  • Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
  • Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
  • Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
  • Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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Research Design


A descriptive study is one in which information is collected without changing the environment (i.e., nothing is manipulated). Sometimes these are referred to as “ correlational ” or “ observational ” studies. The Office of Human Research Protections (OHRP) defines a descriptive study as “Any study that is not truly experimental.” In human research, a descriptive study can provide information about the naturally occurring health status, behavior, attitudes or other characteristics of a particular group. Descriptive studies are also conducted to demonstrate or relationships between things in the world around you.

Descriptive studies can involve a one-time interaction with groups of people ( ) or a study might follow individuals over time ( ). Descriptive studies, in which the researcher interacts with the participant, may involve surveys or interviews to collect the necessary information. Descriptive studies in which the researcher does not interact with the participant include observational studies of people in an environment and studies involving data collection using existing records (e.g., medical record review).

Descriptive studies are usually the best methods for collecting information that will demonstrate relationships and describe the world as it exists. These types of studies are often done before an experiment to know what specific things to manipulate and include in an experiment. Bickman and Rog (1998) suggest that descriptive studies can answer questions such as “what is” or “what was.” Experiments can typically answer “why” or “how.”




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  • Published: 01 July 2024

Time of sample collection is critical for the replicability of microbiome analyses

  • Celeste Allaband   ORCID: orcid.org/0000-0003-1832-4858 1 , 2 , 3 ,
  • Amulya Lingaraju 2 ,
  • Stephany Flores Ramos   ORCID: orcid.org/0000-0002-1918-9769 1 , 2 , 3 ,
  • Tanya Kumar 4 ,
  • Haniyeh Javaheri 2 ,
  • Maria D. Tiu 2 ,
  • Ana Carolina Dantas Machado 2 ,
  • R. Alexander Richter 2 ,
  • Emmanuel Elijah 5 , 6 ,
  • Gabriel G. Haddad 3 , 7 , 8 ,
  • Vanessa A. Leone 9 ,
  • Pieter C. Dorrestein   ORCID: orcid.org/0000-0002-3003-1030 3 , 5 , 6 , 10 ,
  • Rob Knight   ORCID: orcid.org/0000-0002-0975-9019 3 , 6 , 11 , 12 , 13 &
  • Amir Zarrinpar   ORCID: orcid.org/0000-0001-6423-5982 2 , 6 , 13 , 14 , 15  

Nature Metabolism ( 2024 ) Cite this article

Metrics details

  • Animal disease models
  • Circadian regulation
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As the microbiome field moves from descriptive and associative research to mechanistic and interventional studies, being able to account for all confounding variables in the experimental design, which includes the maternal effect 1 , cage effect 2 , facility differences 3 , as well as laboratory and sample handling protocols 4 , is critical for interpretability of results. Despite significant procedural and bioinformatic improvements, unexplained variability and lack of replicability still occur. One underexplored factor is that the microbiome is dynamic and exhibits diurnal oscillations that can change microbiome composition 5 , 6 , 7 . In this retrospective analysis of 16S amplicon sequencing studies in male mice, we show that sample collection time affects the conclusions drawn from microbiome studies and its effect size is larger than those of a daily experimental intervention or dietary changes. The timing of divergence of the microbiome composition between experimental and control groups is unique to each experiment. Sample collection times as short as only 4 hours apart can lead to vastly different conclusions. Lack of consistency in the time of sample collection may explain poor cross-study replicability in microbiome research. The impact of diurnal rhythms on the outcomes and study design of other fields is unknown but likely significant.

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Data availability

Literature review data are at https://github.com/knightlab-analyses/dynamics/data/ . Figure 1 , mock data are at https://github.com/knightlab-analyses/dynamics/data/MockData . Figure 2 (Allaband/Zarrinpar 2021) data are under EBI accession ERP110592 . Figure 3 data (longitudinal IHC) are under EBI accession ERP110592 and (longitudinal circadian TRF) EBI accession ERP123226 . Figure 4 data (Zarrinpar/Panda 2014) are in the Supplementary Excel file attached to the source paper 13 ; (Leone/Chang 2015) figshare for the 16S amplicon sequence data are at https://doi.org/10.6084/m9.figshare.882928 (ref. 63 ). Extended Data Fig. 2 data (Caporaso/Knight 2011) are at MG-RAST project mgp93 (IDs mgm4457768.3 and mgm4459735.3). Extended Data Fig. 3 data (Wu/Chen 2018) are under ENA accession PRJEB22049 . Extended Data Fig. 4 data (Tuganbaev/Elinav 2021) are under ENA accession PRJEB38869 .

Code availability

All relevant code notebooks are on GitHub at https://github.com/knightlab-analyses/dynamics/notebooks .

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C.A. was supported by NIH T32 OD017863. S.F.R. is supported by the Soros Foundation. A.L. is supported by the AHA Postdoctoral Fellowship grant. T.K. is supported by NIH T32 GM719876. A.C.D.M. is supported by R01 HL148801-02S1. G.G.H. and A.Z. are supported by NIH R01 HL157445. A.Z. is further supported by the VA Merit BLR&D Award I01 BX005707 and NIH grants R01 AI163483, R01 HL148801, R01 EB030134 and U01 CA265719. All authors receive institutional support from NIH P30 DK120515, P30 DK063491, P30 CA014195, P50 AA011999 and UL1 TR001442.

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Division of Biomedical Sciences, University of California, San Diego, La Jolla, CA, USA

Celeste Allaband & Stephany Flores Ramos

Division of Gastroenterology, University of California, San Diego, La Jolla, CA, USA

Celeste Allaband, Amulya Lingaraju, Stephany Flores Ramos, Haniyeh Javaheri, Maria D. Tiu, Ana Carolina Dantas Machado, R. Alexander Richter & Amir Zarrinpar

Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

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C.A. and A.Z. conceptualized the work. C.A., E.E., P.C.D., R.K. and A.Z. determined the methodology. C.A., A.L., S.F.R., T.K., H.J., M.D.T., A.C.D.M. and R.A.R. were involved in data investigation. C.A., S.F.R., T.K., H.J., M.D.T., A.C.D.M. and R.A.R. created visualizations. A.Z. acquired funding and was the project administrator. R.K. and A.Z. supervised the work. G.G.H. and V.A.L. provided resources. C.A., A.L., S.F.R., T.K., H.J., M.D.T. and A.Z. wrote the first draft. All authors contributed to the review and editing of the manuscript.

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Correspondence to Amir Zarrinpar .

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A.Z. is a co-founder and a chief medical officer, and holds equity in Endure Biotherapeutics. P.C.D. is an advisor to Cybele and co-founder and advisor to Ometa and Enveda with previous approval from the University of California, San Diego. All other authors declare no competing interests.

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Nature Metabolism thanks Robin Voigt-Zuwala, Jacqueline M. Kimmey, John R. Kirby and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

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Extended data

Extended data fig. 1 microbiome literature review..

A ) 2019 Literature Review Summary. Of the 586 articles containing microbiome (16 S or metagenomic) data, found as described in the methods section, the percentage of microbiome articles from each of the publication groups. B ) The percentage of microbiome articles belonging to each individual journal in 2019. Because the numerous individual journals from Science represented low percentages individually, they were grouped together. C ) The percentage articles where collection time was explicitly stated (yes: 8 AM, ZT4, etc.), implicitly stated (relative: ‘before surgery’, ‘in the morning’, etc.), or unstated (not provided: ‘daily’, ‘once a week’, etc.). D ) Meta-Analysis Inclusion Criteria Flow Chart. Literature review resulting in the five previously published datasets for meta-analysis 11 , 13 , 28 , 29 , 30 .

Extended Data Fig. 2 Single Time Point (Non-Circadian) Example.

A ) Weighted UniFrac PCoA Plot - modified example from Moving Pictures Qiime2 tutorial data [ https://docs.qiime2.org/2022.11/tutorials/moving-pictures/ ]. Each point is a sample. Points were coloured by body site of origin. There are 8 gut, 8 left palm, 9 right palm, and 9 tongue samples. B ) Within-Condition Distances (WCD) boxplot/stripplot for each body site (n = 8–9 mouse per group per time point). C ) Between Condition Distances (BCD) boxplot/stripplot for each unique body site comparison (n = 8–9 mouse per group per time point). D ) All pairwise grouping comparisons, both WCD and BCD, are shown in the boxplots/stripplots (n = 8–9 mouse per group per time point). Only WCD to BCD statistical differences are shown. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: ns (not significant) = p > 0.05, * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001.

Extended Data Fig. 3 Additional Analysis of Apoe-/- Mice Exposed to IHC Conditions.

A ) Weighted UniFrac PCoA stacked view (same as Fig. 2b but different orientation). Good for assessing overall similarity not broken down by time point. Significance determined by PERMANOVA (p = 0.005). B ) Weighted UniFrac PCoA of only axis 1 over time. C ) Boxplot/scatterplot of within-group weighted UniFrac distance values for the control group (Air, n = 3–4 samples per time point). Unique non-zero values in the matrix were kept. Dotted line indicates the mean of all values presented. No significant differences (p > 0.05) found. D ) Boxplot/scatterplot of within-group weighted UniFrac distance values for the experimental group (IHC, n = 3–4 samples per time point)). Unique non-zero values in the matrix were kept. Dotted line indicates the mean of all values presented. No significant differences (p > 0.05) found. E ) Boxplot/scatterplot of within-group weighted UniFrac distance values for both control (Air) and experimental (IHC) groups [n = 3–4 samples per group per time point]. Mann-Whitney-Wilcoxon test with Bonferroni correction used to determine significant differences between groups. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Notation: ns = not significant, p > 0.05; * = p < 0.05; ** = p < 0.01; *** = p < 0.001.

Extended Data Fig. 4 Irregular differences in diurnal rhythm patterns leads to generally minor shifts in BCD when comparing LD vs DD mice.

A ) Experimental design. Balb/c mice were fed NCD ad libitum under 0:24 L:D (24 hr darkness, DD) experimental conditions and compared to 12:12 L:D (LD) control conditions. After 2 weeks, mice from each group were euthanized every 4 hours for 24 hours (N = 4–5 mice/condition) and samples were collected from the proximal small intestine (‘jejunum’) and distal small intestine (‘ileum’) contents. B ) BCD for luminal contents of proximal small intestine samples comparing LD to DD mice (N = 4–5 mice/condition). Dotted line is the average of all shown weighted UniFrac distances. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction; notation: **** = p < 0.00001. C ) BCD for luminal contents of distal small intestine samples comparing LD to DD mice (N = 4–5 mice/condition). Dotted line is the average of all shown weighted UniFrac distances. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values.

Extended Data Fig. 5 Localized changes in BCD between luminal and mucosal contents.

A ) Experimental design and sample collection for a local site study. Small intestinal samples were collected every 4 hours for 24 hours (N = 4–5 mice/condition, skipping ZT8). Mice were fed ad libitum on the same diet (NCD) for 4 weeks before samples were taken. B ) BCD for luminal vs mucosal conditions (N = 4–5 mice/condition). The dotted line is the average of all shown weighted UniFrac distances. Significance is determined using the Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. C ) Heatmap of mean BCD distances comparing luminal and mucosal by time point (N = 4–5 mice/condition). Highest value highlighted in navy, lowest value highlighted in gold. Boxplot centre line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001. D ) Experimentally relevant log ratio, highlighting the changes seen at ZT20 (N = 4–5 mice/condition). Boxplot center line indicates median, edges of boxes are quartiles, error bars are min and max values. Significance was determined using a paired Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction. Notation: * = p < 0.05; ** = p < 0.01; *** = p < 0.001, **** = p < 0.00001.

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Allaband, C., Lingaraju, A., Flores Ramos, S. et al. Time of sample collection is critical for the replicability of microbiome analyses. Nat Metab (2024). https://doi.org/10.1038/s42255-024-01064-1

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DOI : https://doi.org/10.1038/s42255-024-01064-1

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