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• Longitudinal Study | Definition, Approaches & Examples

Longitudinal Study | Definition, Approaches & Examples

Published on May 8, 2020 by Lauren Thomas . Revised on June 22, 2023.

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, other interesting articles, 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 Boston men 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.

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 centers 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 analyzed on a regional level. You will also be restricted to whichever variables the original researchers decided to investigate.

If you choose to go this route, you should carefully examine the source of the dataset as well as what data is 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 are more prone to measurement error.

Like any other research design , longitudinal studies have their tradeoffs: 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.

Disadvantages

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.

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

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

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.

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 .

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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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Editor-in-Chief for Simply Psychology

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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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.

Flexibility

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.

Limitations

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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• Knowledge Base
• Methodology
• 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.

Prevent plagiarism, run a free check.

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.

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.

Disadvantages

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 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.

Cite this Scribbr article

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Thomas, L. (2022, October 24). Longitudinal Study | Definition, Approaches & Examples. Scribbr. Retrieved 21 August 2024, from https://www.scribbr.co.uk/research-methods/longitudinal-study-design/

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Longitudinal Study: Overview, Examples & Benefits

By Jim Frost Leave a Comment

What is a Longitudinal Study?

A longitudinal study is an experimental design that takes repeated measurements of the same subjects over time. These studies can span years or even decades. Unlike cross-sectional studies , which analyze data at a single point, longitudinal studies track changes and developments, producing a more dynamic assessment.

A cohort study is a specific type of longitudinal study focusing on a group of people sharing a common characteristic or experience within a defined period.

Imagine tracking a group of individuals over time. Researchers collect data regularly, analyzing how specific factors evolve or influence outcomes. This method offers a dynamic view of trends and changes.

Consider a study tracking 100 high school students’ academic performances annually for ten years. Researchers observe how various factors like teaching methods, family background, and personal habits impact their academic growth over time.

Researchers frequently use longitudinal studies in the following fields:

• Psychology: Understanding behavioral changes.
• Sociology: Observing societal trends.
• Medicine: Tracking disease progression.
• Education: Assessing long-term educational outcomes.

Learn more about Experimental Designs: Definition and Types .

Duration of Longitudinal Studies

Typically, the objectives dictate how long researchers run a longitudinal study. Studies focusing on rapid developmental phases, like early childhood, might last a few years. On the other hand, exploring long-term trends, like aging, can span decades. The key is to align the duration with the research goals.

Implementing a Longitudinal Study: Your Options

When planning a longitudinal study, you face a crucial decision: gather new data or use existing datasets.

Option 1: Utilizing Existing Data

Governments and research centers often share data from their longitudinal studies. For instance, the U.S. National Longitudinal Surveys (NLS) has been tracking thousands of Americans since 1979, offering a wealth of data accessible through the Bureau of Labor Statistics .

This type of data is usually reliable, offering insights over extended periods. However, it’s less flexible than the data that the researchers can collect themselves. Often, details are aggregated to protect privacy, limiting analysis to broader regions. Additionally, the original study’s variables restrict you, and you can’t tailor data collection to meet your study’s needs.

If you opt for existing data, scrutinize the dataset’s origin and the available information.

Option 2: Collecting Data Yourself

If you decide to gather your own data, your approach depends on the study type: retrospective or prospective.

A retrospective longitudinal study focuses on past events. This type is generally quicker and less costly but more prone to errors.

The prospective form of this study tracks a subject group over time, collecting data as events unfold. This approach allows the researchers to choose the variables they’ll measure and how they’ll measure them. Usually, these studies produce the best data but are more expensive.

While retrospective studies save time and money, prospective studies, though more resource-intensive, offer greater accuracy.

Learn more about Retrospective and Prospective Studies .

Advantages of a Longitudinal Study

Longitudinal studies can provide insight into developmental phases and long-term changes, which cross-sectional studies might miss.

These studies can help you determine the sequence of events. By taking multiple observations of the same individuals over time, you can attribute changes to the other variables rather than differences between subjects. This benefit of having the subjects be their own controls is one that applies to all within-subjects studies, also known as repeated measures design. Learn more about Repeated Measures Designs .

Consider a longitudinal study examining the influence of a consistent reading program on children’s literacy development. In a longitudinal framework, factors like innate linguistic ability, which typically don’t fluctuate significantly, are inherently accounted for by using the same group of students over time. This approach allows for a more precise assessment of the reading program’s direct impact over the study’s duration.

Collectively, these benefits help you establish causal relationships. Consequently, longitudinal studies excel in revealing how variables change over time and identifying potential causal relationships .

Disadvantages of a Longitudinal Study

A longitudinal study can be time-consuming and expensive, given its extended duration.

For example, a 30-year study on the aging process may require substantial funding for decades and a long-term commitment from researchers and staff.

Over time, participants may selectively drop out, potentially skewing results and reducing the study’s effectiveness.

For instance, in a study examining the long-term effects of a new fitness regimen, more physically fit participants might be less likely to drop out than those finding the regimen challenging. This scenario potentially skews the results to exaggerate the program’s effectiveness.

Maintaining consistent data collection methods and standards over a long period can be challenging.

For example, a longitudinal study that began using face-to-face interviews might face consistency issues if it later shifts to online surveys, potentially affecting the quality and comparability of the responses.

In conclusion, longitudinal studies are powerful tools for understanding changes over time. While they come with challenges, their ability to uncover trends and causal relationships makes them invaluable in many fields. As with any research method, understanding their strengths and limitations is critical to effectively utilizing their potential.

Newman AB. An overview of the design, implementation, and analyses of longitudinal studies on aging . J Am Geriatr Soc. 2010 Oct;58 Suppl 2:S287-91. doi: 10.1111/j.1532-5415.2010.02916.x. PMID: 21029055; PMCID: PMC3008590.

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What Is a Longitudinal Study?

Tracking Variables Over Time

Steve McAlister / The Image Bank / Getty Images

The Typical Longitudinal Study

Potential pitfalls, frequently asked questions.

A longitudinal study follows what happens to selected variables over an extended time. Psychologists use the longitudinal study design to explore possible relationships among variables in the same group of individuals over an extended period.

Once researchers have determined the study's scope, participants, and procedures, most longitudinal studies begin with baseline data collection. In the days, months, years, or even decades that follow, they continually gather more information so they can observe how variables change over time relative to the baseline.

For example, imagine that researchers are interested in the mental health benefits of exercise in middle age and how exercise affects cognitive health as people age. The researchers hypothesize that people who are more physically fit in their 40s and 50s will be less likely to experience cognitive declines in their 70s and 80s.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies, a type of correlational research , are usually observational, in contrast with cross-sectional research . Longitudinal research involves collecting data over an extended time, whereas cross-sectional research involves collecting data at a single point.

To test this hypothesis, the researchers recruit participants who are in their mid-40s to early 50s. They collect data related to current physical fitness, exercise habits, and performance on cognitive function tests. The researchers continue to track activity levels and test results for a certain number of years, look for trends in and relationships among the studied variables, and test the data against their hypothesis to form a conclusion.

Examples of Early Longitudinal Study Design

Examples of longitudinal studies extend back to the 17th century, when King Louis XIV periodically gathered information from his Canadian subjects, including their ages, marital statuses, occupations, and assets such as livestock and land. He used the data to spot trends over the years and understand his colonies' health and economic viability.

In the 18th century, Count Philibert Gueneau de Montbeillard conducted the first recorded longitudinal study when he measured his son every six months and published the information in "Histoire Naturelle."

The Genetic Studies of Genius (also known as the Terman Study of the Gifted), which began in 1921, is one of the first studies to follow participants from childhood into adulthood. Psychologist Lewis Terman's goal was to examine the similarities among gifted children and disprove the common assumption at the time that gifted children were "socially inept."

Types of Longitudinal Studies

Longitudinal studies fall into three main categories.

• Panel study : Sampling of a cross-section of individuals
• Cohort study : Sampling of a group based on a specific event, such as birth, geographic location, or experience
• Retrospective study : Review of historical information such as medical records

Benefits of Longitudinal Research

A longitudinal study can provide valuable insight that other studies can't. They're particularly useful when studying developmental and lifespan issues because they allow glimpses into changes and possible reasons for them.

For example, some longitudinal studies have explored differences and similarities among identical twins, some reared together and some apart. In these types of studies, researchers tracked participants from childhood into adulthood to see how environment influences personality , achievement, and other areas.

Because the participants share the same genetics , researchers chalked up any differences to environmental factors . Researchers can then look at what the participants have in common and where they differ to see which characteristics are more strongly influenced by either genetics or experience. Note that adoption agencies no longer separate twins, so such studies are unlikely today. Longitudinal studies on twins have shifted to those within the same household.

As with other types of psychology research, researchers must take into account some common challenges when considering, designing, and performing a longitudinal study.

Longitudinal studies require time and are often quite expensive. Because of this, these studies often have only a small group of subjects, which makes it difficult to apply the results to a larger population.

Selective Attrition

Participants sometimes drop out of a study for any number of reasons, like moving away from the area, illness, or simply losing motivation . This tendency, known as selective attrition , shrinks the sample size and decreases the amount of data collected.

If the final group no longer reflects the original representative sample , attrition can threaten the validity of the experiment. Validity refers to whether or not a test or experiment accurately measures what it claims to measure. If the final group of participants doesn't represent the larger group accurately, generalizing the study's conclusions is difficult.

The World’s Longest-Running Longitudinal Study

Lewis Terman aimed to investigate how highly intelligent children develop into adulthood with his "Genetic Studies of Genius." Results from this study were still being compiled into the 2000s. However, Terman was a proponent of eugenics and has been accused of letting his own sexism , racism , and economic prejudice influence his study and of drawing major conclusions from weak evidence. However, Terman's study remains influential in longitudinal studies. For example, a recent study found new information on the original Terman sample, which indicated that men who skipped a grade as children went on to have higher incomes than those who didn't.

A Word From Verywell

Longitudinal studies can provide a wealth of valuable information that would be difficult to gather any other way. Despite the typical expense and time involved, longitudinal studies from the past continue to influence and inspire researchers and students today.

A longitudinal study follows up with the same sample (i.e., group of people) over time, whereas a cross-sectional study examines one sample at a single point in time, like a snapshot.

A longitudinal study can occur over any length of time, from a few weeks to a few decades or even longer.

That depends on what researchers are investigating. A researcher can measure data on just one participant or thousands over time. The larger the sample size, of course, the more likely the study is to yield results that can be extrapolated.

Piccinin AM, Knight JE. History of longitudinal studies of psychological aging . Encyclopedia of Geropsychology. 2017:1103-1109. doi:10.1007/978-981-287-082-7_103

Terman L. Study of the gifted . In: The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. 2018. doi:10.4135/9781506326139.n691

Sahu M, Prasuna JG. Twin studies: A unique epidemiological tool .  Indian J Community Med . 2016;41(3):177-182. doi:10.4103/0970-0218.183593

Almqvist C, Lichtenstein P. Pediatric twin studies . In:  Twin Research for Everyone . Elsevier; 2022:431-438.

Warne RT. An evaluation (and vindication?) of Lewis Terman: What the father of gifted education can teach the 21st century . Gifted Child Q. 2018;63(1):3-21. doi:10.1177/0016986218799433

Warne RT, Liu JK. Income differences among grade skippers and non-grade skippers across genders in the Terman sample, 1936–1976 . Learning and Instruction. 2017;47:1-12. doi:10.1016/j.learninstruc.2016.10.004

Wang X, Cheng Z. Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest . 2020;158(1S):S65-S71. doi:10.1016/j.chest.2020.03.012

Caruana EJ, Roman M, Hernández-Sánchez J, Solli P. Longitudinal studies .  J Thorac Dis . 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

What is a longitudinal study?

Last updated

20 February 2023

Reviewed by

Longitudinal studies are common in epidemiology, economics, and medicine. People also use them in other medical and social sciences, such as to study customer trends. Researchers periodically observe and collect data from the variables without manipulating the study environment.

A company may conduct a tracking study, surveying a target audience to measure changes in attitudes and behaviors over time. The collected data doesn't change, and the time interval remains consistent. This longitudinal study can measure brand awareness, customer satisfaction , and consumer opinions and analyze the impact of an advertising campaign.

Analyze longitudinal studies

Dovetail streamlines longitudinal study data to help you uncover and share actionable insights

• Types of longitudinal studies

There are two types of longitudinal studies: Cohort and panel studies.

Panel study

A panel study is a type of longitudinal study that involves collecting data from a fixed number of variables at regular but distant intervals. Researchers follow a group or groups of people over time. Panel studies are designed for quantitative analysis but are also usable for qualitative analysis .

A panel study may research the causes of age-related changes and their effects. Researchers may measure the health markers of a group over time, such as their blood pressure, blood cholesterol, and mental acuity. Then, they can compare the scores to understand how age positively or negatively correlates with these measures.

Cohort study

A cohort longitudinal study involves gathering information from a group of people with something in common, such as a specific trait or experience of the same event. The researchers observe behaviors and other details of the group over time. Unlike panel studies, you can pick a different group to test in cohort studies.

An example of a cohort study could be a drug manufacturer studying the effects on a group of users taking a new drug over a period. A drinks company may want to research consumers with common characteristics, like regular purchasers of sugar-free sodas. This will help the company understand trends within its target market.

• Benefits of longitudinal research

If you want to study the relationship between variables and causal factors responsible for certain outcomes, you should adopt a longitudinal approach to your investigation.

The benefits of longitudinal research over other research methods include the following:

Insights over time

It gives insights into how and why certain things change over time.

Better information

Researchers can better establish sequences of events and identify trends.

No recall bias

The participants won't have recall bias if you use a prospective longitudinal study. Recall bias is an error that occurs in a study if respondents don't wholly or accurately recall the details of their actions, attitudes, or behaviors.

Because variables can change during the study, researchers can discover new relationships or data points worth further investigation.

Small groups

Longitudinal studies don't need a large group of participants.

• Potential pitfalls

The challenges and potential pitfalls of longitudinal studies include the following:

A longitudinal survey takes a long time, involves multiple data collections , and requires complex processes, making it more expensive than other research methods.

Unpredictability

Because they take a long time, longitudinal studies are unpredictable. Unexpected events can cause changes in the variables, making earlier data potentially less valuable.

Slow insights

Researchers can take a long time to uncover insights from the study as it involves multiple observations.

Participants can drop out of the study, limiting the data set and making it harder to draw valid conclusions from the results.

Overly specific data

If you study a smaller group to reduce research costs, results will be less generalizable to larger populations versus a study with a larger group.

Despite these potential pitfalls, you can still derive significant value from a well-designed longitudinal study by uncovering long-term patterns and relationships.

• Longitudinal study designs

Longitudinal studies can take three forms: Repeated cross-sectional, prospective, and retrospective.

Repeated cross-sectional studies

Repeated cross-sectional studies are a type of longitudinal study where participants change across sampling periods. For example, as part of a brand awareness survey , you ask different people from the same customer population about their brand preferences. 

Prospective studies

A prospective study is a longitudinal study that involves real-time data collection, and you follow the same participants over a period. Prospective longitudinal studies can be cohort, where participants have similar characteristics or experiences. They can also be panel studies, where you choose the population sample randomly.

Retrospective studies

Retrospective studies are longitudinal studies that involve collecting data on events that some participants have already experienced. Researchers examine historical information to identify patterns that led to an outcome they established at the start of the study. Retrospective studies are the most time and cost-efficient of the three.

• How to perform a longitudinal study

When developing a longitudinal study plan, you must decide whether to collect your data or use data from other sources. Each choice has its benefits and drawbacks.

Using data from other sources

You can freely access data from many previous longitudinal studies, especially studies conducted by governments and research institutes. For example, anyone can access data from the 1970 British Cohort Study on the  UK Data Service website .

Using data from other sources saves the time and money you would have spent gathering data. However, the data is more restrictive than the data you collect yourself. You are limited to the variables the original researcher was investigating, and they may have aggregated the data, obscuring some details.

If you can't find data or longitudinal research that applies to your study, the only option is to collect it yourself.

Collecting your own data

Collecting data enhances its relevance, integrity, reliability, and verifiability. Your data collection methods depend on the type of longitudinal study you want to perform. For example, a retrospective longitudinal study collects historical data, while a prospective longitudinal study collects real-time data.

The only way to ensure relevant and reliable data is to use an effective and versatile data collection tool. It can improve the speed and accuracy of the information you collect.

What is a longitudinal study in research?

A longitudinal study is a research design that involves studying the same variables over time by gathering data continuously or repeatedly at consistent intervals.

What is an example of a longitudinal study?

An excellent example of a longitudinal study is market research to identify market trends. The organization's researchers collect data on customers' likes and dislikes to assess market trends and conditions. An organization can also conduct longitudinal studies after launching a new product to understand customers' perceptions and how it is doing in the market.

Why is it called a longitudinal study?

It’s a longitudinal study because you collect data over an extended period. Longitudinal data tracks the same type of information on the same variables at multiple points in time. You collect the data over repeated observations.

What is a longitudinal study vs. a cross-sectional study?

A longitudinal study follows the same people over an extended period, while a cross-sectional study looks at the characteristics of different people or groups at a given time. Longitudinal studies provide insights over an extended period and can establish patterns among variables.

Cross-sectional studies provide insights about a point in time, so they cannot identify cause-and-effect relationships.

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What is a Longitudinal Study?: Definition and Explanation

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

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

Content Index

What is a longitudinal study?

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

• Longitudinal studies vs. cross-sectional studies

Types of surveys that use a longitudinal study

Longitudinal study examples.

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

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

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

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

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

Advantages of longitudinal studies

• Greater validation: For a long-term study to be successful, objectives and rules must be established from the beginning. As it is a long-term study, its authenticity is verified in advance, which makes the results have a high level of validity.
• Unique data: Most research studies collect short-term data to determine the cause and effect of what is being investigated. Longitudinal surveys follow the same principles but the data collection period is different. Long-term relationships cannot be discovered in a short-term investigation, but short-term relationships can be monitored in a long-term investigation.
• Allow identifying trends: Whether in medicine, psychology, or sociology, the long-term design of a longitudinal study enables trends and relationships to be found within the data collected in real time. The previous data can be applied to know future results and have great discoveries.
• Longitudinal surveys are flexible: Although a longitudinal study can be created to study a specific data point, the data collected can show unforeseen patterns or relationships that can be significant. Because this is a long-term study, the researchers have a flexibility that is not possible with other research formats.

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

Disadvantages of longitudinal studies

• Research time The main disadvantage of longitudinal surveys is that long-term research is more likely to give unpredictable results. For example, if the same person is not found to update the study, the research cannot be carried out. It may also take several years before the data begins to produce observable patterns or relationships that can be monitored.
• An unpredictability factor is always present It must be taken into account that the initial sample can be lost over time. Because longitudinal studies involve the same subjects over a long period of time, what happens to them outside of data collection times can influence the data that is collected in the future. Some people may decide to stop participating in the research. Others may not be in the correct demographics for research. If these factors are not included in the initial research design, they could affect the findings that are generated.
• Large samples are needed for the investigation to be meaningful To develop relationships or patterns, a large amount of data must be collected and extracted to generate results.
• Higher costs Without a doubt, the longitudinal survey is more complex and expensive. Being a long-term form of research, the costs of the study will span years or decades, compared to other forms of research that can be completed in a smaller fraction of the time.

Longitudinal studies vs. Cross-sectional studies

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

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

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

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

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

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

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

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

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

Example 1: Identical twins

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

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

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

Example 2: Violence and video games

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

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

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

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• What’s a Longitudinal Study? Types, Uses & Examples

Research can take anything from a few minutes to years or even decades to complete. When a systematic investigation goes on for an extended period, it’s most likely that the researcher is carrying out a longitudinal study of the sample population. So how does this work? 

In the most simple terms, a longitudinal study involves observing the interactions of the different variables in your research population, exposing them to various causal factors, and documenting the effects of this exposure. It’s an intelligent way to establish causal relationships within your sample population. 

In this article, we’ll show you several ways to adopt longitudinal studies for your systematic investigation and how to avoid common pitfalls. 

What is a Longitudinal Study? 

A longitudinal study is a correlational research method that helps discover the relationship between variables in a specific target population. It is pretty similar to a cross-sectional study , although in its case, the researcher observes the variables for a longer time, sometimes lasting many years. 

For example, let’s say you are researching social interactions among wild cats. You go ahead to recruit a set of newly-born lion cubs and study how they relate with each other as they grow. Periodically, you collect the same types of data from the group to track their development. 

The advantage of this extended observation is that the researcher can witness the sequence of events leading to the changes in the traits of both the target population and the different groups. It can identify the causal factors for these changes and their long-term impact. 

Characteristics of Longitudinal Studies

1. Non-interference: In longitudinal studies, the researcher doesn’t interfere with the participants’ day-to-day activities in any way. When it’s time to collect their responses , the researcher administers a survey with qualitative and quantitative questions . 

2. Observational: As we mentioned earlier, longitudinal studies involve observing the research participants throughout the study and recording any changes in traits that you notice. 

3. Timeline: A longitudinal study can span weeks, months, years, or even decades. This dramatically contrasts what is obtainable in cross-sectional studies that only last for a short time. 

Cross-Sectional vs. Longitudinal Studies 

• Definition 

A cross-sectional study is a type of observational study in which the researcher collects data from variables at a specific moment to establish a relationship among them. On the other hand, longitudinal research observes variables for an extended period and records all the changes in their relationship. 

Longitudinal studies take a longer time to complete. In some cases, the researchers can spend years documenting the changes among the variables plus their relationships. For cross-sectional studies, this isn’t the case. Instead, the researcher collects information in a relatively short time frame and makes relevant inferences from this data. 

While cross-sectional studies give you a snapshot of the situation in the research environment, longitudinal studies are better suited for contexts where you need to analyze a problem long-term. 

• Sample Data

Longitudinal studies repeatedly observe the same sample population, while cross-sectional studies are conducted with different research samples. 

Because longitudinal studies span over a more extended time, they typically cost more money than cross-sectional observations. 

Types of Longitudinal Studies 

The three main types of longitudinal studies are: 

• Panel Study
• Retrospective Study
• Cohort Study 

These methods help researchers to study variables and account for qualitative and quantitative data from the research sample. 

1. Panel Study 

In a panel study, the researcher uses data collection methods like surveys to gather information from a fixed number of variables at regular but distant intervals, often spinning into a few years. It’s primarily designed for quantitative research, although you can use this method for qualitative data analysis . 

When To Use Panel Study

If you want to have first-hand, factual information about the changes in a sample population, then you should opt for a panel study. For example, medical researchers rely on panel studies to identify the causes of age-related changes and their consequences. 

Advantages of Panel Study  

• It helps you identify the causal factors of changes in a research sample. 
• It also allows you to witness the impact of these changes on the properties of the variables and information needed at different points of their existing relationship. 
• Panel studies can be used to obtain historical data from the sample population. 

Disadvantages of Panel Studies

• Conducting a panel study is pretty expensive in terms of time and resources. 
• It might be challenging to gather the same quality of data from respondents at every interval. 

2. Retrospective Study

In a retrospective study, the researcher depends on existing information from previous systematic investigations to discover patterns leading to the study outcomes. In other words, a retrospective study looks backward. It examines exposures to suspected risk or protection factors concerning an outcome established at the start of the study.

When To Use Retrospective Study 

Retrospective studies are best for research contexts where you want to quickly estimate an exposure’s effect on an outcome. It also helps you to discover preliminary measures of association in your data. 

Medical researchers adopt retrospective study methods when they need to research rare conditions. 

Advantages of Retrospective Study

• Retrospective studies happen at a relatively smaller scale and do not require much time to complete. 
• It helps you to study rare outcomes when prospective surveys are not feasible.

Disadvantages of Retrospective Study

• It is easily affected by recall bias or misclassification bias.
• It often depends on convenience sampling, which is prone to selection bias. 

3. Cohort Study  

A cohort study entails collecting information from a group of people who share specific traits or have experienced a particular occurrence simultaneously. For example, a researcher might conduct a cohort study on a group of Black school children in the U.K. 

During cohort study, the researcher exposes some group members to a specific characteristic or risk factor. Then, she records the outcome of this exposure and its impact on the exposed variables. 

When To Use Cohort Study

You should conduct a cohort study if you’re looking to establish a causal relationship within your data sets. For example, in medical research, cohort studies investigate the causes of disease and establish links between risk factors and effects. 

Advantages of Cohort Studies

• It allows you to study multiple outcomes that can be associated with one risk factor. 
• Cohort studies are designed to help you measure all variables of interest. 

Disadvantages of Cohort Studies

• Cohort studies are expensive to conduct.
• Throughout the process, the researcher has less control over variables. 

When Would You Use a Longitudinal Study? 

If you’re looking to discover the relationship between variables and the causal factors responsible for changes, you should adopt a longitudinal approach to your systematic investigation. Longitudinal studies help you to analyze change over a meaningful time. 

How to Perform a Longitudinal Study?

There are only two approaches you can take when performing a longitudinal study. You can either source your own data or use previously gathered data.

1. Sourcing for your own data

Collecting your own data is a more verifiable method because you can trust your own data. The way you collect your data is also heavily dependent on the type of study you’re conducting.

If you’re conducting a retrospective study, you’d have to collect data on events that have already happened. An example is going through records to find patterns in cancer patients.

For a prospective study, you collect the data in real-time. This means finding a sample population, following them, and documenting your findings over the course of your study.

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2. Using previously gathered data

Governmental and research institutes often carry out longitudinal studies and make the data available to the public. So you can pick up their previously researched data and use them for your own study. An example is the UK data service website .

Using previously gathered data isn’t just easy, they also allow you to carry out research over a long period of time. 

The downside to this method is that it’s very restrictive because you can only use the data set available to you. You also have to thoroughly examine the source of the data given to you. 

Advantages of a Longitudinal Study 

• Longitudinal studies help you discover variable patterns over time, leading to more precise causal relationships and research outcomes. 
• When researching developmental trends, longitudinal studies allow you to discover changes across lifespans and arrive at valid research outcomes. 
• They are highly flexible, which means the researcher can adjust the study’s focus while it is ongoing. 
• Unlike other research methods, longitudinal studies collect unique, long-term data and highlight relationships that cannot be discovered in a short-term investigation. 
• You can collect additional data to study unexpected findings at any point in your systematic investigation. 

Disadvantages and Limitations of a Longitudinal Study 

• It’s difficult to predict the results of longitudinal studies because of the extended time frame. Also, it may take several years before the data begins to produce observable patterns or relationships that can be monitored. 
• It costs lots of money to sustain a research effort for years. You’ll keep incurring costs every year compared to other forms of research that can be completed in a smaller fraction of the time.
• Longitudinal studies require a large sample size which might be challenging to achieve. Without this, the entire investigation will have little or no impact. 
• Longitudinal studies often experience panel attrition. This happens when some members of the research sample are unable to complete the study due to several reasons like changes in contact details, refusal, incapacity, and even death. 

Longitudinal Studies Examples

How does a longitudinal study work in the real world? To answer this, let’s consider a few typical scenarios. 

A researcher wants to know the effects of a low-carb diet on weight loss. So, he gathers a group of obese men and kicks off the systematic investigation using his preferred longitudinal study method. He records information like how much they weigh, the number of carbs in their diet, and the like at different points. All these data help him to arrive at valid research outcomes. 

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A researcher wants to know if there’s any relationship between children who drink milk before school and high classroom performance . First, he uses a sampling technique to gather a large research population. 

Then, he conducts a baseline survey to establish the premise of the research for later comparison. Next, the researcher gives a log to each participant to keep track of predetermined research variables . 

Example 3  

You decide to study how a particular diet affects athletes’ performance over time. First, you gather your sample population , establish a baseline for the research, and observe and record the required data.

Longitudinal Studies Frequently Asked Questions (FAQs) 

• Are Longitudinal Studies Quantitative or Qualitative?

Longitudinal studies are primarily a qualitative research method because the researcher observes and records changes in variables over an extended period. However, it can also be used to gather quantitative data depending on your research context. 

• What Is Most Likely the Biggest Problem with Longitudinal Research?

The biggest challenge with longitudinal research is panel attrition. Due to the length of the research process, some variables might be unable to complete the study for one reason or the other. When this happens, it can distort your data and research outcomes. 

• What is Longitudinal Data Collection?

Longitudinal data collection is the process of gathering information from the same sample population over a long period. Longitudinal data collection uses interviews, surveys, and observation to collect the required information from research sources. 

• What is the Difference Between Longitudinal Data and a Time Series Analysis?

Because longitudinal studies collect data over a long period, they are often mistaken for time series analysis. So what’s the real difference between these two concepts? 

In a time series analysis, the researcher focuses on a single individual at multiple time intervals. Meanwhile, longitudinal data focuses on multiple individuals at various time intervals. 

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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.

Conclusions

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 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.

Recommendations

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:

http://www.biomedcentral.com/1471-2288/13/14/prepub

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A longitudinal study is a research method used to investigate changes in a group of subjects over an extended period of time. Unlike cross-sectional studies that capture data at a single point in time, longitudinal studies follow participants over a prolonged period. This allows researchers to examine how variables gradually evolve or affect individuals.

In case your research revolves around observing the same group of participants, you need to know well how to conduct longitudinal study. Today we’ll focus on this type of research data collection and find out which scientific areas require it. Its peculiar features and differences from other research types will also be examined.  This article can help a lot with planning and organizing a research project over a long time period. Below you’ll find some tips on completing such work as well as a few helpful examples from a college paper writing service . Feel free to go on in case you aim to complete such work.

What Is a Longitudinal Study: Definition

Let’s define ‘ longitudinal study ’ to begin with. This is an approach when data from the same respondents’ group is gathered repeatedly over a period of time. The reason why the same individuals are continuously observed over an extended period is to find changes and trends which can be analyzed. This approach is essentially observational as you aren’t expected to influence the group’s parameters you are monitoring in any way. It is typically used in scope of correlational research which means collecting data about variables without assuming any dependencies. Let’s find out more about its usage and how much time it could take.

How Long Are Longitudinal Studies?

How long is a longitudinal study? It depends on your topic and research goals. In case characteristics of the subject are changing fast, it might be enough to take just a few measurements one by one. Otherwise, one might have to wait for a long time before measuring again. So, such projects can take weeks or months but they also can extend over years or even decades. Studies like that are common in medicine, psychology and sociology, where it is important to observe how participants’ characteristics evolve.

How to Perform Longitudinal Research?

Before actively engaging in longitudinal research, it is important to understand well what your next steps should be. Let’s define study subtypes that can be used for such research. They are:

• Collecting and analyzing your own data.
• Finding data already collected by some other researcher and analyzing it.

Each of these subtypes has certain pros and cons. Gathering data yourself usually gives more confidence but it might be hard to contact the right individuals. Let’s discuss each point in detail. Likewise, you can pay someone to write my research paper .

Longitudinal Study: Data From Other Sources

When doing longitudinal studies of a certain group over a long period, you might find available data about them left from other researchers. Make sure to carefully examine sources of each dataset you decide to reuse. Otherwise previous researchers’ mistakes or bias may influence your results after you’ve analyzed that data. However this approach could be very efficient in case the subject has already been investigated by different researchers. Their results could be compared and gaps or bias could be easier to eliminate. As a result, much time and effort could be saved.

Longitudinal Design: Own Data

When doing longitudinal studies without any significant predecessors’ works available, using your own data is the only reasonable way. This data is collected through surveys, measurement or observations. Thus you have more confidence in these results however this approach requires more time and effort. You need proper research design methods  prior to starting the collection process. If you choose such an approach, keep in mind that it has two major subtypes:

• retrospective research: collecting data about past events.
• prospective research: observing ongoing events, making measurements in more or less real time.

Longitudinal Study Types

A longitudinal study can be applied to a wide range of cases. You need to adjust your approach, depending on a specific situation, subject’s peculiarities and your research goals.  There are three major research types you can use for continuous observation:

Longitudinal Cohort Study

Retrospective longitudinal study, longitudinal panel study.

Let’s take a closer look at each type’s definition with our coursework writing service . Dive deep to learn how data is collected and what impact is made on results.

A cohort longitudinal study involves selecting a group based on some unique event which unifies them all. It can be their birth date, geographic location, or historical experience. So there are special relationships between that group’s members which play significant roles for the entire research process. Such a peculiarity is to be carefully selected when doing test design and planning your test steps. Sometimes one unifying event may be more relevant or convenient than another.

This approach takes a special place among longitudinal studies as it involves conducting some historical investigations. As we’ve already mentioned above, during a retrospective, researchers have to make observations and measurements of past events. Collecting historical data and analyzing changes might be easier than tracking live data. However the development of such research design must include checking the credibility of datasets that were used for it.

A panel study involves sampling a cross-section of individuals. This approach is often used for collecting medical data. Such a study when performed continuously is considered more reliable compared to a regular cross sectional study and allows using smaller sample sizes, while still being representative. However, there are various problems that may occur during such studies, especially those which go on for decades. Particularly, such samples can be eventually eroded because of deaths, migration, fatigue, or even by development of response bias.

Longitudinal Research Design

Longitudinal study design requires some serious planning to complete it properly. Keep in mind that your purpose is to directly address some individual change and variation cases. The target population should be chosen carefully so that results achieved through this study would be accurate enough. Another key element is deciding about proper timing. For example you would need bigger intervals to ensure you detect important changes. At the same time, dissertation writers suggest that the intervals shouldn’t be too big. Otherwise, you might lose track of the actual trends within your target population.

Advantages and Disadvantages of Longitudinal Study

Let’s review longitudinal study advantages and disadvantages. Better wrap your head around this information if you are still choosing an optimal approach for your own project. Any study that involves complicated planning and extensive techniques can have some downsides. It is common for them to come together with benefits. So pay close attention to the information below before deciding what method to choose to observe your research subject.

Advantages of Longitudinal Study

These are the benefits of longitudinal study:

• it can provide unique insight that might not be available any other way. Particularly, it is the only way to investigate lifespan issues. It allows researchers to track changes across the entire generation . Let’s suppose the task is to track the percentage of farms which pass from parents to children in a certain location. Obtaining such information requires using historical records.
• such observational approach shows dynamics in respondent’s data and thus allows to model trends and understand their influence. Collecting data once provides only a snapshot of your group’s current state. Doing it continuously allows you to observe this group from some new angles. For example, you would get more information about your respondents’ habits if you observe them at least several times.

Longitudinal Study Disadvantages

This is the disadvantages of longitudinal study:

• it can be quite expensive since numerous repeated measurements require enormous amounts of time and effort. Imagine you need to collect data about a certain group for 10 years. Processing this data alone would require a lot of resources.
• such high costs may induce another problem: researchers might decide to use lesser samples in order to cut the expenditures. Consequently, results of such studies may not be representative enough.
• its participants tend to drop out eventually. The reasons may vary: moving to another location, illness, death or just loss of motivation to participate further. As a result, a sample is shrinking and thus decreasing the amount of data collection in research . This process is called selective attrition. A typical example is observing the life of some neighborhood in a big city: numerous people would move in and out so it would be hard to find a single individual who is available for a long time.

Longitudinal Study Examples

Let’s review some longitudinal study example which would be helpful for illustrating the above information.

Longitudinal research example A famous longitudinal case is The Terman Study of the Gifted also known previously as Genetic Studies of Genius. Its founder and the main researcher, Lewis Terman, aimed to investigate how highly intelligent children developed into adulthood. He was also going to disprove the then-prevalent belief that gifted children were typically delicate physically and also socially inept. Initial observations began in 1921, at Stanford University. Eventually it led to confirming that gifted children were not significantly different from their peers in terms of physical development and social skills. The results of this study were still being compiled during the 2000s which makes it the oldest and longest-running longitudinal study in the world. Such a huge period of data collection made it possible to obtain some really unique knowledge, not only about children’s development but about the history of education as well.

Longitudinal: Final Thoughts

In this article we’ve explored the longitudinal research notion and reviewed its main characteristics:

• conducting observations and measurements continuously over a long period of time
• some particular new insights which can be obtained by prolonged studies
• prospective advantages and disadvantages for researchers.

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Frequently Asked Questions About Longitudinal Studies

1. is a longitudinal study quantitative or qualitative.

According to the definition of a longitudinal study, quantitative methods don’t play any significant role in the process. This approach includes extended case studies, observing individuals over long periods and gaining additional insights thanks to the possibility to analyze changes over time. Since these observations and resulting assumptions mostly consist of descriptions of trends, changes and influences, we can say that it is a purely qualitative approach.

2. Are longitudinal studies more reliable?

Longitudinal studies in general have similar amounts of problems and risks as other studies do. This includes:

• survey aging and period effects.
• delayed results.
• achieving continuity in funding and research direction.
• cumulative attrition.

These factors can decrease reliability of this study type and must be taken into account when selecting such an approach. 

3. Is attrition a limitation of longitudinal studies?

Depending on how big is the period they take, longitudinal studies may suffer more or less for the attrition factor. It can deteriorate generalizability of findings if participants who stay in a study are significantly different from those who drop out. In case a particular study takes many years, researchers need to see the attrition factor as a serious problem and to develop some ways to counter its negative effect.

4. What is longitudinal data collection?

Longitudinal data collection occurs sequentially from the same respondents over time. This is the core element of this study type. Repeated collection of data allows researchers to see temporal changes and understand what trends are there in this population. It allows viewing it from some new angles and thus to obtain new insights about it. There are certain limitations to such data collection, particularly when the target group tends to change over time.

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• Open access
• Published: 24 August 2024

Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson’s disease over seven years in a cohort: a comparative analysis

• Anne-Marie Hanff 1 , 2 , 3 , 4 ,
• Rejko Krüger 1 , 2 , 5 ,
• Christopher McCrum 4 ,
• Christophe Ley 6 on behalf of

BMC Medical Research Methodology volume  24 , Article number:  183 ( 2024 ) Cite this article

Metrics details

Introduction

While there is an interest in defining longitudinal change in people with chronic illness like Parkinson’s disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort.

In this retrospective longitudinal analysis of 802 people with typical Parkinson’s disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models.

Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p -values (+ 1.016/ 7 years, p  = 0.107, -0.056/ 7 years, p  = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p -value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years ( p  < 0.001).

The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.

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In longitudinal studies: “an outcome is repeatedly measured, i.e., the outcome variable is measured in the same subject on several occasions.” [ 1 ]. When assessing the same individuals over time, the different data points are likely to be more similar to each other than measurements taken from other individuals. Consequently, the application of special statistical techniques is required, which take into account the fact that the repeated observations of each subject are correlated [ 1 ]. Parkinson’s disease (PD) is a heterogeneous neurodegenerative disorder resulting in a wide variety of motor and non-motor symptoms including apathy, defined as a disorder of motivation, characterised by reduced goal-directed behaviour and cognitive activity and blunted affect [ 2 ]. Apathy increases over time in people with PD [ 3 ]. Specifically, apathy has been associated with the progressive denervation of ascending dopaminergic pathways in PD [ 4 , 5 ] leading to dysfunctions of circuits implicated in reward-related learning [ 5 ].

T-tests are often misused to analyse changes over time [ 6 ]. Consequently, we aim to demonstrate how the choice of statistical method may influence research outcomes, specifically the size and interpretation of longitudinal effect estimates in a cohort. Thus, the findings are intended for illustrative and educational purposes related to the statistical methodology. In a retrospective analysis of data from the Luxembourg Parkinson's study, a nation-wide, monocentric, observational, longitudinal-prospective dynamic cohort [ 7 , 8 ], we assess change in apathy using three different statistical approaches (paired t-test, linear regression, mixed effects model). We defined the following target estimand: In people diagnosed with PD, what is the change in the apathy score from visit 1 to visit 8? To estimate this change, we formulated the statistical hypothesis as follows:

While apathy was the dependent variable, we included the visit number as an independent variable (linear regression, mixed effects model) and as a grouping variable (paired t-test). The outcome apathy was measured by the discrete score from the Starkstein apathy scale (0 – 42, higher = worse) [ 9 ], a scale recommended by the Movement Disorders Society [ 10 ]. This data was obtained from the National Centre of Excellence in Research on Parkinson's disease (NCER-PD). The establishment of data collection standards, completion of the questionnaires at home at the participants’ convenience, mobile recruitment team for follow-up visits or standardized telephone questionnaire with a reduced assessment were part of the efforts in the primary study to address potential sources of bias [ 7 , 8 ]. Ethical approval was provided by the National Ethics Board (CNER Ref: 201,407/13). We used data from up to eight visits, which were performed annually between 2015 and 2023. Among the participants are people with typical PD and PD dementia (PDD), living mostly at home in Luxembourg and the Greater Region (geographically close areas of the surrounding countries Belgium, France, and Germany). People with atypical PD were excluded. The sample at the date of data export (2023.06.22) consisted of 802 individuals of which 269 (33.5%) were female. The average number of observations was 3.0. Fig. S1 reports the numbers of individuals at each visit while the characteristics of the participants are described in Table  1 .

As illustrated in the flow diagram (Fig.  1 ), the sample analysed from the paired t-test is highly selective: from the 802 participants at visit 1, the t-test only included 63 participants with data from visit 8. This arises from the fact that, first, we analyse the dataset from a dynamic cohort, i.e., the data at visit 1 were not collected at the same time point. Thus, 568 of the 802 participants joined the study less than eight years before, leading to only 234 participants eligible for the eighth yearly visit. Second, after excluding non-participants at visit 8 due to death ( n  = 41) and other reasons ( n  = 130), only 63 participants at visit 8 were left. To discuss the selective study population of a paired t-test, we compared the characteristics (age, education, age at diagnosis, apathy at visit 1) of the remaining 63 participants at visit 8 (included in the paired t-test) and the 127 non-participants at visit 8 (excluded from the paired t-test) [ 12 ].

Flow diagram of patient recruitment

The paired two-sided t-test compared the mean apathy score at visit 1 with the mean apathy score at the visit 8. We attract the reader’s attention to the fact that this implies a rather small sample size as it includes only those people with data from the first and 8th visit. The linear regression analysed the relationship between the visit number and the apathy score (using the “stats” package [ 13 ]), while we performed longitudinal two-level mixed effects models analysis with a random intercept on subject level, a random slope for visit number and the visit number as fixed effect (using the “lmer”-function of the “lme4”-package [ 14 ]). The latter two approaches use all available data from all visits while the paired t-test does not. We illustrated the analyses in plots with the function “plot_model” of the R package sjPlot [ 15 ]. We conducted data analysis using R version 3.6.3 [ 13 ] and the R syntax for all analyses is provided on the OSF project page ( https://doi.org/ https://doi.org/10.17605/OSF.IO/NF4YB ).

Panel A in Fig.  2 illustrates the means and standard deviations of apathy for all participants at each visit, while the flow-chart (Fig. S1 ) illustrates the number of participants at each stage. On average, we see lower apathy scores at visit 8 compared to visit 1 (higher score = worse). By definition, the paired t-test analyses pairs, and in this case, only participants with complete apathy scores at visit 1 and visit 8 are included, reducing the total analysed sample to 63 pairs of observations. Consequently, the t-test compares mean apathy scores in a subgroup of participants with data at both visits leading to different observations from Panel A, as illustrated and described in Panel B: the apathy score has increased at visit 8, hence symptoms of apathy have worsened. The outcome of the t-test along with the code is given in Table  2 . Interestingly, the effect estimates for the increase in apathy were not statistically significant (+ 1.016 points, 95%CI: -0.225, 2.257, p  = 0.107). A possible reason for this non-significance is a loss of statistical power due to a small sample size included in the paired t-test. To visualise the loss of information between visit 1 and visit 8, we illustrated the complex individual trajectories of the participants in Fig.  3 . Moreover, as described in Table S1 in the supplement, the participants at visit 8 (63/190) analysed in the t-test were inherently significantly different compared to the non-participants at visit 8 (127/190): they were younger, had better education, and most importantly their apathy scores at visit 1 were lower. Consequently, those with the better overall situation kept coming back while this was not the case for those with a worse outcome at visit 1, which explains the observed (non-significant) increase. This may result in a biased estimation of change in apathy when analysed by the compared statistical methods.

Bar charts illustrating apathy scores (means and standard deviations) per visit (Panel A: all participants, Panel B: subgroup analysed in the t-test). The red line indicates the mean apathy at visit 1

Scatterplot illustrating the individual trajectories. The red line indicates the regression line

From the results in Table  2 , we see that the linear regression coefficient, representing change in apathy symptoms per year, is not significantly different from zero, indicating no change over time. One possible explanation is the violation of the assumption of independent observations for linear regressions. On the contrary, the effect estimates for the linear mixed effects models indicated a significant increase in apathy symptoms from visit 1 to visit 8 by + 2.680 points (95%CI: 1.880, 3.472, p  < 0.001). Consequently, mixed effects models were the only method able to detect an increase in apathy symptoms over time and choosing mixed effect models for the analysis of longitudinal data reduces the risk of false negative results. The differences in the effect sizes are also reflected in the regression lines in Panel A and B of Fig.  4 .

Scatterplot illustrating the relationship between visit number and apathy. Apathy measured by a whole number interval scale, jitter applied on x- and y-axis to illustrate the data points (Panel A: Linear regression, Panel B: Linear mixed effects model). The red line indicates the regression line

The effect sizes differed depending on the choice of the statistical method. Thus, the paired t-test and the linear regression resulted in an output that would lead to different interpretations than the mixed effects models. More specifically, compared to the t-test and linear regression (which indicated non-significant changes in apathy of only + 1.016, -0.064 points from visit 1 to visit 8, respectively), the linear mixed effects models found an increase of + 2.680 points from visit 1 to visit 8 on the apathy scale. This increase is more than twice as high as indicated by the t-test and suggests linear mixed models is a more sensitive approach to detect meaningful changes perceived by people with PD over time.

Mixed effects models are a valuable tool in longitudinal data analysis as these models expand upon linear regression models by considering the correlation among repeated measurements within the same individuals through the estimation of a random intercept [ 1 , 16 , 17 ]. Specifically, to account for correlation between observations, linear mixed effects models use random effects to explicitly model the correlation structure, thus removing correlation from the error term. A random slope in addition to a random intercept allows both the rate of change and the mean value to vary by participant, capturing individual differences. This distinguishes them from group comparisons or standard linear regressions, in which such explicit modelling of correlation is not possible. Thus, the linear regression not considering correlation among the repeated observations leads to an underestimation of longitudinal change, explaining the smaller effect sizes and insignificant results of the regression. By including random effects, linear mixed effects models can better capture the variability within the data.

Another common challenge in longitudinal studies is missing data. Compared to the paired t-test and regression, the mixed effects models can also include participants with missing data at single visits and account for the individual trajectories of each participant as illustrated in Fig.  2 [ 18 ]. Although multiple imputation could increase the sample size, those results need to be interpreted with caution in case the data is not missing at random [ 18 , 19 ]. Note that we do not further elaborate here on this topic since this is a separate issue to statistical method comparison. Finally, assumptions of the different statistical methods need to be respected. The paired t-test assumes a normal distribution, homogeneity of variance and pairs of the same individuals in both groups [ 20 , 21 ]. While mixed effects models don’t rely on independent observations as it is the case for linear regression, all other assumptions for standard linear regression analysis (e.g., linearity, homoscedasticity, no multicollinearity) also hold for mixed effects model analyses. Thus, additional steps, e.g., check for linearity of the relationships or data transformations are required before the analysis of clinical research questions [ 17 ].

While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome [ 1 ], they are worth considering for longitudinal data analyses. Thus, assuming an increase of apathy over time [ 3 ], mixed effects models were the only method able to detect statistically significant changes in the defined estimand, i.e., the change in apathy from visit 1 to visit 8. Possible reasons are a loss of statistical power due to a small sample size included in the paired t-test and the violence of the assumption of independent observations for linear regressions. Specifically, the effects estimated for the group comparison and the linear regression were smaller with high p -values, indicating a statistically insignificant change in apathy over time. The effect estimates for the mixed effects models were positive with a very small p -value, indicating a statistically significant increase in apathy symptoms from visit 1 to visit 8 in line with clinical expectations. Mixed effects models can be used to estimate different types of longitudinal effects while an inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change and thus clinical significance. Therefore, researchers should more often consider mixed effects models for longitudinal analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.

Availability of data and materials

The LUXPARK database used in this study was obtained from the National Centre of Excellence in Research on Parkinson’s disease (NCER-PD). NCER-PD database are not publicly available as they are linked to the Luxembourg Parkinson’s study and its internal regulations. The NCER-PD Consortium is willing to share its available data. Its access policy was devised based on the study ethics documents, including the informed consent form approved by the national ethics committee. Requests for access to datasets should be directed to the Data and Sample Access Committee by email at [email protected].

The code is available on OSF ( https://doi.org/10.17605/OSF.IO/NF4YB )

Abbreviations

Parkinson's disease

Null hypothesis

Alternative hypothesis

Parkinson's disease dementia

National Centre of Excellence in Research on Parkinson's disease

Open Science Framework

Confidence Interval

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Acknowledgements

We would like to thank all participants of the Luxembourg Parkinson’s Study for their important support of our research. Furthermore, we acknowledge the joint effort of the National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD) Consortium members from the partner institutions Luxembourg Centre for Systems Biomedicine, Luxembourg Institute of Health, Centre Hospitalier de Luxembourg, and Laboratoire National de Santé generally contributing to the Luxembourg Parkinson’s Study as listed below:

Geeta ACHARYA 2, Gloria AGUAYO 2, Myriam ALEXANDRE 2, Muhammad ALI 1, Wim AMMERLANN 2, Giuseppe ARENA 1, Michele BASSIS 1, Roxane BATUTU 3, Katy BEAUMONT 2, Sibylle BÉCHET 3, Guy BERCHEM 3, Alexandre BISDORFF 5, Ibrahim BOUSSAAD 1, David BOUVIER 4, Lorieza CASTILLO 2, Gessica CONTESOTTO 2, Nancy DE BREMAEKER 3, Brian DEWITT 2, Nico DIEDERICH 3, Rene DONDELINGER 5, Nancy E. RAMIA 1, Angelo Ferrari 2, Katrin FRAUENKNECHT 4, Joëlle FRITZ 2, Carlos GAMIO 2, Manon GANTENBEIN 2, Piotr GAWRON 1, Laura Georges 2, Soumyabrata GHOSH 1, Marijus GIRAITIS 2,3, Enrico GLAAB 1, Martine GOERGEN 3, Elisa GÓMEZ DE LOPE 1, Jérôme GRAAS 2, Mariella GRAZIANO 7, Valentin GROUES 1, Anne GRÜNEWALD 1, Gaël HAMMOT 2, Anne-Marie HANFF 2, 10, 11, Linda HANSEN 3, Michael HENEKA 1, Estelle HENRY 2, Margaux Henry 2, Sylvia HERBRINK 3, Sascha HERZINGER 1, Alexander HUNDT 2, Nadine JACOBY 8, Sonja JÓNSDÓTTIR 2,3, Jochen KLUCKEN 1,2,3, Olga KOFANOVA 2, Rejko KRÜGER 1,2,3, Pauline LAMBERT 2, Zied LANDOULSI 1, Roseline LENTZ 6, Laura LONGHINO 3, Ana Festas Lopes 2, Victoria LORENTZ 2, Tainá M. MARQUES 2, Guilherme MARQUES 2, Patricia MARTINS CONDE 1, Patrick MAY 1, Deborah MCINTYRE 2, Chouaib MEDIOUNI 2, Francoise MEISCH 1, Alexia MENDIBIDE 2, Myriam MENSTER 2, Maura MINELLI 2, Michel MITTELBRONN 1, 2, 4, 10, 12, 13, Saïda MTIMET 2, Maeva Munsch 2, Romain NATI 3, Ulf NEHRBASS 2, Sarah NICKELS 1, Beatrice NICOLAI 3, Jean-Paul NICOLAY 9, Fozia NOOR 2, Clarissa P. C. GOMES 1, Sinthuja PACHCHEK 1, Claire PAULY 2,3, Laure PAULY 2, 10, Lukas PAVELKA 2,3, Magali PERQUIN 2, Achilleas PEXARAS 2, Armin RAUSCHENBERGER 1, Rajesh RAWAL 1, Dheeraj REDDY BOBBILI 1, Lucie REMARK 2, Ilsé Richard 2, Olivia ROLAND 2, Kirsten ROOMP 1, Eduardo ROSALES 2, Stefano SAPIENZA 1, Venkata SATAGOPAM 1, Sabine SCHMITZ 1, Reinhard SCHNEIDER 1, Jens SCHWAMBORN 1, Raquel SEVERINO 2, Amir SHARIFY 2, Ruxandra SOARE 1, Ekaterina SOBOLEVA 1,3, Kate SOKOLOWSKA 2, Maud Theresine 2, Hermann THIEN 2, Elodie THIRY 3, Rebecca TING JIIN LOO 1, Johanna TROUET 2, Olena TSURKALENKO 2, Michel VAILLANT 2, Carlos VEGA 2, Liliana VILAS BOAS 3, Paul WILMES 1, Evi WOLLSCHEID-LENGELING 1, Gelani ZELIMKHANOV 2,3

1 Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

2 Luxembourg Institute of Health, Strassen, Luxembourg

3 Centre Hospitalier de Luxembourg, Strassen, Luxembourg

4 Laboratoire National de Santé, Dudelange, Luxembourg

5 Centre Hospitalier Emile Mayrisch, Esch-sur-Alzette, Luxembourg

6 Parkinson Luxembourg Association, Leudelange, Luxembourg

7 Association of Physiotherapists in Parkinson's Disease Europe, Esch-sur-Alzette, Luxembourg

8 Private practice, Ettelbruck, Luxembourg

9 Private practice, Luxembourg, Luxembourg

10 Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

11 Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Maastricht University Medical Centre+, Maastricht, the Netherlands

12 Luxembourg Center of Neuropathology, Dudelange, Luxembourg

13 Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

This work was supported by grants from the Luxembourg National Research Fund (FNR) within the National Centre of Excellence in Research on Parkinson's disease [NCERPD(FNR/NCER13/BM/11264123)]. The funding body played no role in the design of the study and collection, analysis, interpretation of data, and in writing the manuscript.

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Anne-Marie Hanff & Rejko Krüger

Translational Neurosciences, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg

Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University Medical Centre+, Maastricht, The Netherlands

Anne-Marie Hanff

Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands

Anne-Marie Hanff & Christopher McCrum

Parkinson Research Clinic, Centre Hospitalier du Luxembourg, Luxembourg, Luxembourg

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Contributions

A-MH: Conceptualization, Methodology, Formal analysis, Investigation, Visualization, Project administration, Writing – original draft, Writing – review & editing. RK: Conceptualization, Methodology, Funding, Resources, Supervision, Project administration, Writing – review & editing. CMC: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing. CL: Conceptualization, Methodology, Writing – original draft, Writing – review & editing.

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Correspondence to Anne-Marie Hanff .

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Hanff, AM., Krüger, R., McCrum, C. et al. Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson’s disease over seven years in a cohort: a comparative analysis. BMC Med Res Methodol 24 , 183 (2024). https://doi.org/10.1186/s12874-024-02301-7

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DOI : https://doi.org/10.1186/s12874-024-02301-7

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