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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.

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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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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."

Definition of Longitudinal Study:

A longitudinal study is a type of research design used in several fields such as psychology, sociology, and medicine. It involves collecting data from the same subjects over an extended period to observe changes or trends that occur over time.

Key Elements of a Longitudinal Study:

• Sample Selection: Researchers carefully select a representative sample of participants who meet specific criteria for the study.
• Data Collection: Data is collected from participants at multiple time points, often using various methods such as surveys, interviews, observations, and medical tests.
• Time Frame: Longitudinal studies typically extend over months, years, or even decades to capture long-term changes and developments.
• Data Analysis: Researchers analyze the collected data to identify patterns, correlations, and trends that emerge over time.
• Observation and Measurement: Longitudinal studies rely on repeated measurements of variables of interest to track individual or group changes accurately.
• Follow-up Rates: Maintaining high follow-up rates throughout the study helps minimize potential biases and enhances the validity of the findings.

Advantages of Longitudinal Studies:

• Temporal Changes: Longitudinal studies allow researchers to examine changes and developments over time, providing a more accurate understanding of how variables are influenced.
• Cause-and-effect Relationships: By collecting data at multiple points, researchers can explore causality between variables, establishing stronger connections and identifying possible influencing factors.
• Individual Differences: Longitudinal studies enable the examination of individual differences in behavior, cognition, health status, and other variables.
• Complex Analyses: The data collected in longitudinal studies can be subjected to sophisticated statistical analyses, allowing for a deeper understanding of the relationships and dynamics involved.

Limitations of Longitudinal Studies:

• Time and Cost: Conducting a longitudinal study requires substantial time, resources, and funding due to the extended duration and potential attrition of participants over time.
• Attrition: Participants may drop out or become lost to follow-up, leading to biased results if the attrition is non-random.
• External Factors: Changes in the external environment, such as cultural, societal, or political shifts, may influence the study outcomes.
• Selection Bias: Longitudinal studies depend on the initial selection of participants, potentially introducing selection bias if specific characteristics are over- or underrepresented.

Overall, longitudinal studies provide valuable insights into how variables evolve over time, offering a comprehensive understanding of various phenomena and aiding in evidence-based decision-making in numerous fields.

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Longitudinal studies are a type of research design that involves observing and collecting data on the same individuals or group over an extended period of time. These studies allow researchers to track changes, patterns, and developments within a population as it evolves over the course of the study.

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5 Must Know Facts For Your Next Test

• Longitudinal studies allow researchers to observe how individuals or groups change and develop over time, providing valuable insights into the dynamics of human behavior and development.
• These studies are particularly useful for understanding the long-term effects of interventions, treatments, or life events on individuals or populations.
• Longitudinal studies often involve collecting data at multiple time points, which can include surveys, interviews, observations, or physiological measurements.
• Compared to cross-sectional studies, longitudinal studies can better establish causal relationships and distinguish between short-term and long-term effects.
• Maintaining participant engagement and minimizing attrition are key challenges in longitudinal research, as the loss of participants over time can compromise the validity of the findings.

Review Questions

• Longitudinal studies are crucial for understanding the importance of research because they allow researchers to observe and track changes over time, providing valuable insights into the underlying causes and long-term effects of various phenomena. By following the same individuals or groups over an extended period, longitudinal studies can help researchers identify patterns, establish causal relationships, and understand the complex dynamics that shape human behavior and development. This type of research is essential for informing effective interventions, policies, and practices that can improve individual and societal well-being.
• Longitudinal studies are a fundamental tool for studying lifespan development in psychology. By following individuals or cohorts over the course of their lives, researchers can observe and document the changes, transitions, and continuities that occur across various developmental domains, such as physical, cognitive, emotional, and social. Longitudinal data allows psychologists to identify developmental trajectories, understand the factors that influence growth and change, and examine the long-term consequences of life events, experiences, and interventions. This in-depth understanding of lifespan development is crucial for designing effective programs, policies, and interventions that support individuals throughout their lives.
• The primary advantage of longitudinal studies is their ability to provide a comprehensive, in-depth understanding of how individuals and populations change over time. By tracking the same participants over an extended period, researchers can identify causal relationships, observe the long-term effects of interventions or life events, and gain insights into the complex, dynamic nature of human behavior and development. However, longitudinal studies also face several limitations, such as the challenge of maintaining participant engagement and minimizing attrition, which can compromise the validity and generalizability of the findings. Additionally, longitudinal studies can be time-consuming, resource-intensive, and susceptible to the influence of historical and cultural factors that may change over the course of the study. Nonetheless, when designed and executed effectively, longitudinal research remains a powerful tool for advancing our understanding of psychological phenomena and informing evidence-based practices and policies.

Related terms

Cross-Sectional Studies : Cross-sectional studies collect data from a population at a single point in time, providing a snapshot of the characteristics of that population.

Cohort Studies : Cohort studies follow a specific group or cohort of individuals who share a common characteristic, such as age or exposure, over time to observe how events or outcomes unfold.

Attrition : Attrition refers to the loss of participants over the course of a longitudinal study, which can impact the validity and generalizability of the findings.

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Longitudinal study

Understanding Longitudinal Studies: A Comprehensive Overview

Longitudinal studies play a crucial role in scientific research, providing valuable insights into the dynamics of various phenomena over time. By following individuals or groups over an extended period, researchers can examine changes, trends, and correlations, and gain a deeper understanding of how variables evolve.

Studies on human behavior health and behavior generally are either “cross-sectional” or “longitudinal.” Cross-sectional studies “take a slice out of the world at a given moment and look inside” ( Waldinger & Schulz, 2023 ). Most new research is from cross sectional studies. Honestly, if you are working on your doctoral dissertation, you don’t have thirty years to follow a group of people. In a cross-sectional study, takes a snapshot in time, examining variables and connections.

Key Definition:

Longitudinal study is research that involves observing and collecting data over an extended period of time.

Longitudinal studies examine lives through time. Some studies, such as the Harvard Study of Adult Development, follows subjects throughout their entire lives. These studies are not retrospective, asking questions at the end of a life span; they are prospective gathering information at regular intervals, learning about the test subjects lives as they occur ( Waldinger & Schulz, 2023 ).

What Is a Longitudinal Study?

A longitudinal study is a research design that involves observing and collecting data from the same subjects repeatedly over an extended period. Unlike cross-sectional studies that capture a snapshot of a particular point in time, longitudinal studies offer a longitudinal perspective, enabling scientists to analyze changes and patterns over time.

Longitudinal studies provide real world views of life. We don’t live in a snapshot of time but over a course of decades. Longitudinal studies provided the fodder for the insightful life course theory . Daniel J. Sigel wrote, “life course theory and research alert us to this real world, a world in which lives are lived and where people work out paths of development as best they can. It tells us how lives are socially organized in biological and historical time, and how the resulting social pattern affects the way we think, feel and act” (Siegel, 2020, Kindle location: 8,340).

History of Longitudinal Studies

William Isaac Thomas conducted a study of Polish peasants in America and Europe following participants in the study from 1918 to 1920. His study was one of the first notable longitudinal studies. Thomas strongly suggested that there was a vital need for a ‘longitudinal approach to life history’ using recorded data. He advocated following “groups of individuals into the future, getting a continuous record of experience as they occur” ( Elder, 2003, p. 3 ).

A few notable studies early studies were the Grant and Glueck studies beginning in the 1940’s. The Grant study followed 268 Harvard educated men whom were members of Harvard undergraduate classes of 1942, 1943, and 1944. A study conducted in tandem with the Grant study was the Glueck study which followed a cohort of 456 disadvantaged, non-delinquent inner-city youths who grew up in Boston neighborhood between 1940 and 1945. The two longitudinal studies eventually merged, now known as the Harvard Study of Adult Development. The study has continued for over eighty years, following the original subjects, their children, and even their grandchildren.

However, even into the 1950’s little other longitudinal research was conducted. Glen Elder explains, “Quite simply, the social pathways of human lives, particularly in their historical time and place, were not a common subject of study at this time.” He continues, “consequently, social scientists knew little about how people lived their lives from childhood to old age, even less about how their life pathways influenced the course of development and aging, and still less about the importance of historical and geographical contexts” ( Elder, 2003, p. 4 ).

Human Development and Life Course Theories

It wasn’t until the 1960’s that the value of longitudinal studies was recognized. Many social and developmental scientists recognized the value of these massive sources of data. The life course movement beginning in the 1960’s relied heavily on the data now flowing from longitudinal studies. With the enormous flow of data from multiple studies, researchers were able to examine life across time, viewing entire life spans instead of small snapshots from popular cross-sectional research.

Human development researchers also found great value in the longitudinal data. Elder explains, “this wealth of data prompted a new way of thinking about human lives and development—studying life trajectories across multiple stages of life, recognizing that developmental processes extend past childhood, exploring issues of behavioral continuity and change” ( Elder, 2003 p. 5 ).

A significant finding in many of these longitudinal studies is the power of context. A life course without examination in the proper social and political context incorrectly portrays the data. John Bynner argues that “life course research is not just about tracing individuals’ histories across time. It should set these trajectories in their geographical and social context” ( Bynner, 2016 ).

Types of Longitudinal Studies

There are several types of longitudinal studies, each with its own unique characteristics and objectives:

• Trend Studies : Trend studies focus on changes across an entire population over time. Researchers collect data at multiple time points to identify and analyze trends and patterns. These studies are useful in understanding the long-term effects of interventions or policies on a large scale.
• Cohort Studies : Cohort studies follow a specific group of individuals or cohorts over a defined period. Researchers examine these cohorts at regular intervals, collecting data on variables of interest. Cohort studies are particularly valuable for investigating the development of diseases, tracking career paths, or studying the impact of social factors on a specific group.
• Panel Studies : Panel studies involve collecting data from the same group of individuals at multiple time points. Researchers can track changes within the group, analyze individual trajectories, and explore cause-and-effect relationships. These studies are commonly used in social sciences and psychology.
• Event-based Studies : Event-based studies focus on specific events or experiences that occur to individuals over time. Researchers collect data before, during, and after the event to assess its impact or consequences. Examples include studying the effects of natural disasters, major life events, or specific interventions.

Benefits of Longitudinal Studies

Longitudinal studies offer numerous benefits that contribute to the advancement of knowledge and understanding in various fields:

• Capturing Change : By observing changes over time, longitudinal studies help identify patterns and trends that would be missed in cross-sectional studies. This allows researchers to gain a more in-depth understanding of the complexities and dynamics of the variables under investigation. Copeland Young explains, “research questions about lifelong and intergenerational causal relationships are best answered by following respondents in real time rather than retrospectively” ( Young, 1991 ).
• Exploring Cause and Effect : Longitudinal studies enable researchers to investigate cause-and-effect relationships. By collecting data at multiple time points, they can analyze how changes in one variable influence another over time, helping to establish correlations and causal relationships.
• Uncovering Developmental Trajectories : Longitudinal studies are valuable for understanding development and growth, both in individuals and groups. They provide insights into the factors that contribute to positive or negative outcomes and help identify critical periods for intervention.
• Informing Policy and Interventions : The long-term perspective offered by longitudinal studies is vital for informing policy decisions and shaping effective interventions. These studies provide evidence on the long-term impacts of policies, interventions, and social factors, helping policymakers make informed choices.

Challenges and Considerations

While longitudinal studies offer valuable insights, they also present several challenges and considerations:

• Attrition : Longitudinal studies require participants to be involved for an extended period, which can lead to attrition or dropout rates. Researchers must employ strategies to ensure participant retention and address potential biases introduced by attrition.
• Time and Cost : Longitudinal studies are resource-intensive and require significant time and funding commitment. Researchers must carefully plan and allocate resources to ensure the study’s feasibility and success.
• External Factors : External factors such as societal changes, technological advancements, or unforeseen events can impact the validity and generalizability of longitudinal studies. Researchers need to consider and account for these factors in their analysis.
• Data Management and Analysis : Longitudinal studies generate a vast amount of data, requiring efficient data management and analysis techniques. Researchers should employ robust statistical methods and advanced analysis tools to derive meaningful insights.

Longitudinal studies provide a comprehensive and dynamic perspective on the variables and phenomena they investigate. By capturing changes and patterns over time, these studies enhance our understanding of development, inform policy decisions, and provide valuable insights into cause-and-effect relationships. Although challenging, the knowledge gained through longitudinal studies contributes significantly to scientific progress and societal well-being.

Remember, whether you are a researcher or simply interested in a topic, longitudinal studies play a crucial role in unraveling the mysteries of the world around us.

Last Update: April 17, 2024

Note: This article serves as a general guide to longitudinal studies and does not constitute specific research or medical advice.

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References:

Bynner, John (2016). Institutionalization of Life Course Studies. M.J. Shanahan, J.T. Mortimer, M.K. Johnson (Eds.), Handbook of the life course, vol. II, Springer, New York, pp. 27-58

Elder, Glen H., Johnson, Monica Kirkpatrick,  Crosnoe, Robert ( 2003 ) The Emergence and Development of Life Course Theory. Editors Jaylen T. Mortimer, Michael J. Shanahan. Handbook of the Life Course.  Springer, Boston, MA, 2003. 3-19.​

Joshi, Heather ( 2022 ). Placing context in longitudinal research. Longitudinal and Life Course Studies. DOI: 10.1332/175795921×16682554193545

Siegel, Daniel J. ( 2020 ). The Developing Mind: How Relationships and the Brain Interact to Shape Who We Are. The Guilford Press; 3rd edition.

Waldinger, Robert J.; Schulz. Marc ( 2023 ). The Good Life: Lessons from the World’s Longest Scientific Study of Happiness. Simon & Schuster.

Young, Copeland ( 1991 ). Inventory of Longitudinal Studies in the Social Sciences. SAGE Publications, Inc; 1st edition.

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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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• > The Cambridge Handbook of Research Methods in Clinical Psychology
• > Designing and Managing Longitudinal Studies

Book contents

• The Cambridge Handbook of Research Methods in Clinical Psychology
• Copyright page
• Contributors
• Acknowledgments
• Part I Clinical Psychological Science
• Part II Observational Approaches
• Part III Experimental and Biological Approaches
• Part IV Developmental Psychopathology and Longitudinal Methods
• 15 Studying Psychopathology in Early Life
• 16 Adolescence and Puberty
• 17 Quantitative Genetic Research Strategies for Studying Gene-Environment Interplay in the Development of Child and Adolescent Psychopathology
• 18 Designing and Managing Longitudinal Studies
• 19 Measurement and Comorbidity Models for Longitudinal Data
• Part V Intervention Approaches
• Part VI Intensive Longitudinal Designs
• Part VII General Analytic Considerations

18 - Designing and Managing Longitudinal Studies

from Part IV - Developmental Psychopathology and Longitudinal Methods

Published online by Cambridge University Press:  23 March 2020

This chapter outlines critical design decisions for longitudinal research and provides practical tips for managing such studies. It emphasizes that generative longitudinal studies are driven by conceptual and theoretical insights and describes four foundational design issues including questions about time lags and sample sizes. It then provides advice about how to manage a longitudinal study and reduce attrition. The chapter concludes by considering how the advice offered comports with recent discussions about ways to improve psychological science and providing recommended further reading.

Access options

Recommended reading.

Block’s chapter provides an insider perspective on longitudinal studies of personality and offers nine desiderata for studies.

This paper summarizes the thorny issues involved in selecting time lags for longitudinal studies.

Although it is over 25 years old, this piece provides practical advice and tips for running a large-scale longitudinal study. The advice holds for many less expansive designs as well.

This article offers a wealth of advice about reducing attrition beyond what was covered in this chapter.

Transparency and an awareness of researcher degrees of freedom are helpful for all kinds of psychological research. The guidelines in this article seem especially relevant when analyzing data from existing longitudinal studies.

This work provides an overview of the kinds of choices facing researchers and the checklist may increase awareness of p-hacking and improve the rigor of longitudinal analyses.

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• Designing and Managing Longitudinal Studies
• By M. Brent Donnellan , Deborah A. Kashy
• Edited by Aidan G. C. Wright , University of Pittsburgh , Michael N. Hallquist , Pennsylvania State University
• Book: The Cambridge Handbook of Research Methods in Clinical Psychology
• Online publication: 23 March 2020
• Chapter DOI: https://doi.org/10.1017/9781316995808.022

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What is a longitudinal study?

Last updated

20 February 2023

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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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Some fields in psychology are interested in looking at the long-term effects of certain phenomena. Developmental psychology, for example, focuses on explaining how humans develop over time. For instance, Piaget theorised the four stages of development, but how were these investigated in research? 

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What type of longitudinal study is the following example, a study investigating the effects of prenatal exposure to alcohol and later alcohol dependency?

What type of research collects data on something that has already happened? 

Why do researchers need to record all the data found in longitudinal research?

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A specific research study needs to be applied to test for the changes across time of certain psychological features - longitudinal research.

• This explanation will introduce you to longitudinal research studies.
• Secondly, the explanation will apply longitudinal research to psychology .
• Moving on from this, we will explore how longitudinal research designs
• Then a longitudinal research example will be given.
• Lastly, the strengths and weaknesses of longitudinal research will be summarised.

Longitudinal Research Study

When conducting research, you may want to discover how something develops over several years. Researchers may ask themselves how occurrences in childhood affect the person in adulthood, for example.

Similarly, pharmaceutical companies can be interested in assessing how certain drugs affect people in the long term. These two research concepts can be investigated via longitudinal studies.

Longitudinal research refers to a research method in which individuals are tested over a long period. The period in which participants are tested can range from months to years.

One of the main reasons why longitudinal research is used in psychology is to establish the long-term effects of different phenomena.

In developmental psychology, longitudinal research can support researchers in establishing how a developmental process takes.

Longitudinal research is also widely used when assessing a given therapy type or a specific medication's effects. Generally speaking, longitudinal research is practical when studying changes that occur over time.

Longitudinal Research in Psychology

While the term longitudinal refers to research that investigates processes/phenomena that develop over time, within this form of research method, there are several sub-types.

The different types of longitudinal research used depends on:

• The sample.

Longitudinal Research: Cohort Study

A cohort study is a form of longitudinal research investigating a group of people with common characteristics. Part of the design process of a cohort study involves defining the cohorts that will then be compared.

Researchers may be interested in studying how intelligence quotient may change with age. To study this, they may define three cohorts and compare them.

The cohorts or groups could emerge based on age. For example, cohort one could include those aged 10-20 years, the second cohort could include participants aged 21-40 years and the third cohort could consist of those aged 41-60 years.

There are two forms of longitudinal cohort research: retrospective and prospective studies.

Retrospective studies present a sample of participants who have already been exposed to specific phenomena.

Meaning the process is naturally occurring.

An example of a longitudinal cohort retrospective study is that it could investigate the effects of prenatal exposure to alcohol and later alcohol dependency.

As you can infer from the example, the researchers do not actively manipulate the alcohol consumption of pregnant women. Instead, they would look for participants exposed to alcohol prenatally and measure their current alcohol consumption patterns.

Differently, in prospective studies, participants have not experienced the phenomena or outcome but may be vulnerable in some cases to the variables being studied.

Researchers design and start the study before identifying a clear hypothesis to test. The prospective research design could record outcomes in a group with common characteristics.

The 1970 British Cohort Study is an example of a longitudinal cohort prospective study. The study followed the lives of around 17,500 participants born in the same week in England and Wales.

No clear aim was defined for the study back in 1970, but different hypotheses have been tested throughout the years using the data collected.

Longitudinal Research: Panel Study

A panel study is a form of longitudinal research investigating a group of people over a long time. The sample of the study is also called a panel.

The panel is defined at the beginning of the research process and is followed up for a set amount of time.

Panel studies usually investigate people's beliefs, attitudes and opinion changes across time.

Longitudinal Research Design

Designing a longitudinal study is not particularly different from designing other studies. Let's review the steps in the design of a longitudinal study.

First, researchers identify the phenomena they are interested in. In the case of a longitudinal study, it would be something like establishing the effect that reading difficulty in childhood has on career choice in adulthood.

The research aim and hypothesis will determine the parameters the participants need to fulfil. In the case of the example above, one of the parameters would be that participants had reading difficulties in childhood.

Secondly, researchers decide how and what type of data they will be collecting, which is influenced by what approach the longitudinal research takes:

Prospective research collects information on something the researcher expects to happen.

Retrospective research collects information on something that has already happened.

After this has been identified, the researcher establishes the data collection methods they will use. In addition to how frequently and at what time intervals the data will be collected.

An example of longitudinal research is research investigating the effects of maternal deprivation on later relationships . The hypothetical aim of the study is to identify if the adverse effects of maternal deprivation are long-lasting over time.

The researchers may decide to collect data from questionnaires, interviews, and psychometric tests from all participants every two years over ten years.

To ensure that the research is valid, the researchers need to use the same planned data collection methods and follow the same protocol each time they collect data. They need to record all of the collected data from the research needs.

The final stage of the longitudinal research is analysing and reporting the results.

Longitudinal Research Example in Psychology

A longitudinal research example in psychology is the 1970 British Cohort Study. The study initially started by recruiting over 17,000 babies born in the same week in England, Scotland, Wales, and Northern Ireland. The study collected data from participants of different ages throughout their lives.

The study aimed to identify factors such as policing, individual differences, and mental health and the long-term effects of these across the lifespan. It provided vital information on social mobility, education and opportunities, training, and employment.

The study used a series of methods to collect data, such as:

Face-to-face interviews (including parent interviews).

Self-completion questionnaires.

Cognitive assessments.

Medical examinations.

Psychological tests.

Collecting information concerning educational information.

The study used primary and secondary sources to collect data.

From this research, psychologists can learn about the long-term effects of certain traits, illnesses or experiences. Researchers can also use this to identify what factors should be investigated in experimental conditions.

Suppose a longitudinal cohort study identifies that people living in a certain area have lower IQ scores than others. In that case, further investigation may be done to identify if policies in that region are causing inequality in access to education and achievements.

Longitudinal Research Strengths and Weaknesses

The strengths of longitudinal research are the following:

It allows researchers to identify how time affects a phenomenon, specifically ones that affect important social variables such as the economy, education, and general welfare.

For example, researchers can identify whether the quality of attachments formed with a primary caregiver affects later relationships or determine if pharmacology and cognitive behavioural therapy are effective interventions.

Longitudinal studies are large-scale studies. Researchers can identify many variables that may affect the subject the researcher is interested in. Therefore, longitudinal research provides detailed information about a phenomenon.

Findings from longitudinal factors can help researchers identify what phenomenons need to be tested empirically in experimental conditions to learn more about the causes.

The weaknesses of longitudinal research are:

As it is a time-consuming type of research, it is often quite costly and difficult because it uses multiple methods to collect data.

Researchers need to recruit a large sample when conducting longitudinal research. If not, it is difficult to infer if patterns and findings of results are meaningful, leading to non-generalisable results.

As the research takes over a long period, participants are more likely to drop out. When this happens, it isn't easy to compare the results across the study time points, affecting the reliability and validity of the study.

Longitudinal Research - Key Takeaways

• Longitudinal research is used when researchers want to test the same participants for a long time. This method usually collects data from participants at regular intervals throughout the investigation.
• The importance of longitudinal research in psychology is that it can help researchers see the long-term effects of medication and intervention, learn about the order of events that happen over time, and recognise changes that occur over time.
• They are different types of longitudinal research: a Cohort study and a Panel study.
• The strengths of longitudinal research are there is less likelihood of recall bias affecting the study results. It can also provide detailed information that may not be able to be found in a short time. It also can help researchers identify what they should research and investigate further. It also has economic and social benefits.
• The weaknesses of longitudinal research are that it is time-consuming and expensive, a large sample is needed for the findings to be meaningful, and there is a high chance that participants will drop out.

Flashcards in Longitudinal Research 5

 Longitudinal cohort retrospective study.

What type of longitudinal study is the 1970 British Cohort study?

Retrospective research.

What type of research collects data on something the researcher expects to happen?

Prospective research.

To prevent bias affecting results.

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Frequently Asked Questions about Longitudinal Research

What is the difference between cross-sectional and longitudinal research?

The difference between cross-sectional and longitudinal research is that cross-sectional research investigates different people at a specific time. In contrast, longitudinal research investigates the same participants across time.

Why is longitudinal research important?

The importance of longitudinal research in psychology is that it can help researchers:

• See the long-term effects of things such as medication and intervention.
• Learn about the order of events that happen over time.
• Recognise changes that occur over time. 

What is longitudinal research?

Longitudinal research is a type of research that is used when researchers want to test the same participants for an extended time. This method usually collects data from participants at regular time intervals across this period.

What is longitudinal survey research?

Longitudinal survey research takes place over a long period. The study collects data using surveys at regular time intervals throughout the investigation. 

What is qualitative longitudinal research?

Qualitative longitudinal research is a form of longitudinal research that use qualitative methods such as observations and interviews to collect data. 

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Cohort Study: Definition, Designs & Examples

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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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A cohort study is a type of longitudinal study where a group of individuals (cohort), often sharing a common characteristic or experience, is followed over an extended period of time to study and track outcomes, typically related to specific exposures or interventions.

In cohort studies, the participants must share a common factor or characteristic such as age, demographic, or occupation. A “cohort” is a group of subjects who share a defining characteristic.

Cohort studies are observational, so researchers will follow the subjects without manipulating any variables or interfering with their environment.

This type of study is beneficial for medical researchers, specifically in epidemiology, as scientists can use data from cohort studies to understand potential risk factors or causes of a disease.

Before any appearance of the disease is investigated, medical professionals will identify a cohort, observe the target participants over time, and collect data at regular intervals.

Weeks, months, or years later, depending on the duration of the study design, the researchers will examine any factors that differed between the individuals who developed the condition and those who did not.

They can then determine if an association exists between an exposure and an outcome and even identify disease progression and relative risk.

Retrospective

• A retrospective cohort study is a type of observational research that uses existing past data to identify two groups of individuals—those with the risk factor or exposure (cohort) and without—and follows their outcomes backward in time to determine the relationship.
• In a retrospective study , the subjects have already experienced the outcome of interest or developed the disease before starting the study.
• The researchers then look back in time to identify a cohort of subjects before developing the disease and use existing data, such as medical records, to discover any patterns.

Prospective

A prospective cohort study is a type of longitudinal research where a group of individuals sharing a common characteristic (cohort) is followed over time to observe and measure outcomes, often to investigate the effect of suspected risk factors.

In a prospective study , the investigators will design the study, recruit subjects, and collect baseline data on all subjects before they have developed the outcomes of interest.

• The subjects are followed and observed over a period of time to gather information and record the development of outcomes.

Determine cause-and-effect relationships

Because researchers study groups of people before they develop an illness, they can discover potential cause-and-effect relationships between certain behaviors and the development of a disease.

Provide extensive data

Cohort studies enable researchers to study the causes of disease and identify multiple risk factors associated with a single exposure. These studies can also reveal links between diseases and risk factors.

Enable studies of rare exposures

Cohort studies can be very useful for evaluating the effects and risks of rare diseases or unusual exposures, such as toxic chemicals or adverse effects of drugs.

Can measure a continuously changing relationship between exposure and outcome

Because cohort studies are longitudinal, researchers can study changes in levels of exposure over time and any changes in outcome, providing a deeper understanding of the dynamic relationship between exposure and outcome.

Limitations

Time consuming and expensive.

Cohort studies usually require multiple months or years before researchers are able to identify the causes of a disease or discover significant results. Because of this, they are often more expensive than other types of studies. Retrospective studies, though, tend to be cheaper and quicker than prospective studies as the data already exists.

Require large sample sizes

Cohort studies require large sample sizes in order for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

Prone to bias

Because of the longitudinal nature of these studies, it is common for participants to drop out and not complete the study. The loss of follow-up in cohort studies means researchers are more likely to estimate the effects of an exposure on an outcome incorrectly.

Unable to discover why or how a certain factor is associated with a disease

Cohort studies are used to study cause-and-effect relationships between a disease and an outcome. However, they do not explain why the factors that affect these relationships exist. Experimental studies are required to determine why a certain factor is associated with a particular outcome.

The Framingham Heart Study

Studied the effects of diet, exercise, and medications on the development of hypertensive or arteriosclerotic cardiovascular disease, in a longitudinal population-based cohort.

The Whitehall Study

The initial prospective cohort study examined the association between employment grades and mortality rates of 17139 male civil servants over a period of ten years, beginning in 1967. When the Whitehall Study was conducted, there was no requirement to obtain ethical approval for scientific studies of this kind.

The Nurses’ Health Study

Researched long-term effects of nurses” nutrition, hormones, environment, and work-life on health and disease development.

The British Doctors Study

This was a prospective cohort study that ran from 1951 to 2001, investigating the association between smoking and the incidence of lung cancer.

The Black Women’s Health Study

Gathered information about the causes of health problems that affect Black women.

Millennium Cohort Study

Found evidence to show how various circumstances in the first stages of life can influence later health and development. The study began with an original sample of 18,818 cohort members.

The Danish Cohort Study of Psoriasis and Depression

Studied the association between psoriasis and the onset of depression.

The 1970 British Cohort Study

Followed the lives of around 17,000 people born in England, Scotland, and Wales in a single week of 1970.

Frequently Asked Questions

1. are case-control studies and cohort studies the same.

While both studies are commonly used among medical professionals to study disease, they differ.

Case-control studies are performed on individuals who already have a disease (cases) and compare them with individuals who share similar characteristics but do not have the disease (controls).

In cohort studies, on the other hand, researchers identify a group before any of the subjects have developed the disease. Then after an extended period, they examine any factors that differed between the individuals who developed the condition and those who did not.

2. What is the difference between a cross-sectional study and a cohort study?

Like case-control and cohort studies, cross-sectional studies are also used in epidemiology to identify exposures and outcomes and compare the rates of diseases and symptoms of an exposed group with an unexposed group.

However, cross-sectional studies analyze information about a population at a specific point in time, while cohort studies are carried out over longer periods.

3. What is the difference between cohort and longitudinal studies?

A cohort study is a specific type of longitudinal study. Another type of longitudinal study is called a  panel study  which involves sampling a cross-section of individuals at specific intervals for an extended period.

Panel studies are a type of prospective study, while cohort studies can be either prospective or retrospective.

Barrett D, Noble H. What are cohort studies? Evidence-Based Nursing 2019; 22:95-96.

Kandola, A.A., Osborn, D.P.J., Stubbs, B. et al. Individual and combined associations between cardiorespiratory fitness and grip strength with common mental disorders: a prospective cohort study in the UK Biobank. BMC Med 18, 303 (2020). https://doi.org/10.1186/s12916-020-01782-9

Marmot, M. G., Rose, G., Shipley, M., & Hamilton, P. J. (1978). Employment grade and coronary heart disease in British civil servants. Journal of Epidemiology & Community Health, 32(4), 244-249.

Rosenberg, L., Adams-Campbell, L., & Palmer, J. R. (1995). The Black Women’s Health Study: a follow-up study for causes and preventions of illness. Journal of the American Medical Women’s Association (1972), 50(2), 56-58.

Samer Hammoudeh, Wessam Gadelhaq and Ibrahim Janahi (November 5th 2018). Prospective Cohort Studies in Medical Research, Cohort Studies in Health Sciences, R. Mauricio Barría, IntechOpen, DOI: 10.5772/intechopen.76514. Available from: https://www.intechopen.com/chapters/60939

Setia M. S. (2016). Methodology Series Module 1: Cohort Studies. Indian journal of dermatology, 61(1), 21–25. https://doi.org/10.4103/0019-5154.174011

Zabor, E. C., Kaizer, A. M., & Hobbs, B. P. (2020). Randomized Controlled Trials. Chest, 158(1). https://doi.org/10.1016/j.chest.2020.03.013

Further Information

• Cohort Effect? Definition and Examples
• Barrett, D., & Noble, H. (2019). What are cohort studies?. Evidence-based nursing, 22(4), 95-96.
• The Whitehall Studies
• Euser, A. M., Zoccali, C., Jager, K. J., & Dekker, F. W. (2009). Cohort studies: prospective versus retrospective. Nephron Clinical Practice, 113(3), c214-c217.

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The Seattle Longitudinal Study: Relationship Between Personality and Cognition

This article reviews the history, measures and principal findings of the Seattle Longitudinal Study. This study began in 1956 focusing upon age differences and age changes in cognitive abilities. Its sampling frame is a large HMO in the Pacific Northwest. The study has been expanded to investigate various influences on cognitive aging including, cognitive styles, personality traits, life styles, and family environment. Current interest is also in the early detection of risk for dementia. In addition, this article reports original analyses of the relation of personality dimensions to cognitive abilities (both concurrent and longitudinal). While personality remains relatively stable over the adult life span, modest proportions of variance are shared between various personality traits and the cognitive abilities.

INTRODUCTION

In this article we will provide an overview of the Seattle Longitudinal Study (SLS). Because this study has provided a major model for longitudinal-sequential studies of aging we emphasize the basic conceptual model, the design, and the measures utilized. But we also provide a summary of the major findings thus far obtained, including the major references to more detailed descriptions of these findings. We then turn to the topic of the relationship between personality traits and cognition and report results of analyses of new data collected during our most recent assessment wave as well as findings resulting from the integration of the new data with data previously collected.

The Seattle Longitudinal Study began as the senior author’s doctoral, dissertation at the University of Washington in 1956 { Schaie, 1958 , 2000b ). Results of previous work on the SLS have been widely disseminated in the psychological and gerontological literature. Comprehensive reports of the study can be found in Schaie (1983 [1956–1977]; 1996 [1956–1991], 2004 , [1956–1998]).

In brief, the SLS has charted the course of selected psychometric abilities from young adulthood through old age. It has investigated individual differences and differential patterns of change. In so doing it has focused not only on demonstrating the presence or absence of age-related changes and differences but has attended also to the magnitude and relative importance of the observed phenomena. More recent phases of the study have identified contextual, health, and personality variables that offer explanations for differential change and that provide a scientific basis for possible interventions. We have also studied cognitive similarity within parent-offspring sibling pairs, and have recently begun to acquire data on a third generation (participants who have both a parent and a grand-parent participating in the SLS). Cognitive interventions have been designed that have been successful in remediating statistically reliable declines and in improving the cognitive functions of older persons who have remained stable. Age changes and age differences in cognitive ability structure have also been studied at the latent construct level, the relative effect of speed and accuracy in age decline and training gain has been examined, and the relevance of cognitive training to real life tasks has been investigated. Work in progress seeks to relate our behavioral data to neuropsychological diagnostic procedures, to study behavior-stractural-anatoraical relation-via data collected at autopsy and to investigate the cognitive change correlates of the Apolipoprotein-E marker gene and of serum cholesterol level. A schematic conceptual model for the study is shown in Figure 1 .

Conceptual model for the Seattle Longitudinal Study (from Schaie, 200la).

DESIGN OF THE SLS

The data base.

The data base for the SLS consists of data acquired during our seven major testing cycles (1956, 1963, 1970, 1977, 1984, 1991, 1998; see Fig. 2 ). The 1984, 1991 and 1998 data collections also included cognitive training follow-up studies ( Schaie, 1996 ; Schaie & Willis, 1986b ; Willis & Schaie, 1994 ). In addition there were four collateral studies concerned with issues of life complexity (1974; Gribbin, Schaie, & Parham, 1980 ), shifting to an expanded sampling frame (1974; Schaie, 1996 ) dealing with the “aging” of the test battery (1975; Schaie, 1996 ) and the family similarity study (1989–90 and 1996–97; Schaie, Plomin, Willis, Gruber-Baldini, & Dutta, 1992 ; Schaie, Plomin, Willis, Gruber-Baldini, Dutta, & Bayen, 1993 ; Schaie & Willis, 1995 ). Current data collections involve the neuropsychological testing of participants aged 60 or older, Apolipoprotein-E testing, and autopsy studies, as well as follow-up data collections on participants in the family study.

Basic Design of the Seattle Longitudinal Study (SLS).

All of our study participants are or were originally members of an HMO, the Group Health Cooperative of Puget Sound, in the Seattle, Washington metropolitan area, or are family members of these individuals. The initial sampling frame in 1956 consisted of approximately 18,000 potential adult participants. These were stratified by age and sex, with 25 men and 25 women randomly selected for each year of birth from 1889 to 1939. Testing proceeded in small groups from ten to thirty persons until a total of 500 persons (25 men and 25 women in each 5-year age interval from 21 to 70 years) had been, tested (cf. Schaie, 1958 ).

In the 1963 cycle, 301 members of the original sample were retested. In addition, approximately 3000 new names were drawn randomly from the original, sampling frame, deleting those individuals who had been tested in 1956. In the second wave of the study 996 persons aged 22–77 years were ( Schaie & Strother, 1968 ). A similar procedure was followed in 1970; retesting as many survivors as possible from the first two cycles, and initially examining a new randomly selected panel of 705 persons, aged 22–84 years ( Schaie & Labouvie-Vief, 1974 ). Since the original sampling frame had been substantially depleted, a collateral study determined that it would be feasible to shift to a sampling with replacement (1974; Schaie, 1996 ). Thus, in the 1977 cycle, in addition to retesting survivors of the first three waves, we sampled 3000 persons from what had by then become a 210,000 member organization. During the fourth wave 612 new participants (aged 22–84) were tested ( Schaie, 1983 ). During the 1984 cycle retests were conducted for 839 surviving participants of the first four waves as well as a subset of 160 participants of the 1974–75 collateral studies. Again a random sample was drawn from the now 300,000 member health plan and 628 new participants were tested ( Schaie, 1988 , 1996 ). In the 1991 cycle 1117 surviving participants were reassessed and 693 new participants were tested from a new random sampling of the plan that now exceeds 400,000 members ( Schaie, 1996 ). Finally, in the 1998 cycle we reassessed 1051 participants and added a new random sample of 724 participants.

The response rates of potential participants have ranged from 16% for the 1956 data collection to 11% for the most recent data collection, with best response for the middle aged and worst response for young adult males. However, the demographic characteristics of our sample do not differ significantly from their parent population.

Although the successive random draws from our sampling frame have been quite representative of the population described above, there has clearly been non-random attrition. We have studied the effects of attrition systematically, have found, as is true in other studies (e.g., Palmore, Busse, Maddox. Nowlin, & Siegler, 1985 ; Rott, 1993 ; Sliwinski & Buschke, 1999 ), that those who return for retest outperform those who do not return. However, we have also noted that these effects do not seem to be systematically related to the age of the participants, although reasons for drop-out may change across the age span. Furthermore, dropout effects are of greatest magnitude subsequent to the first retest occasion, but lessen progressively the longer participants remain in the study. Less than 5% of our participants were lost through refusal to return. We have reported attrition effects for each of our study cycles, and have proposed a number of corrections that correct for the effects of attrition and other confounds from our estimates of cognitive age changes (cf. Schaie, 1996 ).

The main SLS database currently consists of the 9,476 complete records on 4,857 participants, of whom. 36 were tested seven times, 122 were six times, 223 were tested five times, 281 four times, 527 were tested three times, 1,004 were tested twice, and 2,664 participants were tested only once. Cumulatively this results in a total of 2,193 participants followed over 7 years, 1,189 over 14 years, 662 over 21 years, 381 over 28 years, 158 over 35 years, and 36 over 42 years. Five hundred and eighty participants of the training study received an additional test administration after a one-month interval (also see Schaie, 1996 , 2004 ).

The family study archive contains 776 adult offspring of longitudinal participants (465 daughters and 311 sons). It also contains 400 siblings of the longitudinal participants (248 sisters and 152 brothers). These participants were first tested with the basic test battery in 1989–90. Of these participants, 886 persons were successfully retested in 1996–97 (385 daughters, 229 sons; 177 sisters, 95 brothers). In addition, 672 relatives not previously tested were added to the archives during 1996–97 (239 daughters, 134 sons; 162 sisters, 107 brothers).

THE MEASUREMENT VARIABLES

During all seven cycles of the SLS, our principal dependent variables were the measures of Verbal Meaning, Space, Reasoning, Number and Word Fluency, identified by Thurstone as accounting for the major proportion of variance in the abilities domain in children and adolescents ( Thurstone, 1938 ) contained in the 1948 version of the Thurstone’s SRA Primary Mental Abilities Test (Form AM 11–17; Schaie, 1985 ; Thurstone & Thurstone, 1949 ). The second set of variables that has been collected consistently includes the rigidity-flexibility measures from, the Test of Behavioral Rigidity ( Schaie & Parham, 1975 ), which also include a modified version of the Gough social responsibility scale ( Gough, McCloskey, & Meehl, 1952 ). Limited demographic were collected during the first three cycles. The above measures are referred to as the “Basic Test Battery,” and have been supplemented since 1974 with a more complete personal data inventory, the Life Complexity Inventory (LCI; Gribbin et al., 1980 ), which includes topics such as major work circumstances (with home-making defined as a job), friends and social interactions, daily activities, travel experiences, physical environment and life-long educational pursuits. The battery was expanded in 1991 by adding the Moos Family Environment and Work Scales ( Moos, 1981 , 1986 ; Schaie & Willis, 1995 ), and a family contact scale. A Health Behavior Questionnaire ( Maier, 1995 ; Maitland, 1997 ) was in 1993.

In the 1975 collateral study ( Schaie, 1996 ) a number of measures from the ETS kit of factor-referenced tests ( Ekstrom, French, Harman, & Derman, 1976 ) as well as the 1962 revision of the PMA ( Thurstone, 1962 ) were added. Of these the Identical Picture, Finding A’s and Hidden Pattern tests ( Ekstrom et al., 1976 ) were included in the fourth (1977) SLS cycle.

To be able to explore age changes and differences in factor structure, we included multiple markers for most abilities during the fifth (1984) cycle. We also added measures of Verbal Memory ( Zelinski, Gilewski, & Schaie, 1993 ). This now permits us to measure the primary abilities of Verbal Comprehension, Spatial. Orientation, Inductive Reasoning, Numerical Facility, Perceptual. Speed and Verbal Memory at the latent construct level (cf. Schaie, Dutta, & Willis, 1991 ; Schaie, Maitland, Willis, & Intrieri, 1998 ). The expanded cognitive battery is described referenced in Table 1 . Also added were a criterion measure of “real life tasks,” the ETS Basic Skills test ( Educational Testing Service, 1977 ), and a scale for measuring participants’ subjective assessment of ability changes between test cycles ( Schaie, Willis, & O’Hanlon, 1994 ). Beginning in 1997 we substituted the Everyday Problems Test (EPT, Diehl, Willis, & Schaie, 1995 ; Willis, 1992 , for the Basic Skills test, since the more recent test was specifically constructed for work with adults and has been related to measures of the Instrumental Activities of Daily Living (IADL; Lawton & Brody, 1969 ).

Psychometric Intelligence Measurement Battery.

We have abstracted the health histories of most of our participants who were retested in 1991 and/or 1998, and who have been tested two or more times, over the entire period they have been in the study using the International Classification of Diseases (U.S. Public Health Service, 1968 ), coding each outpatient visit or hospital day by diagnosis and by constructing annual illness counts by illness incidents (single visits) and illness episodes (continuous series of visits for a specified diagnosis). Records of drugs used concurrently with the psychological testing were also obtained and coded ( American Society of Hospital Pharmacists, 1985 ). These data have been used to study the relation of health, cognition and mortality (e.g., Bosworth & Schaie, 1997 ; Bosworth, Schaie, & Willis, 1999 ).

SUMMARY OF RESULTS OF PREVIOUS WORK ON THE SLS

Throughout the history of the SLS, an effort now extending over 48 years, we have focused on five major questions with regard to the nature and antecedents of cognitive decline across adulthood, which we have attempted to ask with greater clarity and increasingly sophisticated methodologies at each successive stage of the study:

• Does intelligence change uniformly through adulthood or are there different life course ability patterns?
• At what age are there reliably detectable age decrements in ability and what is the magnitude of these decrements?
• What are the patterns of generational differences and what is the magnitude of these differences?
• What accounts for individual differences in age-related change in cognitive abilities in adulthood?
• Can intellectual decline with increasing age be reversed by educational interventions?

More recently we have considered three other questions. The first concerns family similarity in level and rate of change of cognitive functioning. The second begins to bridge behavioral science biology by inquiring into structural anatomical and physiological changes that correlate with behavior. The third topic concerns the bridge between the study of normal aging and the precursors of dementia. This summary will review what we have learned from the SLS up to now to answer the five basic questions as well as provide some information on the family studies.

Does Intelligence Change Uniformly Through Adulthood or Are There Different Life Course Ability Patterns?

Our studies have shown that there is no uniform pattern of age-related changes across all intellectual abilities, and that studies of an overall index of intellectual ability (IQ) therefore do not suffice to monitor age changes and age differences in intellectual functioning for either individuals or groups. Our data do lend some support to the notion that fluid abilities tend to decline earlier crystallized abilities. There are, however, important ability by age and ability by cohort interactions that complicate matters. In our most recent (cycles 4–7) cross-sectional sequences, gender difference trends emerge that suggest that women, may decline earlier on fluid abilities, while men do so on the crystallized abilities. Moreover, while fluid abilities begin to decline earlier, crystallized abilities show steeper decrement once the late seventies are reached (cf. Schaie, 1996 , 2004 ). With respect to perceptual speed, age changes begin in young adulthood, and show a virtually linear decrement trend (Schaie, 1989). Figure 3 provides a graphic view of our latest estimates of the longitudinal age changes on the six latent ability constructs.

Longitudinal estimates of within participant age changes on the latent ability constructs (from 7-year longitudinal data).

While cohort-related differences in the rate magnitude of age changes in intelligence remained fairly linear for cohorts entering old age during the first three cycles in our study, they have since shown substantial shifts. For example, of decremental age change have abated, while at the same time negative cohort trends are appearing as we begin to study members of the baby-boom generation. Patterns of socialization unique to a given sex role within a specific historical period may be a major determinant for the pattern of change in abilities. More finegrained analyses suggest that there may be substantial gender differences for those who remain stable as well as differential changes for those who decline, when age changes are decomposed into those due to accuracy or speed (cf. Willis & Schaie, 1988 ). We have also demonstrated substantial relationships between the psychometric abilities and real life tasks ( Willis & Schaie, 1986 ).

With multiple markers of abilities first available for the fifth cycle, we have conducted cross-sectional and longitudinal analyses of ability structure invariance over a wide age range ( Maitland, Intrieri, Schaie, & Willis, 2000 ; Schaie et al., 1998 ). Two types of invariance may be distinguished. The first, configured invariance, implies that the same measurement variables load on the same factors across time or different groups. This is a minimal requirement that assures that the constructs of interest remain stable over time. Metric invariance implies the additional requirement that the factor loadings retain the same magnitude across time or groups. In this case we can be assured that the measurement metric remains constant. Our results suggest that it is possible to demonstrate configural but not metric factor invariance across a wide age/cohort range, but that metric invariance within cohorts over seven years prevails at all but the oldest ages.

At What Age is There a Reliably Detectable Age Decrement in Ability and What is the Magnitude of that Decrement?

Data collected during the first three cycles of the SLS suggested that average age decrements in psychometric abilities could not be demonstrated prior to age 60, but that such reliable decrement may be found for all abilities by age 74. Analyses from the most recent three cycles, however, suggest that small but statistically significant average decrement for some abilities can be found for some, but not all, cohorts in the 50s ( Schaie, 1996 , in press). Analyses of individual differences, however, demonstrate that even at age 81 less than half of all observed individuals experienced reliable decremental change on a particular ability over the preceding seven years ( Schaie, 1984 ). In addition, average decrement before age 60 amounts to less than two-tenths of a standard deviation, while by age 81 average decrement rises to approximately one standard deviation for most variables ( Schaie, 1984 , 1996 ). The magnitude of decrement, moreover, is significantly reduced, when the effects of age changes in perceptual speed are removed (Schaie, 1989).

The data from the SLS have attained increasing importance in providing a normative base to determine at what ages declines reach practically significant levels of importance for public policy issues related to mandatory retirement, age discrimination in employment or for cases of population proportions that can live independently in the community. From the SLS data we were able to show both level of performance and rate of decline show significant age by cohort interactions ( Schaie, 1983 , 1996 , 2000a ).

What are the Patterns of Generational Differences and What is Their Magnitude?

Results from the SLS have conclusively demonstrated the prevalence of substantial generational (cohort) differences in psychometric abilities ( Schaie, 1983 , 1996 ; Willis, 1989 ). These cohort trends differ in magnitude and direction by ability and can therefore not be determined from composite IQ indices. There has been an almost linear positive cohort shift for Inductive Reasoning, with more spasmodic positive shifts for Verbal Meaning and Spatial Orientation. On the other hand a curvilinear cohort pattern has been found for Number skills reaching a peak with the 1924 birth cohort and negative slope thereafter. Cohorts born more recently are also at a disadvantage when compared with prior cohorts on the variable of Word Fluency. It can be concluded from these findings that cross-sectional studies overestimate age changes prior to the 60 s for those variables that show negative cohort gradients and underestimate age changes for those variables with positive cohort gradients (e.g., for perceptual speed; Schaie, 1989). The negative cohort trends observed on SAT scores have reappeared in our study as baby boomers entered adulthood. However, these trends, extending to the variables we monitor in adulthood, are confounded with period effects, suggesting somewhat lower performance over time for a fairly wide age range. Figure 4 provides a graphic view of cumulative cohort differences for the five mental ability measures used throughout the study from the cohort born in 1889 to that born in 1973.

Cross-sectional differences on five ability measures for cohorts born from 1889 to 1973.

What Accounts for Individual Differences in Age-Related Change in Adulthood?

The most powerful and unique contribution of a longitudinal study of adult development is made due to fact that only longitudinal, data permit investigation of individual differences in antecedent variables that lead to early decrement for some persons and maintenance of high levels of functioning for others well into very advanced age. Previous results from the SLS have implicated a number of factors that account for these individual differences, some of which have been shown to be amenable to experimental intervention. The variables most intensively studied thus far that have been implicated in reducing risk of cognitive decline in old age have included: (a) Absence of cardiovascular and other chronic diseases ( Bosworth & Schaie, 1997 ; Hertzog, Schaie, & Gribbin, 1978 ); (b) favorable environment mediated by high SES ( Gribbin, Schaie, & Parham, 1980 ; Schaie, 1984 ); (c) involvement in a complex and intellectually stimulating environment ( Gribbin et al., 1980 ; O’Hanlon, 1993 ; Schaie, 1984 , 1996 ; Schaie & O’Hanlon, 1990 ); (d) flexible personality style at midlife ( Schaie, 1984 , 1996 ); (e) high cognitive status of spouse ( Gruber-Baldini, Schaie, & Willis, 1995 ); and (f) maintenance of high levels of perceptual processing speed (Schaie, 1989).

Can Intellectual Decline with Increasing Age be Reversed by Educational Intervention?

We have also been able to carry out interventions designed to remediate known intellectual decline, as well as to reduce cohort differences in individuals who have remained stable in their own performance over time but who have become disadvantaged when compared to younger peers. The cognitive training studies conducted with our longitudinal participants suggested that observed decline in many community dwelling older people is likely to be a function of disuse and is therefore reversible for many. Indeed, approximately two thirds of the experimental participants showed significant improvement, and about 40% of those who had declined significantly over 14 years were returned to their pre-decline level ( Schaie & Willis, 1986 ; Willis & Schaie, 1986b ). Training effects are long-lasting with the trained participants still at an advantage over their controls after 7 and 14 years ( Schaie, 1996 , 2004 ; Willis & Schaie, 1994 ).

What is the Degree of Stability of Family Similarity Over Age and Time and What is the Magnitude of Within-Family Cohort Differences?

In 1990–91 we were able to assess 776 adult offspring and 400 siblings of SLS participants. We found that family members shared approximately 25% of the variance for virtually all mental abilities and measures of flexibility. The similarities were found for parents and their offspring (adult children) and for siblings (brothers and sisters). The two exceptions to this finding were for the attitude measure of Social Responsibility and for a measure of perceptual speed; neither of which seems to display inherited characteristics. The magnitude of parent-offspring and sibling similarity differed for specific abilities, the overall similarity was somewhat greater for parent-offspring pairs. The size of the correlations among family members were also comparable to those found between young adults and their children in other studies (cf. DeFries, Vandenberg, & McClearn, 1976 ). Generational differences within families were similar in magnitude to those reported earlier for unrelated individuals ( Schaie, Plomin, Willis, Gruber-Baldini, & Dutta, 1992 ; Schaie, Plomin, Willis, Gruber-Baldini, Dutta, & Bayen, 1993 ). In 1996–97 we were able to complete a follow-up on 669 adult offspring and 334 siblings. An additional 466 offspring and 334 siblings were also added (cf. Schaie, 2004 ).

A revision of the Moos Family Environment and Work Environment scale was constructed. Separate forms were constructed to survey family environment in the family of origin and the current family. Psychometric analyses of these forms have demonstrated retention of construct validity for our revised forms, equivalence of structure across the family of origin and current family versions, as well as comparability across different age levels throughout adulthood. Substantial changes in perception of family environments over time was found to occur in both parent and adult offspring, with greater similarity found between the parent’s current family and offspring’s family of origin (same family) than between current families or families of origin for either generation ( Schaie & Willis, 1995 ).

What is the Relation Between Cognition, Health and Mortality?

We have revisited the phenomenon of terminal decline, and have shown that lower levels of cognitive functioning and decline in crystallized abilities seven to fourteen years prior to death are important predictors of mortality ( Bosworth, Schaie, & Willis, 1999 ; Bosworth, Schaie, Willis, & Siegler, 1999 ). Cluster analyses have identified five distinct patterns in which cognitive decline chronic disease predict mortality ( Bosworth & Schaie, 1999 ).

The effects of structural and functional social support as well as age and previous health status on health outcomes was examined ( Bosworth & Schaie, 1997 ). The latent outcome variable is marked by number of physician visits, hospital stays, and number of illness diagnoses over a 7 year period. Structural social support is marked by demographic variables and functional social support is measured by the Moos family environment scales ( Moos, 1986 ). Results of this study suggest that social support variables account for only very small amounts of individual differences variance (primarily in those individuals with greatest disease incidence), while previous health status accounts for substantial, variance.

Recent Findings

Changes in rate of aging.

We now have available 7-year longitudinal change data for parents and adult offspring assessed at approximately the same ages. These data have allowed us to supplement the findings on generational differences in level of performance to address the question of whether rates of aging have changes. The findings suggest that during young old age, the slopes of decline for several of the primary mental abilities have significantly flattened. This is the case for Verbal Meaning, Inductive Reasoning, Spatial Orientation, and Psychomotor Speed. ( Schaie, 2004 ).

Early Detection of Risk for Dementia

Data on the CERAD neuro-psychology measures have been accumulated for over 500 study participants over the age of 60. From these data individuals were rated by neuro-psychologists as normal, to be monitored, probably demented, or demented. Using the methods of extension analysis (describe above) neuro-psychology measures were estimated from psychometric tests collected seven and fourteen years previously. We found that a significant proportion of study participants who were eventually diagnosed as demented could have been predicted from data collected 7 and 14 years earlier, (cf. Schaie, Caskie, et al., in press).

In summary, the Seattle Longitudinal Study has provided a model for longitudinal-sequential studies of cognition over the adult life course. It introduced the concept of cohort into cognitive aging research, and it has pioneered family studies of cognitive aging. It has substantively contributed to our understanding of the varied indigenous and exogenous influences on cognitive aging, including health, life styles, and personality characteristics. Most recently it has expanded to studies of early identification of risk for dementia and policy relevant studies of generational differences in rates of cognitive aging.

THE RELATION OF PERSONALITY AND COGNITION

Throughout the SLS we have collected limited personality data that have been derived from, the questionnaire part of the Test of Behavioral Rigidity (TBR; Schaie & Parham, 1975 ). Most recently we have also collected data on the NEO Personality Inventory ( Costa & McCrae, 1992 ). In this section we report new analyses that speak to the relationship between personality traits and performance on tests of cognitive abilities.

Participants

Included in these analyses are three different subsets from the SLS. The first set used for the analyses of concurrent relationships between personality factors and cognitive abilities consists of the 1,761 participants who were assessed during the SLS seventh data collection and who had both personality and cognitive ability scores. The second set used for the longitudinal analyses consists of the 1,055 participants who have personality factor and cognitive scores in both 1991 and 1998. Of these participants 667 have scores also in 1984, 419 in 1977, 285 in 1970 and 157 in 1963. The third set consists of 1,501 participants who completed the NEO and who have 1998 cognitive ability scores.

The measures include the cognitive ability scores described above (see Table 1 ), the cognitive factor scores, and the five NEO factor scores.

Personality Factor Scores

A factor analysis was conducted on the 75 questionnaire items in the Test of Behavioral Rigidity (TBR; Schaie & Parham, 1975 ) using 4326 test records accumulated over the 1963–1984 study cycles. Initial analyses considering the number of factors unambiguously represented in the data resulted in an acceptable 13-factor model with good fit (χ 2 [ df = 1,191] = 3,548.16, p <.00l; GFI = .945, RMSR = .007). The 13-factor model was then tested by means of confirmatory factor analyses on the participants assessed in 1977 and 1984, and it continued to show an acceptable fit (χ 2 [ df = 1,191] = 4,302.98, p <.001; GFI = .941, RMSR = .007). A two-group analysis further investigated factorial invariance across time by constraining factor loadings and factor variance–covariance matrices to be equal across the two data sets. This analysis also yielded an acceptable fit (χ 2 [ df = 2,512] = 6,910.00, p <.00l; GFI = .945, RMSR = .007). ( Maitland, Dutta, Schaie, & Willis, 1992 ).

The 13-factor model includes 8 factors that can be mapped upon the Cattell (1957 ; Cattell, Eber, & Tatsuoka, 1970 ) taxonomy of personality dimensions: Affectothymia, Superego Strength, Threctia, Premsia, Untroubled Adequacy, Conservatism of Temperament, Group Dependency, and Low Self-Sentiment. The remaining five factors are best described as attitudinal traits and were labeled Honesty, Interest in Science, Inflexibility, Political Concern, and Community Involvement.

The factors that were mapped upon one end of the trait continuum described by Cattell, have been described as follows ( Cattell, Eber, & Tatsuoka, 1970 ):

Affectothymia – Outgoing, warmhearted, easygoing, participating tendencies.

Superego Strength – Conscientious, persistent, moralistic, staid.

Threctia — Shy, timid, restrained, threat-sensitive.

Premsia – Tender-minded, sensitive, clinging over-protected.

Untroubled Adequacy – Self-assured, placid, secure, complacent, serene.

Conservatism – Respecting traditional ideas, tolerant of traditional difficulties.

Group Dependency – A “joiner” and sound follower, group adherence.

Low Self-Sentiment – Uncontrolled, lax, follows own urges, careless of social rules.

The additional five attitudinal traits may be described as follows:

Honesty – Endorsement of items that reflect personal beliefs of honesty

Interest in science – Endorsement of an item couplet that reflects interest in science

Inflexibility — Endorsement of items that reflect lack of tolerance for disruption of routines

Political Concern – Reflects attitudes toward other countries

Community Involvement — Endorsement of positive attitudes about citizenship and civic responsibilities.

The NEO Personality Inventory

The scales in this inventory ( Costa & McCrae, 1992 ) are described as follows:

Neuroticism (N)

This scale contrasts adjustment or emotional stability with maladjustment or neuroticism.

Extraversion (E)

Extraverts are sociable but also assertive, active and talkative. Introverts are reserved, independent and they prefer to be alone.

Openness (O)

Open individuals are curious, willing to entertain novel ideas and unconventional, values. They experience positive and negative emotions more intensely than do closed individuals.

Agreeableness (A)

Agreeable persons are altruistic, sympathetic to other and eager to help, expecting others to be equally helpful in return. Disagreeable persons are egocentric, sceptical of others’ intentions and competitive rather than cooperative.

Conscientiousness (C)

High scorers are scrupulous, punctual and reliable. Low scores do not necessarily lack moral principles, but are less exacting in a-plying them, more hedonistic, and more lackadaisical in working towards their goals.

The TBR Questionnaire (from which the 13 personality factor scores are derived) was administered either as part of the cognitive group testing sessions or as part of a take-home package. The NEO was administered as a mail survey.

Extension Analysis

In order to permit postdiction of past standing on the NEO we conducted an extension analysis that was designed to project the NEO scores into the TBR 13 factor space. An important application of confirmatory factor analysis is to use this procedure to implement the Dwyer (1937) extension method. As Tucker (1971) has demonstrated, it is not necessarily optimal to use factor scores on a latent variable to estimate their regression on an observed variable. However, confirmatory factor analysis permits the estimation of the location of some new observed variable or variables of interest within a previously known factor (latent construct) space. This is a situation that frequently arises in aging studies as samples are followed over long lime periods.

In the extension analysis, the 75 × 75 TBR personality item correlation matrix for the 1998 sample was augmented by the 5 NEO scores converted into z -score metric. The factor loadings for the 13 TBR factors were constrained to the values obtained from the original confirmatory factor analysis solution for the 13 personality factors for this sample. Factor loadings for the NEO scales were then freely estimated providing information on the projection of these measures into the previously established 13 personality factor space. This procedure produced an acceptable fit for the extended model (χ 2 [ df = 1,453] =5924.22, p <.001; GFI = 920, RMSR = .04).

Projections of the NEO into the 13 factor space have a + sign if a NEO scale is positively correlated with the personality factor or a − sign if it is negatively correlated.

Significant projections of the NEO ( p < .01) into the 13 factor space were found for Neuroticism with Affectothymia (+), Superego Strength (−), Untroubled Adequacy (−), Conservatism (+), Inflexibility (+), and Community Interest (−). Extraversion projected significantly to Superego Strength (+), Premsia (+), Untroubled Adequacy (+), Conservatism (−), Low Self Esteem (−), and Community Interest (+). Openness projected to Premsia (+), Low Self Esteem (−) and Inflexibility (−). Agreeableness projected to Superego Strength (+) Threctia (+), Premsia (+), Untroubled Adequacy (+), Low Self Esteem (−), Honesty (+) and Community Interest (+). Finally, Conscientiousness projected to Affectothymia (−), Superego Strength (+), Untroubled Adequacy (+), Conservatism (−). Low Self Esteem (−), Inflexibility (−), and community Interest (+). Table 2 gives the concurrent correlations between the NEO scales and the 13 personality factors. Table 3 provides standardized factor loadings for the projection of the NEO into the 1.3 personality factor space.

Concurrent Correlations of Personality Factor Scores and NEO Scales ( N = 1417).

Standardized Factor Loadings for the NEO on the 13 Personality Factors.

Concurrent Relationships

We first examined concurrent relationships between the TBR personality factors and the NEO with the measures of the six latent ability constructs.

TBR Personality Factors

Stability of personality factor scores.

As an initial step we conducted an analysis of the stability of the thirteen personality factors over time. Included in this analysis were the 1055 participants who had retest data over at least seven years. All stability coefficients were statistically significant ( p < .01). The seven-year stabilities ranged from 0.32 for Affectothymia to 0.71 for Group Dependency. Fourteen-year stability ranged from 0.33 for Affectothymia to 0.69 for Interest in Science. Twenty-one year stabilities ranged from 0.20 for Affectothymia to 0.67 for Group Dependency. Twenty-eight year stabilities ranged from 0.24 for Political Concern to 0.65 for Interest in Science. And the 35 year stabilities ranged from 0.29 for Honesty to 0.66 for Group Dependency. Average stability coefficients were 0.59 over 7 years; 0.54 over 14 years; 0.49 over 21 years; 0.46 over 28 years; and 0.45 over 35 years (see Table 4 ).

Stability of the Personality Factor Scores.

Note. All stability coefficients are statistically significant at p < .01.

We also examined the 7-year stabilities separately for four age groups: Young adult (age 29–49; N = 182): middle-aged (age 50–63; N = 276); young–old (age 64–77; N = 379); and old–old (age 78+; N = 182). All ages are given for the second measurement occasion. Average stability coefficients over seven years were 0.54 for the young adults, 0.58 for the middle-aged, 0.60 for the young–old, and 0.57 for the old–old. These stabilities range from somewhat lower to comparable values frequently seen in the personality literature (cf. Roberts & DelVecchio, 2000 ).

Concurrent relation between personality and ability factors

The concurrent correlations are provided in Table 5 . Correlations range from small to modest. Consistently highest relationships for all abilities occurred with Conservatism (−), Untroubled Adequacy (+), and Group Dependency (−). Additional correlations significant at or beyond the .001 level of confidence were found for Inductive Reasoning with Affectothymia (+), Threctia (−), Premsia (+), Low Self Esteem (−), Honesty (+), Interest in Science (+), Inflexibility (−), Political Concern (+), and Community Interest (−); for Spatial Orientation with Affectothymia (+), Threctia (−), Premsia (+), Low Self Esteem (−), Interest in Science (+), Inflexibility (−), and Community Interest (−); for Perceptual Speed with Affectothymia (+), Premsia (+), Low Self Esteem (−), Interest in Science (+), Inflexibility (−), Political Concern (+), and Community Interest (−); for Verbal Comprehension with Affectothymia (+), Superego Strength (+), Premsia (+), Low Self Esteem (−), Interest in Science (+), Inflexibility (−), Political Concern (+), and Community Interest (+); and for Verbal Memory with Affectothymia (+), Premsia (−), Inflexibility (−), Political Concern (+), and Community Interest (−).

Concurrent Correlations of Personality Factor Scores and Cognitive Abilities.

We also computed OLS regressions of the ability factor scores on the personality factor scores (see Table 6 ). Multiple Rs range from 0.27 for Numeric Facility to 0.49 for Verbal Comprehension. Proportions of variance accounted for by personality in the ability factors are approximately 20% for Inductive Reasoning, 11% for Spatial. Orientation, 18% for Perceptual Speed, 7% for Numeric Facility, 24% for Verbal Comprehension, and 14% for Verbal Memory.

Concurrent OLS Regression of Personality Factor Scores on Cognitive Abilities.

Concurrent correlations were computed also between the five scales of the NEO personality inventory and our six ability factors. These correlations are provided in Table 7 . Again, correlations range from small to modest. Consistently highest relationships for all abilities (except Numeric Facility) occurred with Openness. Additional correlations significant at or beyond the .001 level of confidence were found for Inductive Reasoning with Extraversion (+) and Agreeableness (−); for Spatial Orientation with Agreeableness (−); for Perceptual Speed with Extraversion (+); for Numeric Facility with Extraversion (+) and Conscientiousness (+); and for Verbal Memory with Extraversion (+).

Concurrent Correlations of NEO Scores and Cognitive Abilities.

OLS regressions of the ability factor scores on the NEO scales are shown, in Table 8 . Multiple R s range from 0.12 for Numeric Facility to 0.40 for Verbal Comprehension. Proportions of variance accounted for by the NEO personality factors in the ability factors are. approximately 10% for Inductive Reasoning, 6% for Spatial Orientation, 12% for Perceptual Speed, 2% for Numeric Facility, 16% for Verbal Comprehension, and 12% for Verbal Memory.

OLS Regressions of Cognitive Abilities on NEO Scores.

Longitudinal Relationships

We next examined the longitudinal relationship between personality factors and current cognitive performance. The assumption here is that personality is relatively stable and that one would therefore expect a long-term effect on cognition. We first examine this hypothesis using personality predictors that precede the current cognitive performance by 7, 14, 21, 28 and 35 years. We then present the results of extending the NEO into the thirteen personality factor space and estimate (postdict) past NEO scores from the thirteen personality factors.

OLS regressions of the ability factor scores obtained in 1998 on each of the personality factor scores were competed using personality factor scores obtained in 1963, 1970, 1977, 1984, and 1991. The pattern of statistically significant predictors remained fairly constant across increasing time intervals, although the p levels declined with shrinking sample sizes. Group dependency (−) was the strongest personality predictors for most abilities, followed by Conservatism (−). Untroubled adequacy (+), Premsia (+), and Low Self Esteem (−). Table 9 reports regression coefficients and proportions of variance accounted for in the 1998 cognitive ability factors by earlier standing on the 13 personality factors. The proportions did not vary markedly across the increasing length of the prediction interval. Average proportion of cognitive ability variance predicted was 15.8% over 7 years, 13.6% over 14 years, 13.0% over 21 years, 15.5% over 28 years, and 14.8% over 35 years. The predictability was highest for Verbal Comprehension (20–37%) and lowest for Spatial Orientation (7–13%) and Numeric Facility (6–15%).

Predictive OLS Regression of Personality Factor Scores on Cognitive Abilities.

Postdicting the NEO

Our final analyses are concerned with obtaining postdicted NEO scores by bootstrapping via the 13 personality factor extension analysis, thereby obtaining an estimated longitudinal NEO data set for study participants in 1991 and 1998. Estimation procedures involved computing NEO scores by multiplying the 13 factor scores by weights obtained from the orthonormalized factor loadings in Table 3 and restandardizing resulting scores to a mean of 50 and SD of 10. Within participant changes over 7 years were then computed and aggregated across successive 7-year age cohorts. The resulting longitudinal age gradients were then centered on average scores at age 53 and are depicted in Figure 5 . Considerable caution is in order in interpreting the findings using these NEO proxy estimates. However, they do represent within subject change data across much of the adult life span. While cross-sectional data usually depict few personality differences adulthood, these data suggest much more dramatic developmental trends. For neuroticism we see a sharp increase until midlife with virtual stability thereafter. Openness shows a modest until age 46, a plateau until the late sixties and decline thereafter. Extraversion shows steady decline from the forties, Agreeableness shows steep increment with age, finally, Conscientiousness declines until the fifties followed by a virtual plateau.

Longitudinal estimates of within participant age changes on the NEO scales (from predicted 7-year longitudinal data).

There have been many suggestions that the study of cognition and aging might be advanced by introducing personality constructs as possible covariates that might explain some proportion of age-related changes and differences in cognitive performance. In the earlier literature (cf. Mischell, 1973) it was often argued that most measures of personality traits were not sufficiently stable nor showed, high enough correlations with cognitive measures to make it likely they could account for substantial proportions of age-related variance. The former criticism has largely been addressed by more recent rigorous measurement development (e.g., Costa & MacCrae, 1992 ), but the latter concern requires further empirical investigations. In this article, we try to lay the necessary groundwork for studying these questions by examining the relationship between two personality trait measures and measures of cognitive performance in a large sample covering the adult age range.

Interestingly enough we can show that there are modest but significant concurrent relationships between personality trait measures and ability construct that account for up to 20% of shared variance. Both our 13 personality factor measures and the NEO could be related to the cognitive ability constructs, albeit the 13-factor measure accounted for more of the shared variance than did the NEO. The personality dimensions that were found to be most substantively related to high performance on cognitive ability factors were high Untroubled Adequacy, low Conservatism and low Group Dependency from the 13 PF measure, and high scores of Openness on the NEO.

We were also able to show that there is moderate stability across time for the personality measures that is fairly comparable with the stability found in much of the personality literature (cf. Roberts & DelVecchio, 2000 ). It might be argued therefore, that prediction of cognitive change over age would benefit from the inclusion of personality traits as predictors of distal levels of cognitive performance. This argument is bolstered by the fact that some of the personality-cognition relations could be established over as long as a 35-year interval.

Given suitable longitudinal data, we also show that it is possible to utilize methods of extension analysis to bootstrap older to newer measurement domains, and thus try to reconstruct what changes on the newer measures would have been like had they be available at the earlier measurement points. Findings from the analyses using the estimated NEO data must be taken with caution since the multiple correlations of the personality factor scores with the NEO scales range from 0.39 to 0.59 (highest for the estimated scores for Openness and Conscientiousness, lowest for Agreeableness). Nevertheless, it is noteworthy that the estimated longitudinal data suggest greater developmental changes in personality over the adult life course than has previously been suspected as well as being generally consistent in changes from young adulthood documented in other studies (cf. Costa & McCrae 1993 , Soldz & Vaillant, 1999).

Perhaps, most importantly, we also demonstrated even though the Seattle Longitudinal Study did not originally focus on the assessment of personality traits, it was possible to utilize suitable estimation procedures that permit longitudinal, analyses bearing upon the contributions of personality constructs in understanding adult cognition.

Acknowledgments

The authors wish to express their gratitude to the members and staff of the Group Health Cooperative of Puget Sound without whose long-term support this study would have been impossible. The study has been supported by grants from the National Institute of Child Health and Human Development (HD00367, 1963–65; and HD04476, 1970–73) and by the National Institute on Aging (AG00480, 1973–79; AG03544, 1982–86; AG04770, 1984–88 and AG08055, 1989–2005).

The following colleagues, students and support staff (in alphabetical order) participated in one or more of the various data collections and analyses and/or contributed to the resultant scholarly products: Christopher Adams, David Adams, Diane Backschies, Margret Baltes, Paul Baltes, Thomas Barrett, Ute Bayeo, Timothy Benner, Gisela Bertulis, Julie Blaskevicz, Joy Bodnar, Hayden Bosworth, Barbara Buech, Michael Cady, Heather Chipaer, Soyeon Cho, Theresa Cooney, Jean Day, Cindy DeFrias, Robin Dunlap, Ranjana Dutta, Walter Eden, Charles Pick, Carrie Freeh, Michael Gilewski, Judith Gonda, Kathy Gribbin, Ann Gruber-Baldini, Cheryl Guyer, Brian Hallett, Elaine Hardin, Gene Hardin, Sarah Haessler, Charlene Herold, Christopher Hertzog, Judy Higgins, Robert Intrieri, Gina Jay, Christine Johnson, Heather Johnson, John Just, Alfred Kaszniak, Iseli Krauss, Eric Labouvie, Gisela Labouvie-Vief, Tamra Lair, Karen Lala, Karen Laughlin, Christine Lehl, Helen Leisowitz, Jackie Levine, Holly Mack, Heiner Mater, Scott Maitland, Hiroko Makiyama, Renee Marquardt, Dean Melang, Sherry Murr, Ann Nardi, John Nesselroade, Ha Nguyen, Shirley Paton Norleen, Ann O'Hanlon, Phyllis Olson, Holly Overman, Sara Paneck, Ms Parham, Julie Parmentier, Cherill Petera, Robert Peterson, Robert Plomin, Samuel Popkin, Alan Posthomer, Margaret Quayhagen, Andrew Revell, Sarah Rosen, Amy Roth, Christine Roy, Pat Sand, Coloma Harrison Schaie, Carolyn Seszniak, John Schulenberg, Anna Shuey, Michaei Singer, Anita Stolov, Yield Stone, Charles Strother, Alejandra Suarez, Linda Ten, Richard Vigesaa, Nathaniel Wagner, Faika Zanjani, and Elizabeth Zelinski.

Further information on the Seattle Longitudinal Study can be obtained at http://geron.psu.edu/sls .

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