Cohort Effect

A cohort refers to a group of individuals who share a common demographic characteristic or life experience within a specific timeframe.

In plain English, this is a group of people who “grew up” or experienced a major life event together.

While we often think of cohorts as generations, such as Baby Boomers or Gen Z, the term applies to any group linked by timing. Common examples of cohorts include:

• Students attending the same university during a specific decade.

• Individuals who entered the workforce during a global recession.

• New parents who navigated the first year of child-rearing in 2020.

Because these groups share a distinct cultural and historical background, they often develop unique behaviors or attitudes.

This shared history creates a confounding variable, which is an outside factor that can unintentionally influence the results of an experiment.

A cohort effect occurs when research findings are heavily influenced by the fact that the participants all lived through this same specific era or time period.

Examples of Cohort Effects

Behavioral and Philosophical Worldviews

Generations often develop distinct “philosophies of life” based on the economic climate of their upbringing.

• The “Make-Do” Philosophy: People born in the 1930s lived through food rationing and global conflict. This created a lifelong habit of repairing items and conserving resources.

• The “Disposable” Philosophy: Those born in the 1980s matured during rapid financial and technological growth. This cohort is more likely to view products as replaceable.

• Trust and Reciprocity: In economic games, older cohorts show higher levels of reciprocity. This is the social norm of responding to a positive action with another positive action.

The Great Rewiring: Understanding the Gen Z Cohort Effect

While previous generations were defined by war or economic shifts, Generation Z (born after 1995) is defined by technology.

Jonathan Haidt describes this as a cohort effect. In The Anxious Generation, he argues it is a generational shift in mental health and behavior caused by a shared historical experience.

He defines this shift as the Great Rewiring of Childhood, occurring between 2010 and 2015.

During this window, childhood moved from being “play-based” in the physical world to “phone-based” in a virtual world.

• Millennials: Mostly finished puberty before the meteoric rise of smartphones and social media.

• Gen Z: The first group to navigate developmental milestones through social media and constant digital connection.

Haidt identifies this as a cohort effect because the surge in anxiety and depression is specific to those who grew up during this technological transition.

He posits that Gen Z lost “antifragility”, the ability to grow through real-world challenges. At the same time, “experience blockers” like smartphones permanently distinguished this generation’s psychological profile from earlier ones.

Handedness and Cultural Pressure

Hatta and Kawakami (1995) looked at handedness, which is the natural preference for using one hand over the other. They compared Japanese students from 1973 to a new sample in 1993.

The later group had significantly more left-handed and ambidextrous (able to use both hands equally) females.

The researchers concluded that Western media made non-right-handedness more acceptable. This shift reduced parental pressure to “correct” left-handed children.

Personality and Locus of Control

Meta-analyses show that more recent cohorts in the United States score higher in narcissism, which is an inflated sense of self-importance.

Additionally, there has been a shift toward an external locus of control. This is the belief that outside forces, rather than personal effort, determine one’s success.

These changes reflect shifting cultural values rather than a fundamental change in human nature.

Cross-Sectional Challenge

Most researchers begin with a cross-sectional design, comparing different age groups at a single point in time. This approach is efficient and cost-effective, but it frequently suffers from confounding variables: outside influences that distort the results.

In these studies, age is confounded with the era of birth. That confound is hard to remove.

Variations in the data might simply reflect the distinct educational or social environments of each generation. Even if researchers match participants on social backgrounds, one group may have experienced a unique historical event.

This distortion makes it difficult to claim that getting older is the true cause of any observed change.

Intelligence and Education

If a cross sectional study finds that 70-year-olds have lower IQ scores than 20-year-olds, is intelligence naturally declining?

Not necessarily.

This difference often reflects educational attainment, which is the highest level of formal schooling an individual completes.

Older cohorts frequently had fewer opportunities for higher education than younger generations. Thus, the IQ gap may reflect schooling quality rather than cognitive decay.

Salthouse vs. Schaie: The Cognitive Ageing Debate

This confound played out in a real dispute between two prominent psychologists. Salthouse (2009) used cross-sectional data to argue that healthy cognitive decline starts surprisingly early, in a person’s twenties or thirties.

Schaie (2009) rejected this conclusion. He called it the “cross-sectional fallacy,” the mistake of treating an age difference between generations as though it were ageing within one person.

The younger and older groups in Salthouse’s sample had grown up with different schooling and test experience. Schaie argued their test-score gap could just as easily reflect that cohort difference as true ageing.

Both readings fit the data. The exchange shows that a cross-sectional design can detect an age difference efficiently. It cannot say on its own whether that difference is ageing or generational change.

Longitudinal Research

To solve these issues, psychologists use longitudinal research.

This involves testing the same group of individuals repeatedly over many years.

By tracking the same people, researchers eliminate generational differences.

For example, a study might measure a person’s diet at age 20, 30, and 40. This reveals true developmental changes because the cohort remains constant.

Longitudinal designs support life course theories, which suggest that human development is a cumulative result of belonging to a specific cohort.

This solution has its own cost. Testing the same people for years takes far longer and far more money than a single cross-sectional survey. Some participants also drop out along the way.

This creates attrition: the progressive loss of participants as people move, withdraw, or simply stop responding over the years. People who leave are rarely a random slice of those who remain. The final sample can end up biased toward whoever happened to stay in touch.

Sequential Method

To overcome the limitations of simpler methods, researchers often turn to sequential research.

This is a hybrid method that combines the strengths of both cross-sectional and longitudinal approaches.

By examining several different age groups at multiple points in time, scientists can finally separate the effects of aging from the influence of history.

How Sequential Designs Work

The sequential (cross-longitudinal) method begins like a cross-sectional study. Researchers first select participants from various age cohorts to compare them immediately.

However, the study then adopts a longitudinal element by retesting those same participants at least once more in the future.

This dual approach allows for empirical validation, or the use of observable evidence to confirm that findings are consistent across different generations.

This approach traces back to Baltes (1968). He showed that cross-sectional designs confound age with cohort, while longitudinal designs confound age with the specific time of testing.

His solution was to study more than one birth cohort across more than one occasion. Doing so lets researchers separate the effects of age, cohort, and testing time instead of leaving them tangled together.

Case Study: The Development of Self-Esteem

A prominent example of this method is the work of Orth, Trzesniewski, and Robins (2010), who investigated how self-esteem changes across the lifespan.

Why Strategy Matters

Advanced designs like these reduce the impact of confounding variables.

These are outside factors that can confuse the results of an experiment by providing an alternative explanation for the data.

By using sequential methods, psychologists ensure their findings represent true human growth rather than just a “snapshot” of a specific historical moment.

This rigor allows for a deeper understanding of the life course, which is the sequence of events and roles that individuals occupy as they age.

References

Atingdui, N. (2011). Cohort effect. Encyclopedia of child behavior and development, 389-389.

Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11(3), 145–171. https://doi.org/10.1159/000270604

Cozby, P. C., Bates, S., Krageloh, C., Lacherez, P., & Van Rooy, D. (1977). Methods in behavioral research: Mayfield publishing company Houston, TX.

Dołęga, Z., Jeż, W., & Irzyniec, T. (2014). The cohort effect in studies related to differences in psychosocial functioning of women with Turner syndrome. Endokrynologia Polska, 65(4), 287-294.

Haidt, J. (2024). The anxious generation: How the great rewiring of childhood is causing an epidemic of mental illness. Penguin Press.

Hatta, T., & Kawakami, A. (1995). Patterns of handedness in modern Japanese: a cohort effect shown by re-administration of the HN Handedness Inventory after 20 years. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 49(4), 505.

Keyes, K. M., Utz, R. L., Robinson, W., & Li, G. (2010). What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971–2006. Social science & medicine, 70(7), 1100-1108.

Orth, U., Trzesniewski, K. H., & Robins, R. W. (2010). Self-esteem development from young adulthood to old age: A cohort-sequential longitudinal study. Journal of Personality and Social Psychology, 98(4), 645–658.

Porac, C., & Coren, S. (1979). A test of the validity of offsprings” report of parental handedness. Perceptual and Motor Skills, 49(1), 227-231.

Ryder, N. B. (1985). The cohort as a concept in the study of social change. In Cohort analysis in social research (pp. 9-44): Springer.

Schaie, K. W. (1986). Beyond calendar definitions of age, time, and cohort: The general developmental model revisited. Developmental review, 6(3), 252-277.

Warner Schaie, K. Cohort Sequential Designs (Convergence Analysis). In The Encyclopedia of Clinical Psychology (pp. 1-6).

Willets, R. C. (2004). The cohort effect: insights and explanations. British Actuarial Journal, 10(4), 833-877.

Worden, P. E., & Sherman-Brown, S. (1983). A word-frequency cohort effect in young versus elderly adults” memory for words. Developmental Psychology, 19(4), 521.