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Key Takeaways
- Cohort effects describe how studying populations in different “cohorts” — having been born in a different time or region or having different life experiences — can alter the outcomes of studies.
- Norman Ryder first popularized sociological cohort effects as a means of providing a record of social and structural change.
- Researchers must weigh convenience and mitigating cohort effects when designing studies in psychology and medicine. Common study designs include cross-sectional, longitudinal, and sequential methods.
Cohort Definition
A cohort is a group of people who share a common set of demographic characteristics or experiences, including but not limited to age. Usually, in cross-sectional studies, age confounds cohort effects.
Some examples of cohorts include:
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People who became parents in the same year
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People who retire at the same time
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All of the students who went to a particular school during a certain time period.
Cohort Effect Definition
The cohort effect is the effect that having been born in a certain time, region, or period or having experienced the same life experiences has on the development or perceptions of a particular group.
These perceptions and characteristics are unique to the group in question (Atingdui, 2011).
Individual cohort effects can significantly alter the outcomes of studies, as cohorts reflect different economic and political conditions in society, different popular cultures, different educational systems, and different child-rearing practices (Cozby and Bates, 1977).
Cohort effects arise when different distributions of some trait, such as a disease or handedness, come about because of a changing or new environmental cause that affects different age groups differently (Keyes, Utz, Robinson, and Li, 2010).
The demographer Norman Ryder popularized the sociological cohort effect in his publication, The Cohort as a Concept in the Study of Social Change (1965).
Ryder believed that a cohort is a structural category where the unique circumstances and conditions where cohorts emerge, mature, and die to provide a record of social and structural change.
Thus, the conditions, barriers, and resources that each cohort is born into and lives their lives can shape the patterns and experiences of, for example, health and mortality for that cohort.
Sociologists, in particular, are concerned with the long-term risks of being born in a particular cohort above fluctuations in the health and behavior among members of the cohort.
For example, a sociologist may posit that the obesity epidemic is shaped by individuals coming of age in a media-saturated environment where sedentary lifestyles are socially acceptable, and many families are priced out of healthy, nourishing food (Keyes, Utz, Robinson, and Li, 2010).
Cross-Sectional vs. Longitudinal Research
Some research study designs can exaggerate the cohort effect more greatly than others. A cross-sectional research design is one where the researcher measures the outcome and exposures in participants at the same time.
For example, a cross-sectional study investigating the effect of cancer on taste preferences may sample from people in several stages of cancer at the same time, or a cross-sectional study investigating aging and brain activity may sample from people in different age groups at the same time.
Meanwhile, the longitudinal method is one where a single cohort is studied over time. For example, in the case of the study on aging and brain activity, a longitudinal study may sample the same group of individuals from ages 20 to 50. Consequently, the longitudinal study would take 30 years to conduct.
The cross-sectional method is more common than the longitudinal method for research because it is less expensive and yields results immediately, and allows researchers to compare different age groups quickly.
However, this is not without its disadvantages. For one thing, researchers must infer that differences between age groups are a consequence of age and not some other factor (Schaie, 1986)
For example, consider a study attempting to measure the impact of age on learning to use computers, which finds that older adults have a significantly decreased ability such that 50-year-olds score lower than 40-year-olds, who score lower than 30-year-olds, and so on.
Although it is possible that the ability to learn to use a computer decreases with age, the difference could also be due to a cohort effect, as the older participants had significantly lower exposure to computers growing up.
Here, the cross-sectional method of the study confounds age and cohort effects. In general, studies investigating larger age ranges have greater cohort effects than those studying narrower ranges.
A study comparing young adolescents to 90-year-olds, for example, is likely to have a more pronounced cohort effect than one comparing 20 and 30-year-olds, as the contrast in the societal, political, educational, and economic factors experienced between those in the first study is significantly greater than those in the second (Cozby and Bates, 1977).
Although longitudinal study designs mitigate cohort effects, they can be expensive and difficult. There is also another major problem with longitudinal studies: people can move, die, or otherwise lose interest in the study.
Thus, the researchers must dedicate large amounts of resources to convincing people to continue, travel far to collect more data, and compare the scores of people who drop out with those who stay in order to provide a better analysis of their results (Cozby and Bates, 1977).
Sequential Method
One possible alternative to the longitudinal and cross-sectional study design methods is the sequential method. The sequential method begins like the cross-sectional method, with researchers studying groups of people in different age cohorts.
However, the sequential method incorporates elements of the longitudinal method by testing those in the cross-sectional method at least one other time (Cozby and Bates, 1977). Sequential study designs can help separate age and cohort effects (Schaie, 2015), reducing their impact on the analysis of results.
One example of a study using the sequential method is Worth, Trześniewski, and Orth, Trzesniewski, and Robins (2010), which studied the development of self-esteem over time. Orth and his colleagues examined six age cohorts and examined their self-esteem ratings from 1986, 1989, 1994, and 2002.
Ultimately, the researchers found that self-esteem tends to increase from age 25 to age 60 but decreases in later years. As a longitudinal study, such a result would have taken many more years to obtain (Cozby and Bates, 1977).
Examples of the Cohort Effect
UK Mortality Study
Willets (2004) investigated the effects of the cohort effect in a study on mortality for those born in the United Kingdom over a large period of time.
In particular, the study that Willets analyzed found that people born between 1925 and 1945 experienced a markedly more rapid improvement in mortality than generations either older or younger than those. This effect was replicated by other studies.
Willets (2004) suggested that this cohort effect was caused by a number of factors.
For example, those born between 1925 and 1945 saw a large drop-off in cigarette smokers, and these participants also experienced lower rates of heart disease and breast cancer — possibly a consequence of a better diet in early life than previous generations.
Psychosocial Functioning of Women With Turner Syndrome
Turner syndrome is a condition where women have a completely or partially missing X chromosome, and it can include symptoms such as short stature and lack of pubertal development.
Dolega, Jez, and Irzyniec (2014) investigated the differences in the psychosocial functioning of women with Turner syndrome in two different generations, particularly the social and family environments that these women faced in different generations.
Generally, those in the younger patient groups had received better and earlier therapeutic care for Turner’s Syndrome, the condition often noticed in childhood. Those from the younger cohorts felt less handicapped, leading to a higher likelihood of having a higher level of education.
Meanwhile, those in the older cohorts tended to have a lower level of education and a lack of a life partner, causing the desire for a life change.
Additionally, the parents of the younger Turner syndrome participants tended to have greater levels of education, perhaps resulting in them having greater resources to treat and diagnose their child’s condition (Dolega, Jez, and Irzyniec, 2014).
Memory Cohort Effects
Worden and Sherman-Brown (1983) describe the ability of 72 undergraduates and 72 elderly participants to recall words from four types of lists varying in terms of word frequency and datedness.
While so-called “popular” words had a higher frequency in dictionaries from both 1921 and 1967, “dated” words had a high frequency in 1921 but a low frequency in 1967, and “contemporary” words had a low frequency in 1921 but a high frequency in 1967.
The researchers found that older participants were better able to recall words that were frequent in the 1921 dictionary, while younger participants tended to better recall those frequent in 1967.
All in all, the results suggested a word-frequency cohort effect, where high-frequency words from someone’s youth are most memorable (Worden and Sherman-Brown, 1983).
Handedness
Hatta and Kawakami (1995) investigated the results of the H.N. Handedness Inventory, originally administered to 1199 Japanese students in 1973, when administered to a new sample of 1700 Japanese students 20 years later.
The researchers found that there were more left-handed and ambidextrous females in the second samples and that this difference in handedness was underlined by a number of non-biological factors. For example, researchers have attributed handedness to cultural demands and parental pressure (Corren and Porac, 1979).
In particular, Hatta and Kawakami argued that after the Second World War, the Western mass media made the presence of non-right-handedness more present, thus making left-handedness more acceptable (Hatta and Kawakami, 1995).
References
Atingdui, N. (2011). Cohort effect. Encyclopedia of child behavior and development, 389-389.
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.
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.
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.
