A cohort effect is a research result that occurs because of the characteristics of the cohort being studied. A cohort is any group that shares common historical or social experiences, like their year of birth. Cohort effects are a concern for researchers in fields such as sociology, epidemiology, and psychology.
A cohort is a group of people who share a particular characteristic. Typically, the shared characteristic is a life event that took place in a particular time period, like birth or high school graduation. The most commonly studied cohorts are age-related (e.g. individuals who share a birth year or generational designation). Additional examples of cohorts include:
A cohort is any group that shares common historical or social experiences, like their year of birth. The impact of the characteristics of a cohort on the results of a research study is called a cohort effect. While the factors that make a group of people a cohort may seem broad and therefore have little to do with each individual member of the group, the characteristics the group have in common may influence findings in a research context. This is because different cohorts’ characteristics vary over time due to their shared experiences, even if those experiences were very general. Psychological studies tend to focus on birth or generational cohorts. Such cohorts share common life experiences and experience similar social trends. For example, the historical events, arts and popular culture, political realities, economic conditions, and moral climate experienced by Millennials growing up were much different than those experienced by Baby Boomers. In other words, generational and birth cohorts develop in different sociocultural contexts, which can have an influence on the outcomes of research. Say a researcher wanted to see how easily people learned how to play a new mobile game featuring artificial intelligence. She decided to conduct a research study and recruited participants that ranged in age from 20 to 80 years old. Her findings showed that while the younger participants had an easy time learning how to play the game, the older participants had much more difficulty. The researcher could conclude that older people are less capable of learning to play the game than younger people. However, the research findings could also be the result of cohort effects in that older participants would have far less exposure to mobile devices than younger participants, potentially making it more difficult for them to learn how to play the new game. Thus, cohort effects are important to take into account in research. Cohort effects are a particular issue in studies that employ cross-sectional methods. In cross-sectional studies, researchers collect and compare data from participants in two or more age-related cohorts at a single point in time. For example, a researcher might collect information on attitudes towards gender equality in the workplace from people in their 20s, 40s, 60s, and 80s. The researcher might find that those in the 20-year-old group are more open to gender equality at work than those in the 80-year-old group. The researcher could conclude that as one ages they become less open to gender equality, but the results could also be the consequence of a cohort effect—the 80-year-old group had very different historical experiences than the 20-year-old group and, as a result, values gender equality differently. In cross-sectional studies of birth or generational cohorts it is difficult to discern whether a finding is the result of the aging process or if it is due to the differences between the various cohorts studied. The only way to guard against cohort effects when investigating the way people change over time is to perform a longitudinal study. In longitudinal studies, researchers collect data from a single set of participants over time. So, a researcher might collect information on attitudes towards gender equality in the workplace in 2019 from a group of 20 year olds, and then ask the participants the same questions when they are 40 (in 2039) and again when they are 60 (in 2059). The advantage of the longitudinal method is that by studying a group of people across time, change can be observed directly, ensuring there is no concern that cohort effects will compromise the research outcomes. On the other hand, longitudinal studies are expensive and time consuming, so researchers are more likely to use cross-sectional methods. With a cross-sectional design, comparisons among different age groups can be made quickly and efficiently, however, it is always possible that cohort effects have influenced a cross-sectional study’s findings. Psychological researchers have utilized cross-sectional and longitudinal studies to measure changes in personality traits over time. For example, a cross-sectional study of a group of participants ranging in age from 16 to 91 found that older adults were more agreeable and conscientious than younger adults. In explaining the limitations of their study, however, the researchers wrote that they couldn’t be certain if their findings were due to the effects of development over the lifespan or the result of cohort effects. In fact, there is research that indicates cohort effects play a role in personality differences. For example, a study published in the journal Personality and Individual Differences, the researcher utilized past research measuring extraversion in American college students to compare levels of this trait in birth cohorts from 1966 to 1993. The results showed a large increase in extraversion over time, showing the effect that birth cohort can have on personality.
Study design depends greatly on the nature of the research question. In other words, knowing what kind of information the study should collect is a first step in determining how the study will be carried out (also known as the methodology). Let’s say we want to investigate the relationship between daily walking and cholesterol levels in the body. One of the first things we’d have to determine is the type of study that will tell us the most about that relationship. Do we want to compare cholesterol levels among different populations of walkers and non-walkers at the same point in time? Or, do we want to measure cholesterol levels in a single population of daily walkers over an extended period of time? The first approach is typical of a cross-sectional study. The second requires a longitudinal study. To make our choice, we need to know more about the benefits and purpose of each study type. Cross-sectional studyBoth the cross-sectional and the longitudinal studies are observational studies. This means that researchers record information about their subjects without manipulating the study environment. In our study, we would simply measure the cholesterol levels of daily walkers and non-walkers along with any other characteristics that might be of interest to us. We would not influence non-walkers to take up that activity, or advise daily walkers to modify their behaviour. In short, we’d try not to interfere. The defining feature of a cross-sectional study is that it can compare different population groups at a single point in time. Think of it in terms of taking a snapshot. Findings are drawn from whatever fits into the frame. To return to our example, we might choose to measure cholesterol levels in daily walkers across two age groups, over 40 and under 40, and compare these to cholesterol levels among non-walkers in the same age groups. We might even create subgroups for gender. However, we would not consider past or future cholesterol levels, for these would fall outside the frame. We would look only at cholesterol levels at one point in time. The benefit of a cross-sectional study design is that it allows researchers to compare many different variables at the same time. We could, for example, look at age, gender, income and educational level in relation to walking and cholesterol levels, with little or no additional cost. However, cross-sectional studies may not provide definite information about cause-and-effect relationships. This is because such studies offer a snapshot of a single moment in time; they do not consider what happens before or after the snapshot is taken. Therefore, we can’t know for sure if our daily walkers had low cholesterol levels before taking up their exercise regimes, or if the behaviour of daily walking helped to reduce cholesterol levels that previously were high. Longitudinal studyA longitudinal study, like a cross-sectional one, is observational. So, once again, researchers do not interfere with their subjects. However, in a longitudinal study, researchers conduct several observations of the same subjects over a period of time, sometimes lasting many years. The benefit of a longitudinal study is that researchers are able to detect developments or changes in the characteristics of the target population at both the group and the individual level. The key here is that longitudinal studies extend beyond a single moment in time. As a result, they can establish sequences of events. To return to our example, we might choose to look at the change in cholesterol levels among women over 40 who walk daily for a period of 20 years. The longitudinal study design would account for cholesterol levels at the onset of a walking regime and as the walking behaviour continued over time. Therefore, a longitudinal study is more likely to suggest cause-and-effect relationships than a cross-sectional study by virtue of its scope. In general, the research should drive the design. But sometimes, the progression of the research helps determine which design is most appropriate. Cross-sectional studies can be done more quickly than longitudinal studies. That’s why researchers might start with a cross-sectional study to first establish whether there are links or associations between certain variables. Then they would set up a longitudinal study to study cause and effect. Source: At Work, Issue 81, Summer 2015: Institute for Work & Health, Toronto This column updates a previous column describing the same term, originally published in 2009. |
What research studies people of different ages at the same time?
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