The Building Blocks of Adult Development & Aging Research: Age, Cohort & Time of Measurement

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Lesson Transcript
Instructor: Devin Kowalczyk

Devin has taught psychology and has a master's degree in clinical forensic psychology. He is working on his PhD.

This lesson explores some of the fundamental building blocks of designing a research study that has to do with how people change over time. This lesson examines ways a researcher can parse out the difference between group changes and changes over time.


Have you ever talked to a bunch of people around the same age and they all kind of seem the same? Maybe I'm just getting old and these teen pop stars start sounding the same. But it's also true for older people. Everyone around my parents' age seems very similar.

When researching adult development and aging, there are three types of factors that should be considered: age, cohort and time of measurement. These three things sound fairly similar, and in fact, they can be if you describe them too broadly.

Age is a participant's chronological age. This is fairly straightforward; it is how old the participant is on paper in years. Cohort is a participant's historical framing based on time and possibly place of birth. People born during the Great Depression see the world very differently than those born after. Those who were born pre-9/11 see the world differently than those who were born after it.

I included location, although this is not necessarily always included, meaning if you're interested in comparing locations, like north versus south, you can, but don't necessarily have to. Lastly, time of measurement is a participant's current framing based on current circumstances. Here we are concerned with what is occurring in the time and the world you are studying. Is there something occurring now that is causing the events you're seeing? We'll get into the example of these three in just a moment.

These items are part of the most efficient design, which was coined by Schaie, and is defined as the study of age, cohort and time elements to elicit the maximum amount of information from a study. By understanding this data, a researcher is able to parse out exactly which effect is occurring. For instance, if a researcher found it was age and not the time element, this would mean something very different than if it was the time but not the age element. This was most prominently used in the Seattle Longitudinal Study, which examined intelligence over a period of time.


Let's say you're curious about the effects of stress when it comes to learning in aging adults. You, the researcher, create a stressful environment to study your subjects, who are 30, 40, 50 and 60 years of age. You provide them with learning materials, place them in a stressful environment and then test their memory.

When examining your results, you will look at the group of scores between each age. Next, you will divide the different aged people into cohorts based on something like their responses to a personal quiz. This may give you two cohorts, the younger, 30 to 40, and the older, 50 to 60. With these two cohorts, you would compare the scores between the younger and the older groups.

Lastly, you will have to see if there are external reasons affecting the participants' learning. This would be done by having additional participants learn the same material without the stress in the environment. A two-fold benefit comes from this. One, you see if stress is affecting them. Two, you see if maybe some bigger issue is affecting the participants, such as a bombing or terrorism stress effect.

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