Question

10) Describe and diagram a

cohort sequential design

and evaluate its advantages and limitations for

identifying aging effects.

11) What is a

time of measurement

effect? Why should a researcher be concerned with time of

measurement effects when trying to identify developmental or aging effects? Be prepared to recognize

examples of possible time of measurement effects within descriptions of research studies.

Answer 1

Answer 1.

A cohort-sequential or cross-sequential design is a research method in lifespan development studies that combines both a longitudinal design and a cross-sectional design. It combines the benefits of of both cross-sectional and longitudinal designs.

It has an obvious advantage in a research where a researcher wants to study development over some large period of time within the lifespan. Instead of studying particular individuals across that whole period of time (e.g. 30- 80 years) as in a longitudinal design, or multiple individuals of different ages at one time (e.g. 30, 35, 40, 45, 50, 55, and 60, 65, 70, 75, and 80 years) as in a cross-sectional design, the researcher would choose a smaller time window (e.g. 30 years) to study several individuals of different starting ages. An example of a cross-sequential design is shown in the diagram below.

cohort	age
A	20	25	30
B	25	30	35
C	30	35	40
D	35	40	45
E	40	45	50
F	45	50	55
G	50	55	60
year of measurement:	2005	2010	2015

As can be seen above, the design spans a period of 10 years, from 1990 to 2000, which makes it possible for the researcher to study 7 overlapping cohorts with different starting ages and thus the study would be able to provide information on the entire span of development from ages 20 to 60 years. Thus, by following several differently aged cohorts over time, we look at one group over a long time and we are also able to look at a whole bunch of groups within the time frame of  the present.

While a cohort design can be used to investigate common exposures such as, risk factors for cardiovascular disease and cancer they are particularly useful for evaluating the effects of rare or unusual exposures, since the design makes it a point to identify an adequate number of subjects who have a significant exposure. It would allow the researcher to not mistake normative cohort effects for age graded differences that is whether the difference is due to exposure to a variable at a particular stage of development or due to expected and predictable changes due to age.

However, a major disadvantage of this method is that  age- related differences are confounded with interactions between the cohort and the time of measurement. For example, older cohorts like Generation Xers would give “age” estimates that occurred before major historical events like such as the Iraq war, while Younger cohorts specially Generation Yers would give these estimates after the historical events had happened.