Journal of Gerontological Nursing

Age, Cohort, and Time-Period Confounds in Aging Research

Christine R Kovach, MSN, RN; Thomas R Knapp, MD

Abstract

A recent study has concluded that older adult females demonstrated a significantly less positive perception of their body images, as measured by the Draw-AFtrson protective technique, in comparison to younger adult females.1 What inferences can be made from these results? Do these results indicate that, as women age, their body image decreases? Or do they indicate that depression era women have less positive body images than baby boom era women?

This article will explore these questions through a discussion of the age, cohort, and time confounds prevalent in quantitative developmental research. Nurses interested in developmental issues must understand these confounds so that they can interpret research results in light of these confounds, and not be misled by research results which do not control for age, cohort, and timeperiod effects.

Definitions of Terms

In almost all studies, age is operationally defined by years since birth. The advantage of this is that everyone clearly knows what age means. The disadvantage is that years since birth does not really tell us much about age or what similarities are possessed by people who are that age. It is a vague definition in one sense, and a clear unarguable definition in another.

Cohort is most often defined as year of birth. Cohorts are people about the same age who, in a given period, have similar experiences that may affect them in the same way. The concept of cohort assumes that common experience may result in common distinctive effects.

Rosow2 has defined cohort as: "I) consisting of people who share a given life experience; 2) this experience is socially or historically structured; 3) it occurs in a common generational framework; 4) its effects distinguish one generation from another; and 5) these effects are relatively stable over the life course."

This definition seems more adequate than year of birth as a definition of cohort. However, the difficulty in operationalizing this conceptualization has limited its easy application and most studies have neglected the full meaning of cohort in their operationalization.

Time-period refers to the time at which the measurement was taken. The time-period can be broad, such as the year of measurement. In certain studies, such as neonatal or biorhythmic research, the time period may be hour of the day or even as restricted as minute or second of measurement.

Confounding refers to research situations in which it cannot be determined if it was variable A or another variable which caused the effect on the dependent variable. This is a common problem in research. It is often difficult, even in experimental designs, to achieve sufficient control to eliminate all confounding influences.

The Confounds

Consider the following hypothetical situation: In pursuing developmental research, an investigator interviews women and discovers that older women attempt to teach the investigator many facets of fanning: how to preserve fresh cabbage and apples for the winter, how to milk cows the fastest, and how to smoke hams. When she speaks with younger women in the community they do not have farming knowledge. She concludes that as women grow older they improve in their ability to learn fanning skills.

This example seems to reflect cohort differences rather than age-related differences. The conclusion made by this researcher is therefore suspect. Imagine the following example where the confound is not as clear: A nurse is interested in memory.

She studies the ability to memorize the side effects of five common cardiac drugs in young and old adults. Since the elderly subjects do not memorize the side effects as well, she concludes that as people age, their ability to learn declines.

These examples represent the confound which exists…

A recent study has concluded that older adult females demonstrated a significantly less positive perception of their body images, as measured by the Draw-AFtrson protective technique, in comparison to younger adult females.1 What inferences can be made from these results? Do these results indicate that, as women age, their body image decreases? Or do they indicate that depression era women have less positive body images than baby boom era women?

This article will explore these questions through a discussion of the age, cohort, and time confounds prevalent in quantitative developmental research. Nurses interested in developmental issues must understand these confounds so that they can interpret research results in light of these confounds, and not be misled by research results which do not control for age, cohort, and timeperiod effects.

Definitions of Terms

In almost all studies, age is operationally defined by years since birth. The advantage of this is that everyone clearly knows what age means. The disadvantage is that years since birth does not really tell us much about age or what similarities are possessed by people who are that age. It is a vague definition in one sense, and a clear unarguable definition in another.

Cohort is most often defined as year of birth. Cohorts are people about the same age who, in a given period, have similar experiences that may affect them in the same way. The concept of cohort assumes that common experience may result in common distinctive effects.

Rosow2 has defined cohort as: "I) consisting of people who share a given life experience; 2) this experience is socially or historically structured; 3) it occurs in a common generational framework; 4) its effects distinguish one generation from another; and 5) these effects are relatively stable over the life course."

This definition seems more adequate than year of birth as a definition of cohort. However, the difficulty in operationalizing this conceptualization has limited its easy application and most studies have neglected the full meaning of cohort in their operationalization.

Time-period refers to the time at which the measurement was taken. The time-period can be broad, such as the year of measurement. In certain studies, such as neonatal or biorhythmic research, the time period may be hour of the day or even as restricted as minute or second of measurement.

Confounding refers to research situations in which it cannot be determined if it was variable A or another variable which caused the effect on the dependent variable. This is a common problem in research. It is often difficult, even in experimental designs, to achieve sufficient control to eliminate all confounding influences.

The Confounds

Consider the following hypothetical situation: In pursuing developmental research, an investigator interviews women and discovers that older women attempt to teach the investigator many facets of fanning: how to preserve fresh cabbage and apples for the winter, how to milk cows the fastest, and how to smoke hams. When she speaks with younger women in the community they do not have farming knowledge. She concludes that as women grow older they improve in their ability to learn fanning skills.

This example seems to reflect cohort differences rather than age-related differences. The conclusion made by this researcher is therefore suspect. Imagine the following example where the confound is not as clear: A nurse is interested in memory.

She studies the ability to memorize the side effects of five common cardiac drugs in young and old adults. Since the elderly subjects do not memorize the side effects as well, she concludes that as people age, their ability to learn declines.

These examples represent the confound which exists between age and cohort. In the memory example it seems more appropriate to infer agerelated differences rather than cohortdifferences as responsible for variation in ability to memorize side effects of drugs.

But it may, in fact, be a wrong inference. Differences in ability to memorize side effects may relate to age changes, generational influences, or some combination of the two.

It is impossible to sort out the confound of age and cohort in any study based on observations of different age or developmental groups at a single point in time. These studies are called cross-sectional studies because they only look at one cross section of time. In some instances the reader can logically infer a separation of age and cohort in cross-sectional studies, but this is dangerous because erroneous conclusions can be made.

Nursing research is largely cross-sectional for several reasons. Cross-sectional studies are easy to manage, economical, and take less time to conduct. In cross-sectional studies involving age-related changes, there are usually several rival hypotheses for any observed differences. That is to say, there are a number of alternative explanations in the form of several generational life experiences which could also account for the observed differences.

Any nurse reading cross-sectional developmental research should question inferences which have been made, and seek alternative explanations for any observed differences present in research. Too often, researchers become so committed to their area of interest that they cannot "see the forest for the trees." Great caution must be exerted in making inferences about agerelated changes in cross-sectional studies.

Research projects designed to collect data over more than one point in time are called longitudinal studies. Some researchers have begun using longitudinal designs in the hope that the age and cohort problem could be resolved. Consider the following example:

In 1970, a nurse studied the attitudes of people to public health policy. These attitudes were quite liberal. In 1985, she studied these same people and found their attitudes toward public health policy were much more conservative. Do these changes indicate that as people age they become more conservative in these attitudes, or do they reflect changes in attitudes due to the influence of time-period effects?

The influence of time-period effects is the confound which is a problem in longitudinal design. As people are studied overtime, the observed changes may be true age-related changes but they may also be changes due to time-period effects.

Time-period effects have also been called time effects, or time-of-measurement effects. They are most often thought of as being due to environmental influences but may also relate to changes in subjects over time.

Schaie3 wrote that time effects "denote that state of the environment within which a given set of data were obtained . . . changes in the state of the environment may contribute to the effects noted in an aging study." It is often difficult to distinguish between cohort and time-period effects. Timeperiod effects are presumed to only exert an influence during the measurement period. Cohort effects, on the other hand, often begin at birth and are most often thought to have a lifelong impact.5

Disentangling the Confounds

Due to recognition by researchers of the prevalence of studies plagued with age, cohort, and time confounds, many complex designs and interpretive decision rules have been developed in an attempt to disentangle these confounds. Since these designs are being used increasingly in developmental studies, several will be presented.

Schaie3 proposed three sequential strategies designed to permit inferences regarding the relative contribution of age, cohort, and time-period variables to developmental trends. Each of these strategies requires that several analyses of variance be conducted on data classified in terms of age, cohort, and/or time-period.

In the cross-sectional-sequential design, two or more different age cohorts are studied longitudinally so that both changes over time and cohort differences can be detected. From such a design it is possible to detect the contributions of age/cohort and age/time to the observed differences in the study. This design involves a series of simultaneous cross-sectional and longitudinal analyses.

Table

TABLE 1A CROSS-SECTIONAL-SEQUENTIAL DESIGN

TABLE 1

A CROSS-SECTIONAL-SEQUENTIAL DESIGN

Table

TABLE 2A TIME-LAG-SEQUENTIAL DESIGN

TABLE 2

A TIME-LAG-SEQUENTIAL DESIGN

Table 1 shows a cross-sectionalsequential design in which three hypothetical cohorts might be studied. Nine different samples would be needed for this study and data could be collected over a four-year period in 1988, 1990, and 1992. Note that in this design, observations are not repeated on the same members of a cohort from one time to the next, but on a different random sample from the same cohort.

Cross-sectional-sequential designs have the advantage of not requiring the same subjects from one observation to the next. This is important because it is often very difficult or impossible to preserve a sample over time and can lead to serious attrition biases in a study. Also, because different samples are used, the effects of being studied and initial selection biases for expected availability over an extended period do not threaten the validity of this design. Cross-sectional-sequential designs are not, however, able to identify changes in individuals over time.

The equivalence of successive samples from a cohort may be difficult to obtain and would threaten the validity of the study.4 The cross-sectionalsequential design involves both the age/ cohort cross-sectional confound and the longitudinal age/time confound.

When different groups of subjects are tested at more than one point in time it is a time-lag comparison. The groups studied are of different cohorts, but are studied when they are the same age. An example of a time-lag study would involve a sample of 60-year-old men studied for reminiscent behavior in 1975 , and in 1990 a new sample of 60year-old men would be studied for reminiscent behavior. This type of longitudinal study confounds time and cohort.

Time-lag-sequential designs involve a comparison of different time-lag groups with cross-sectional comparisons. In the example of this design in Table 2, three samples from each uf three cohorts are compared on three occasions. This study could reveal the behavior of subjects aged 60, 62, and 64 from three different cohorts. Notice in this example that it has taken eight years to study an age span of four years using this design.

In the time-lag-sequential design, different aged cohorts are compared over the same longitudinal period. This design involves cross- sectional comparisons of cohorts, comparisons of the longitudinal course of development in each cohort over the course of the study, and comparisons between the effect of time of observation and the effect of age at which behavior is studied.4 This design does not have the attrition, testing, and selection threats which longitudinal designs have because different samples are studied at each testing period. Equivalence of successive cohort samples may also be difficult to ensure in this design. The confounds in this design are cohort/time and age/ cohort.

A complex design called the longitudinal-sequential design involves a series of simultaneous cross-sectional and longitudinal comparisons. In the hypothetical example, the same subjects from each cohort would be studied on three occasions from 1988 to 1992 (see Table 3).

Longitudinal-sequential designs make it possible to obtain longitudinal data of development in less time than it takes development to occur. In Table 3 eight years of development could be studied in a four-year period.

The longitudinal-sequential design allows cross- sectional comparisons of cohorts at any point during the study, comparisons of the longitudinal course of development in each cohort, and time-lag comparisons among cohorts as they reach a particular age in successive years.

This design is intended to determine whether changes in behavior are attributable to age, cohort, or the interaction between the two.4 In this design each cohort is measured at a different time and each age-group is measured at a different time so mat it involves the confounds of age/time and cohort/time.

Table

TABLE 3A LONGITUDINAL-SEQUENTIAL DESIGN

TABLE 3

A LONGITUDINAL-SEQUENTIAL DESIGN

Decision Rules

In order to interpret the results of these complex studies several authors have developed decision rules or inference schemes to guide the interpretation of results.5'8 These guides should be referred to by the researcher using one of these designs.

Several warning flags must be raised at this point. First, inference schemes are based on the following assumptions: I) one and only one of the confounds is important in the data; 2) the effects are linear; and 3) in cross-sectionalsequential and time-lag-sequential analyses, the effects do not interact (i.e. no one age/cohort group changes over time more or less than does another age/ cohort group).5 Unfortunately, in most studies these assumptions are not met. The confounded effects may be multiplicative rather than additive. The data may be curvilinear. And when one agegroup changes differently from the others, the inference of age or cohort is not as meaningful as the interaction. It is imperative to remember that the scheme and inferences based upon it are only useful if the assumptions are met.

Sample size must also be considered in these studies since sample size plays an important part in determining statistical significance. If the sample size of a study is too small a Type II error can be easily made by wrongly accepting a false null hypothesis. Conversely, statistical significance can be "bought" through a large sample size which serves to increase the power of a study or the probability of rejecting the null hypothesis.9

The final warning flag regarding inference schemes is that they are not without error and there are logical limits to any such set of rules. Current researchers working with these designs and strategies have demonstrated and discussed the falsity of the notion that sequential strategies unequivocably permit the separation of age, cohort, and time effects.5'8 These researchers have concluded that it is not possible to establish firm decision rules regarding the interpretation of developmental studies.

It is important to use one's logical thinking in interpreting the data from these complex designs. At times a researcher's knowledge of the subject matter and logical deduction can come in direct conflict with the interpretation suggested by the inference scheme. Each researcher must decide how to resolve this conflict and include it in the research study discussion.

Another topic to consider in developmental aging research is the intracohort differences which exist in the aged population. The aged are a diverse group and there can be relatively large intracohort differences which can play an important role in the study of intercohort differences. If a study yields a large within cohort variability it is unlikely to obtain between cohort differences from the statistical analysis.10

Conclusion

This article has attempted to point out some difficulties and hazards inherent in doing research aimed at discovering age-related changes. For those individuals interested in developmental issues, an understanding of these problems is essential so that interpretations of developmental research are done with insight into these confounds.

Most nursing research is cross-sectional. In interpreting cross-sectional data it is not possible to infer whether age or cohort is responsible for the obtained results. There are times when this confound is of little importance and there are times when it is of great importance. If a researcher is interested in adults' attitudes toward nurse practitioners it may matter little to the researcher if the differences in younger and older persons' attitudes are due to age or cohort. This information's descriptive value may still be important even though the differences observed today may not be the same 40 years from today, because cohort may be responsible for the differences rather than age.

When a researcher is interested in cause-effect explanations the cross-sectional approach can lead to erroneous conclusions and the separation of age and cohort becomes necessary. If a researcher is interested in studying the effect of age on ability to self-administer insulin in patients with diabetes mellitus, it may be very important to be able to interpret if differences in ability to self-administer insulin can be attributed to age or cohort effects . Also, nursing research which attempts to examine differences in abilities or psychological traits with age (e.g. learning, compliance, anxiety, self-esteem) should attempt to sort out the confounds of age, cohort, and time.

Longitudinal studies do allow for examination of changes over time and the temporal sequencing of phenomena. Longitudinal designs, however, must deal with threats such as age/ time confounds, selection biases, mortality, and the effects of testing. Time effects are often indistinguishable from cohort effects because both involve environmental influences. Longitudinal studies are also time consuming and expensive.

Several complex designs have been presented which allow for some sorting of the age. time, and cohort confounds. These designs involve a considerable investment of time, effort, and money. Only under certain conditions and under certain assumptions is it possible to separate and estimate the age, timeperiod, and cohort effects. These designs can be useful particularly if the nurse is interested in cause-effect developmental changes.

All nurses who read and interpret research results must remember that the results obtained are not written in stone. The careful reader of research studies will search her mind for alternate hypotheses which may explain the obtained results in a study. Cross-sectional and longitudinal studies possess many threats to validity and possibilities for erroneous conclusions. The more complex designs presented in this article allow for more comparisons but must also be interpreted with caution and logical rigor.

References

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  • 2. Rosow I: What is a cohort and why? Human DfV 1978; 21:65-75.
  • 3. Schaie KW: A general model for the study of developmental problems. PSVCA Bull 1965; 64:92-107.
  • 4. Achenbach TM: Research in Developmental Psvchologv: Concepts, Strategies, and Methods. New York, Free Press, 1978.
  • 5. Botwinick J: Aging and Behavior. New York. Springer Publishing, 1984.
  • 6. Costa PT & McCrae RR: An approach to the attribution of aging, period, and cohort effects. Psych Bull 1982; 92:238-250.
  • 7. Palmore E: When can age, period, and cohort be separated? Social Forces 1978; 57:282-295.
  • 8. Adam J: Sequential strategies and the separation of age, cohort, and time-of-measurement contributions to developmental data. Psych Bull 1978; 85:1309-1316.
  • 9. Cohen J & Cohen P; Applied Multiple Regression/Correlation Analysis for lhe Behavioral Sciences. Hillsdale. NJ: Lawrence Erlbaum Associates, 1983.
  • 10. Dannefer D: Aging as intracohort differentiation: Accentuation, the Matthew effect, and the life course. Social Forum 1987; 2:211-235.

TABLE 1

A CROSS-SECTIONAL-SEQUENTIAL DESIGN

TABLE 2

A TIME-LAG-SEQUENTIAL DESIGN

TABLE 3

A LONGITUDINAL-SEQUENTIAL DESIGN

10.3928/0098-9134-19890301-05

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