By Thoralf Skolem
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Extra resources for Abstract Set Theory (Notre Dame Mathematical Lectures 8)
These trends should be conceptualized as period effects or cohort effects is not clear, but acknowledging that they are at least partially cohort effects is important for interpreting the cross-sectional data on age and happiness. For instance, any intercohort trend toward greater happiness will reduce cross-sectional age differences created by positive age effects. 1 indicate. 1, but positive age effects have very likely been greater than those suggested by the data. , 2002). a. ” b. Indicated ages are the middle ages in the 3-year series.
2002). a. ” For males, the relationship is monotonically positive, but for females, the HI is highest in late middle age, lower at the more advanced ages, and lowest in young adulthood. Up to the 50s, females reported greater happiness than males, but this difference is sharply reversed among the elderly. As the reader should know at this point, these relationships of reported happiness to age may confound age, cohort, and attrition (compositional) effects. The most likely first interpretation of the data is that they show how the happiness of men and women changes as they grow older, but there are alternative explanations.
Suppose that in a population of persons ages 25–64, each 10 years of aging had an effect of +10 on the dependent variable, the age distribution did not change between Times A and B, there was a period effect of +10 from Time A to Time B, and cohort succession had no effect during the time covered. Age effects do not contribute to population change if the age distribution stays constant, but they do contribute to intracohort change, adding to that caused by period influences. Thus, in this hypothetical case, intracohort change will be more than enough to account for total population change.