By John C. Cavanaugh, Fredda Blanchard-Fields
Written inside a biopsychosocial framework, Cavanaugh and Blanchard-Fields' best-selling textual content covers the categorical ages-stages of grownup improvement and getting older. In its extraordinary assurance of present learn and thought, the authors draw transparent connections among examine and alertness. The book's concentrate on "positive aging" and the earnings and losses humans adventure throughout maturity distinguish it from its rivals.
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Additional resources for Adult Development and Aging (6th Edition)
He hoped that she inherited the family genes for good health. He wondered how Devonna’s life growing up as an African American in the United States would be different from his experiences. Like many grandparents, Levar wonders what the future holds for his granddaughter. The questions he considers are interesting in their own right, but they are important for another reason: They get to the heart of general issues of human development that have intrigued philosophers and scientists for centuries.
Second, we may have a problem with participant dropout because it is difficult to keep a group of research participants intact over the course of a longitudinal study. Participants may move, lose interest, or die. Participant dropout can result in two different outcomes. g. the surviving participants are the ones who were the most healthy at the beginning of the study). g. the surviving participants may have been those who initially less healthy). The third problem with longitudinal designs is that our ability to apply the results to other groups is limited.
Despite the confounding of age and cohort and the limitation of being able to identify only age differences, cross-sectional designs dominate the research literature in gerontology. Why? The reason is a pragmatic one: Because all the measurements are obtained at one time, cross-sectional research can be conducted more quickly and inexpensively than research using other designs. In addition, one particular variation of cross-sectional designs is used the most: the extreme age groups design. Suppose you want to investigate whether people’s ability to remember items at the grocery store differs with age.