Research Methods in Adult Development

  • John C. Cavanaugh
  • Susan Krauss Whitbourne
Part of the The Springer Series in Adult Development and Aging book series (SSAD)

Abstract

The study of adult development is grounded in the principles of scientific inquiry. Information concerning aging is gathered in the same ways as in other sciences, such as biology, psychology, sociology, anthropology, and the medical and allied health fields. Adult developmentalists have the same problems as other scientists: finding appropriate control or comparison groups, limiting generalizations to the types of groups included in the research, and finding adequate means of measurement (Kausler, 1982).

Keywords

Covariance Sonal Clarification 

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References

  1. Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11, 145–171.PubMedCrossRefGoogle Scholar
  2. Bentler, P. M. (1992). EQS structural equation program manual. Los Angeles: BMDP Statistical Software.Google Scholar
  3. Byrne, B. M. (1995). One application of structural equation modeling from two perspectives: Exploring the EQS and LISREL strategies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 138–157). Thousand Oaks, CA: Sage.Google Scholar
  4. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research on teaching. In N. L. Gage (Ed.), Handbook of research on teaching (pp. 171–246). Chicago: Rand McNally.Google Scholar
  5. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally.Google Scholar
  6. Hertzog, C., & Dixon, R. A. (1996). Methodological issues in research on cognition and aging. In F. Blanchard-Fields & T. M. Hess (Eds.), Perspectives on cognitive change in adulthood and aging (pp. 66–121). New York: McGraw-Hill.Google Scholar
  7. Hertzog, C., Hultsch, D. F., & Dixon, R. A. (1989). Evidence for the convergent validity of two self-report metamemory questionnaires. Development Psychology, 25, 687–700.CrossRefGoogle Scholar
  8. Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 1–15). Thousand Oaks, CA: Sage.Google Scholar
  9. Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76-99). Thousand Oaks, CA: Sage.Google Scholar
  10. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: User’s reference guide. Chicago: Scientific Software.Google Scholar
  11. Kausler, D. H. (1982). Experimental psychology and human aging. New York: John Wiley & Sons.Google Scholar
  12. Labouvie, E. W. (1980). Identity versus equivalence of psychological measures and constructs. In L. W. Poon (Ed.), Aging in the 1980’s: Psychological issues (pp. 493–502). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  13. Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement, design, and analysis: An integrated approach. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  14. Schaie, K. W. (1965). A general model for the study of developmental change. Psychological Bulletin, 64, 92–107.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • John C. Cavanaugh
    • 1
  • Susan Krauss Whitbourne
    • 2
  1. 1.University of West FloridaPensacolaUSA
  2. 2.Department of PsychologyUniversity of Massachusetts at AmherstAmherstUSA

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