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Analytic Strategies for the Study of Adaptation to Major Life Events: Making the Most of Large-Scale Longitudinal Surveys

  • Frank J. Infurna
  • Denis Gerstorf
  • Nilam Ram
  • Jutta Heckhausen
Chapter

Abstract

Longitudinal surveys are essential for studying developmental change across the lifespan and have been instrumental in contributing to a better understanding of how people change from childhood through adolescence, adulthood, and into old age. This chapter reviews some of the strengths of longitudinal surveys for studying the adaptation and self-regulation of individuals who experience major life events across their adult lives. First, large national longitudinal surveys are highly instructive and necessary in order to prospectively collect data on sufficiently large sub-samples of people who are confronted with certain life events as the survey unfolds. Second, having access to prospective data from such sub-samples enables us to thoroughly track developmental changes in the nature, correlates, and outcomes of adaptation and self-regulation with the experience of major life events. Third, we discuss how multi-phase growth curve models can be used to distinguish between pre-event changes, reaction, and adaptation in order to examine individual differences in each of these phases and to explore individual and contextual variables that may serve as risk- or protective factors. Finally, we consider how embedded micro-longitudinal study designs and propensity score matching techniques may increase the advantages of panel surveys for studying adaptation and self-regulation across adulthood. In sum, combining the strengths of longitudinal surveys with contemporary methods of analysis can put researchers in a position to advance their knowledge of how life events shape developmental change trajectories across the entire lifespan.

Keywords

Propensity Score Developmental Change Propensity Score Match Longitudinal Survey Major Life Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Fachmedien Wiesbaden 2016

Authors and Affiliations

  • Frank J. Infurna
    • 1
  • Denis Gerstorf
    • 2
  • Nilam Ram
    • 3
  • Jutta Heckhausen
    • 4
  1. 1.ArizonaUSA
  2. 2.BerlinDeutschland
  3. 3.PhiladelphiaUSA
  4. 4.IrvineUSA

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