Modeling bivariate change in individual differences: Prospective associations between personality and life satisfaction

Hounkpatin, Hilda Osafo, Boyce, Christopher J., Dunn, Graham and Wood, Alex M. (2018) Modeling bivariate change in individual differences: Prospective associations between personality and life satisfaction. Journal of Personality and Social Psychology, 115 (6), e12-e29. (doi:10.1037/pspp0000161).

Abstract

A number of structural equation models have been developed to examine change in 1 variable or the longitudinal association between 2 variables. The most common of these are the latent growth model, the autoregressive cross-lagged model, the autoregressive latent trajectory model, and the latent change score model. The authors first overview each of these models through evaluating their different assumptions surrounding the nature of change and how these assumptions may result in different data interpretations. They then, to elucidate these issues in an empirical example, examine the longitudinal association between personality traits and life satisfaction. In a representative Dutch sample (N = 8,320), with participants providing data on both personality and life satisfaction measures every 2 years over an 8-year period, the authors reproduce findings from previous research. However, some of the structural equation models overviewed have not previously been applied to the personality-life satisfaction relation. The extended empirical examination suggests intraindividual changes in life satisfaction predict subsequent intraindividual changes in personality traits. The availability of data sets with 3 or more assessment waves allows the application of more advanced structural equation models such as the autoregressive latent trajectory or the extended latent change score model, which accounts for the complex dynamic nature of change processes and allows stronger inferences on the nature of the association between variables. However, the choice of model should be determined by theories of change processes in the variables being studied.

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Contributors

Author: Hilda Osafo Hounkpatin

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