KNOW ABOUT WEIGHT-LOSS EFFECTIVENESS OVER TIME
Cross-lagged regressions weight-loss effectiveness. To assess whether any of the individual-difference or relationship variables predicted change over time in either weight-loss effectiveness or body satisfaction, the standard multiple regression approach was used. For example, body satisfaction at time 4 (the final measurement) was regressed on both body satisfaction at time 1 and one predictor variable (relationship satisfaction, self-esteem, perceived support, etc.). None of the analyses produced significant regression coefficients for the predictor variables, suggesting that none of these independent variables predicted change over time in weight or body satisfaction.
Growth curve analysis. A different and more subtle approach to assessing change over time is the use of growth curve analysis using Structural Equation Modeling. Before variables predicting change over time can be introduced into models, it is necessary to establish that rate of change significantly varies across individuals. Unfortunately, this proved not to be the case for either body weight or body satisfaction – the variances of the rate of change latent variable were not significant (z’s < 1.0). It seem plausible, for example, that those with low self-esteem may produce more marked or chaotic changes over time in body satisfaction and weight, whereas those with higher self-esteem are more stable and linear over time. To test this idea, we ran the growth curve analyses again, and tested for cubic effects by setting the rate of change paths as 0, 1, 8, and 27. However, once again, the variances of the rate of change latent variables were not significant for either body satisfaction or weight (z’s < 1.0).