Fig 1.
Illustration of identical results plotted on (a) a moderation graph, and (b) a response surface.
In the moderation graph, X is the focal variable, Y is the moderator variable, and Z is the dependent variable. In the response surface graph, X is the first focal variable, Y is the second focal variable, and Z is the dependent variable.
Fig 2.
Hypothetical response surfaces supporting the hypotheses that (a) stability is optimal, (b) moderate growth is optimal, or (c) maximal growth is optimal.
Table 1.
Demographic Characteristics of Participants (N = 1,725).
Fig 3.
Response surfaces for polynomial analyses of sociality and well-being.
Fig 4.
Response surfaces for polynomial analyses of agency and well-being.
Fig 5.
Response surfaces for polynomial analyses of neuroticism and well-being.
Fig 6.
Response surfaces for polynomial analyses of conscientiousness and well-being.
Fig 7.
Scatterplots of Trait Change (X axis) and Well-Being (Y axis) among participants in the lowest trait quartile at Time 1.
The lines of best fit are from lowess regressions using the Epanechnikov kernel with 75% of points fitted.
Table 2.
Trait and Well-Being Levels of Participants (N = 1,725).
Table 3.
Inter-Correlations of Predictor and Outcome Variables.
Table 4.
Trait Change Analyses: Estimates of the Slopes and Curvature Coefficients of Line of Stability and Its Orthogonal.
Table 5.
Estimates and confidence intervals of displacement and parallelism between the lines of stability and optimality.
Fig 8.
Scatterplots of Standardized Trait Change (X axis) and Well-Being (Y axis) with outliers removed.
Lines represent the results of piecewise regression analyses.
Table 6.
Trait Change Analyses: Piecewise Regression with Moderate Changers and Large Changers.