Table 4. Fit indices and parameter estimates from the limited information multilevel latent growth models. Coefficient Between-level Within-level Fit Indices Effect t value Effect t value Intrinsic Motivation Means Intercept 2.21 25.04 χ2 = 86.05 df = 17 Slope .07 .49 p < .01 Quadratic -.04 -.52 NFI = .920 Cubic .01 .56 CFI = .926 Variances RMSEA = .146 Intercept .14 .78 .16 2.67 90% CI = Slope .12 .35 .42 3.43 (.118, .174) Quadratic .02 .32 .09 4.02 Cubic .00 .23 .01 4.11 Identified Regulation Means Intercept .68 7.36 χ2 = 35.23 df = 17 Slope 1.85 13.48 p < .01 Quadratic -.66 9.97 NFI = .984 Cubic .07 8.18 CFI = .992 Variances RMSEA = .049 Intercept .16 .71 .15 2.27 90% CI = Slope .06 .16 .08 .68 (.010, .083) Quadratic .00 .00 .01 .52 Cubic .00 .02 .00 .03 Introjected Regulation Means Intercept 2.33 24.98 χ2 = 59.33 df = 17 Slope -.19 1.76 p < .01 Quadratic .14 2.67 NFI = .968 Cubic - .03 3.65 CFI = .973 Variances RMSEA = .108 Intercept .15 .79 .48 7.01 90% CI = Slope .00 .00 .29 2.81 (.080, .137) Quadratic .01 .14 .02 .95 Cubic .00 .24 .00 .06 External Regulation Means Intercept 1.83 19.06 χ2 = 51.47 df = 17 Slope .10 .83 p < .01 Quadratic -.02 .40 NFI = .973 Cubic .00 .33 CFI = .977 Variances RMSEA = .107 Intercept .16 .96 .26 4.34 90% CI = Slope .16 .60 .00 .00 (.080, .137) Quadratic .03 .52 .02 .99 Cubic .00 .00 .01 1.01 Amotivation Means Intercept .60 6.82 χ2 = 67.07 df = 26 Slope .51 7.44 p < .01 Quadratic -.73 53.05 NFI = .960 Variances CFI = .971 Intercept .11 1.02 .43 11.67 RMSEA = .079 Slope .06 1.00 .12 6.26 90% CI = Quadratic .01 1.00 .09 4.52 (.059, .101)