Individual growth curve
models were developed for
multilevel analysis and specifically designed for
exploring longitudinal data on individual changes over time.23 Using this approach, we applied the MIXED procedure in SAS (SAS Institute) to account for the random effects of repeated measurements.24 To specify the correct
model for our individual growth curves, we compared a series of MIXED
models by evaluating the difference in deviance between nested
models.23 Both fixed quadratic and cubic MIXED
models fit our data well, but we selected the fixed quadratic MIXED
model because the addition of a cubic time term was not statistically significant based on a log - likelihood ratio test.
Multilevel autoregressive
model with restricted maximum likelihood estimator was utilized in order to
explore cross-lagged associations between schema modes and personality psychopathology scores over subsequent measurements at baseline, 6, 12, 18, 24 and 36 months.