Introductory And Intermediate Growth Models - Mplus

Growth Models WithCategorical Outcomes189Growth Model With Categorical Outcomes• Individual differences in development of probabilities over time• Logistic model considers growth in terms of log odds (logits), e.g.⎡ P(uti= 1| η0i, η1i, η , x ) ⎤2i ti2(1) log⎢⎥ = η0i+ η1i⋅(xti− c)+ η2i⋅(xti− c)⎣ P (uti= 0| η0i, η1i, η2i, xti) ⎦for a binary outcome using a quadratic model with centering at timec. The growth factors η 0i , η 1i , and η 2i are assumed multivariatenormal given covariates,(2a) η 0i = α 0 + γ 0 w i + ζ 0i(2b) η 1i = α 1 + γ 1 w i + ζ 1i(2c) η 2i = α 2 + γ 2 w i + ζ 2i19095