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. Author manuscript; available in PMC: 2023 Feb 8.
Published in final edited form as: Neuropsychology. 2021 Sep 27;35(8):889–903. doi: 10.1037/neu0000775

Table 3.

Factor Mixture Model (FMM) Specifications

Model Description
FMM-1 Class-varying factor means (i.e., different EF performance); class-invariant factor loadings, intercepts, and residuals (i.e., the same EF structure); factor (co) variance fixed to zero (i.e., no within-class variability).
FMM-2 Class-varying factor means and (co)variance; class-invariant factor loadings, intercepts, and residuals (i.e., strict invariance).
FMM-3 Class-varying factor means and (co) variance; class-invariant factor loadings and intercepts; class-varying residuals (i.e., scalar invariance).
FMM-4 Class-varying factor (co)variance; class-invariant factor loadings; class-varying intercepts and residuals (i.e., metric invariance); latent factor means fixed to zero.
FMM-5 Class-varying factor loadings, intercepts and residuals (i.e., configural invariance); latent factor means and (co) variance fixed to zero.

Note: Each model was systematically specified and evaluated across classes and factors.