Including Auxiliary Variables with FIML in Mplus

Auxiliary variables are variables that correlate with missingness/dropout and/or with the outcome variables that have missing values. Dr. Christian Geiser shows how a longitudinal cross-lagged panel model can be analyzed in Mplus using FIML with auxiliary variables.

#Mplus #Mplusforbeginners #statistics #missingvalues #CFA #SEM

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Dr. Christian Geiser is a quantitative psychologist, author of two books on Mplus, and a leader in the development of latent variable techniques for complex data. With his accessible books and sought-after workshops, he has helped thousands of researchers and students around the world to achieve their analytic goals.

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