Multiple Imputation in Mplus

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• Data set containing scores from 480 employees on eight workrelated variables • Variables: Age, gender, job tenure, IQ, psychological wellbeing, job satisfaction, job performance, and turnover intentions • 33% of the cases have missing well-being scores, and 33% have missing satisfaction scores • The mechanism is MCAR because the data are missing by design
• With some planning, the same set of imputations can serve as input data for many different analyses • At a minimum, the imputation process should include all effects that are of interest in the subsequent analysis phase (e.g., main effects, interactions, non-linear relations) • Imputation should also incorporate auxiliary variables that predict missingness or are correlated with the analysis variables
Page 4 and 5: • TITLE (optional) • DATA (same
Page 6 and 7: • All variables listed on the USE
Page 8 and 9: • Specifies characteristics of th
Page 10 and 11: DATA:! file = employee.dat;! VARIAB
Page 12 and 13: • Mplus saves each imputed data s
Page 14 and 15: • Mplus fully automates the analy
Page 16 and 17: SAMPLE STATISTICS! ! NOTE: These ar
Page 18 and 19: • The Wald statistic (a chi-squar
Page 20 and 21: • Because the continuous are cent
Page 22 and 23: ! R-SQUARE! ! ! Observed Two-Tailed
Page 24 and 25: • After creating the complete dat
Page 26: • Tabular presentations of missin

Multiple Imputation in Mplus

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