Chapter 11: Missing Data Modeling and Bayesian Analysis

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Example	View output	Download input	Download data	View Monte Carlo output	Download Monte Carlo input
11.1: Growth model with missing data using a missing data correlate	ex11.1	ex11.1.inp	ex11.1.dat	mcex11.1	mcex11.1.inp
11.2: Descriptive statistics and graphics related to dropout in a longitudinal study	ex11.2	ex11.2.inp	ex11.2.dat	mcex11.2	mcex11.2.inp
11.3: Modeling with data not missing at random (NMAR) using the Diggle-Kenward selection model	ex11.3	ex11.3.inp	ex11.3.dat	mcex11.3	mcex11.3.inp
11.4: Modeling with data not missing at random (NMAR) using a pattern-mixture model	ex11.4	ex11.4.inp	ex11.4.dat	mcex11.4	mcex11.4.inp
11.5: Multiple imputation for a set of variables with missing values	ex11.5	ex11.5.inp	ex11.5.dat	mcex11.5	mcex11.5.inp
11.6: Multiple imputation followed by the estimation of a growth model using maximum likelihood	ex11.6	ex11.6.inp	ex11.6.dat	mcex11.6	mcex11.6.inp
11.7: Multiple imputation of plausible values using Bayesian estimation of a growth model	ex11.7	ex11.7.inp	ex11.7.dat	mcex11.7	mcex11.7.inp
11.8: Multiple imputation using a two-level factor model with categorical outcomes followed by the estimation of a growth model (part 1)	ex11.8part1	ex11.8part1.inp	ex11.8.dat	mcex11.8part1	mcex11.8part1.inp
11.8: Multiple imputation using a two-level factor model with categorical outcomes followed by the estimation of a growth model (part 2)	ex11.8part2	ex11.8part2.inp	ex11.8implist.dat	none	none
11.8: Multiple imputation using a two-level factor model with categorical outcomes followed by the estimation of a growth model (part 3)	ex11.8part3	ex11.8part3.inp	ex11.8implist.dat	none	none

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