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Chapter 11: Missing Data Modeling and Bayesian Analysis

Download all Chapter 11 examples

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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