Multiple imputation regression analysis (imputation phase)

TITLE:

section 8.15 data analysis example 2: multiple regression (diagnostic phase);

DATA:

file = employee.dat;

VARIABLE:

! id = employee id;

! age = employee age;

! tenure = years on the job;

! female = gender (0 = male, 1 = female);

! wbeing = psychological well-being;

! jobsat = job satisfaction

! jobperf = job performance;

! turnover = turnover intentions (0 = no, 1 = yes);

! iq = iq score;

names = id age tenure female wbeing jobsat jobperf turnover iq;

! select variables for analysis;

usevariables = age - iq;

! specify missing value code;

missing = all (-99);

ANALYSIS:

! specify saturated imputation model;

type = basic;

! random number seed for mcmc algorithm;

bseed = 73293;

DATA IMPUTATION:

! incomplete variables to be imputed;

impute = wbeing jobsat;

! number of imputed data sets;

ndatasets = 50;

! filename prefix for imputed data sets;

save = employeeimp*.dat;

! number of between-imputation iterations;

thin = 100;

OUTPUT:

! tech8 gives the potential scale reduction factor convergence diagnostic;

tech8;

Questions or suggestions? Email Craig Enders