Maximum likelihood regression analysis with auxiliary variables

TITLE:

section 5.13 data analysis example 2: multiple regression with auxiliary variables;

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;

usevariables = wbeing jobsat jobperf;

auxiliary = (m) turnover iq; ! auxiliary variables;

missing are all (-99);

ANALYSIS:

estimator = ml;  ! FIML (the default);

MODEL:

jobperf on 

  wbeing (b1) 

  jobsat (b2); ! (b1) and (b2) are labels for wald test;

wbeing jobsat; ! include incomplete predictors;

MODEL TEST:

b1 = 0; ! wald test that both coefficients = 0;

b2 = 0;

OUTPUT:

standardized(stdyx) sampstat;

Questions or suggestions? Email Craig Enders