A Test of the Measurement Equivalence of the Revised Job Diagnostic Survey: Past Problems and Current Solutions

The measurement equivalence of the revised Job Diagnostic Survey (JDS) was studied across samples from five worker populations. Samples included workers at a printing plant, engineers, nurses and nurses' aides, dairy employees, and part-time workers. Data were analyzed according to Jöreskog's model for simultaneous factor analysis in several populations (SIFASP), revealing the five factors contained in Hackman and Oldham's theory of job characteristics. A sixth factor also appeared that apparently resulted from the two different formats used on the instrument. When the data from each group were analyzed separately by principal axes factor analysis, three-, four-, and five-factor solutions appeared. To explain these inconsistencies, a Monte Carlo simulation was conducted. Matrices representing the a priori JDS factor loadings and a hypothetical, lengthened JDS with twice the number of items per factor were used in the simulation with three sample sizes (N s = 75, 150, and 900). Results suggested that for scales like the JDS, which has only a few items per factor, sample sizes larger than those typically recommended are needed to consistently recover the true underlying structure. The simulation results support our conclusions that the SIFASP solution is preferable to the principal axes solution and that the JDS provides measurement equivalence across worker populations.