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DETECTING JOB DIFFERENCES: A MONTE CARLO STUDY 1
Author(s) -
ARVEY RICHARD D.,
MAXWELL SCOTT E.,
GUTENBERG RHONDA L.,
CAMP CAMERON
Publication year - 1981
Publication title -
personnel psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.076
H-Index - 142
eISSN - 1744-6570
pISSN - 0031-5826
DOI - 10.1111/j.1744-6570.1981.tb01425.x
Subject(s) - statistics , monte carlo method , covariance , psychology , univariate , analysis of variance , dimension (graph theory) , statistical analysis , econometrics , statistical power , analysis of covariance , repeated measures design , statistical significance , mathematics , combinatorics , multivariate statistics
A monte carlo computer study was conducted where the statistical power of the univariate repeated measures ANOVA design proposed by Arvey and Mossholder (1977) to detect job differences was investigated. Also investigated was the relative value and usefulness of omega‐squared estimates to indicate job similarities and differences. Job profile means and covariance structures were generated by using data from six relatively similar jobs and six dissimilar jobs based on Position Analysis Questionnaire (PAQ) data bank information. Different combinations of job differences (4 conditions), number of job raters (2 conditions), and violations of statistical assumptions (3 conditions) were generated (1000 sets for each of the 24 combinations) and each data set analyzed using the ANOVA design. Results indicate that testing for statistical significance is not as useful in determining job differences as examining the omega‐squared estimates. Specifically, the omega‐squared estimates for the interaction of the Jobs × Dimension effect is a relatively sensitive and stable indicator of job differences regardless of the number of raters and violations of the statistical assumptions.