I/O psychology is, from one viewpoint, a pragmatic psychology. Much research is devoted to the prediction of behavior in organizations. For example, what are the psychological factors that lead a group of employees to attempt to change a dissatisfying or stressful situation by unionizing? What factors go into decisions to quit one's job? What psychological traits best predict job performance across a wide range of occupations? Effort typically goes into identifying measures that accurately predict these types of outcomes because the outcomes themselves are important. Indeed, one popular method of selecting employees, biographical data, relies primarily on empirical keying to score individual items according to the options that best predict job success. Perhaps as a result of a tension to identify measures that predict our chosen behaviors well, we have not used the multitrait-multimethod (MTMM) approach to triangulate on theoretical constructs, preferring instead to use the measures that best predict the behaviors. There are counterexamples, a few of which we describe following, but most I/O psychologists would probably admit that at some level we are driven by pragmatic concerns of prediction (Hulin, 2001).
Some in the field have addressed these issues. Dunnette (1966), an I/O psychologist, argued points quite similar to Campbell and Fiske's (1959)
in his provocative "fads, fashions, and folderol" article. Dunnette warned against researchers whose findings or theory depended on one operation. Researchers with one method are limited; their findings are equally limited. Their research is method— rather than problem—oriented; disentangling method and construct variance may be impossible if results are based on monomethod studies. If the methods generate systematic error variance not related to the construct being assessed but correlated with other responses, the theory and its evidence may be misleading. Through demonstration that multiple methods and measures independently converge on the same conclusions, researchers can avoid ascribing errors in measurement to effects at the construct level (Campbell & Fiske, 1959).
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