Prior research has demonstrated the importance of distinguishing among foci and bases of commitment. Foci of commitment are the individuals and groups to whom an employee is attached, and bases of commitment are the motives engendering attachment. This study uses distinctions among foci and bases of commitment to develop four profiles of commitment, and examines the extent to which differences in these patterns predict other variables. Cluster analysis of 440 employees suggests the following profiles: (1) The Locally Committed (employees who are attached to their supervisor and work group), (2) the Globally Committed (who are attached to top management and the organization), (3) the Committed (who are attached to both local and global foci), and (4) the Uncommitted (who are attached to neither local nor global foci). The profiles are differentially related to intent to quit, job satisfaction, prosocial organizational behaviors, and certain demographic and contextual variables. Implications o f these findings for theory and practice are discussed.

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The results of exploratory studies of the factor structure of the Job Diagnostic Survey (JDS) have been ambiguous: Some concluded it measures a single underlying factor, others claimed its dimensionality varies across different populations, and still others found support for the a priori factor structure. This study performed confirmatory tests of the factor structure of the JDS. Data were obtained from 2,028 National Guard employees. Neither the Hackman-Oldham nor single-factor models provided acceptable fit until construct-irrelevant method variance factors were added. After incorporating method factors, the confirmatory factor analyses supported Hackman and Oldham's a priori structure; however, when a different goodness-of-fit measure was used, the results indicated that a general factor model was more parsimonious. Because the primary effect of using different response formats (especially negative wording) was the introduction of substantial amounts of method variance into the JDS item scores, modifications of the JDS are suggested.We would like to thank two anonymous reviewers for their very helpful comments on this article.

Path analysis or structural equation modeling is a technique for testing the consequences of proposed causal relationships among a set of variables. The technique rests on specific procedures and important assumptions (uncorrelated residuals, one-way causality, linearity, additivity, interval measures). These assumptions, along with the problem of multicollinearity, are discussed and specific techniques offered to deal with them. Path analysis studies from the industrial/organizational psychology literature are reviewed to illustrate the specific consequences of disregarding these assumptions and procedures. The relationship between cross-lagged correlation and path analysis is explored, particularly with regard to the differing but complementary purposes of each. Finally, the problems due to both shared and random measurement error and the application of path analysis to experiments are discussed.Path analysis is a technique that uses ordinary least squares regression to help the researcher test the consequences of proposed causal relationships among a set of variables. Used most specifically, path analysis can test an a priori causal hypothesis against a set of observed correlations. At the most general level, path analysis can be used to test a number of alternative causal sequences against one another. In any application of path analysis, very specific and important assumptions underlie the technique; if any of these assumptions are violated, the causal inferences will very possibly be incorrect.This article explores these assumptions in detail and offers specific advice about how to deal with them. Our purpose is not to add to the techniques of path analysis, but rather to translate the procedures, problems, and possible solutions into operational terms that can be more easily understood and used by industrial/organizational researchers. In doingThe authors would like to thank Tony Greenwald and two anonymous reviewers for very useful comments.

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