Research That Matters (January 17 - 20, 2008)


Directors Room (Omni Shoreham)

A Systematic Review of Structural Equation Modeling in Social Work Research

Brian Perron, PhD, University of Michigan-Ann Arbor, Baorong Guo, PhD, University of Missouri-Saint Louis, and David F. Gillespie, PhD, Washington University in Saint Louis.

Background and Purpose: Structural equation modeling (SEM) is serving an increasingly important role in developing knowledge for the social work profession. Numerous advances have made the software more user-friendly, enabling users to conduct an analysis without fully understanding the underlying assumptions or the implications from their analytic decisions. Unlike other fields, there have not been any published reviews in social work research that systematically describe and critique the use of SEM. This study systematically reviewed how SEM is used in social work research and the extent to which it reflects best practices.

Methods: This systematic review targeted the top-ten ranked journals that were considered to publish the highest quality social work research based on a national survey of social work faculty. Thirty-two articles from top-ranked social work journals published from 2001 to 2007 were examined. All author affiliations were examined and studies were excluded if the first or second author did not have an affiliation with a department or school of social work, social welfare, or social service administration at the time of publication.

Results: Eight of the ten journals under review published SEM research by social workers since 2001. The SEM studies in these journals included 20 confirmatory factor analyses, 10 full SEM models, one path analysis and one latent growth curve model. Of the different types of SEM, the most commonly used was confirmatory factor analysis. Strengths of the research included examining models and measures that have never been empirically tested before or generating new insights into old topics through the use of SEM. Weaknesses included significant model modifications without theoretical justification or substantive interpretations. The results provide evidence for a publication bias for positive findings in SEM research.

Implications: Social work researchers employing SEM should use modification indices to inform potential problems with the theory, data and model, and any ensuing modifications must be regarded as exploratory analysis instead of validation of the theoretical model. Greater attention to full reporting is needed to allow for thorough assessment of SEM research. Social work researchers using SEM should also consider many of its options that are not being used to its fullest extent. For example, social work theories often imply reciprocal or feedback relationships. Although close attention must be paid to issues of model identification, these can be specified and tested as non-recursive relationships, which were not present in any of the studies reviewed.