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Structural equation modeling in management research: a guide for improved analysis

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>2009
<mark>Journal</mark>The Academy of Management Annals
Issue number1
Number of pages62
Pages (from-to)543-604
Publication StatusPublished
<mark>Original language</mark>English


A large segment of management research in recent years has used structural equation modeling (SEM) as an analytical approach that simultaneously combines factor analysis and linear regression models for theory testing. With this approach, latent variables (factors) represent the concepts of a theory, and data from measures (indicators) are used as input for statistical analyses that provide evidence about the relationships among latent variables. This chapter first provides a brief introduction to SEM and its concepts and terminology. We then discuss four issues related to the measurement component of such models, including how indicators are developed, types of relationships between indicators and latent variables, approaches for multidimensional constructs, and analyses needed when data from multiple time points or multiple groups are examined. In our second major section, we focus on six issues related to the structural component of structural equation models, including how to examine mediation and moderation, dealing with longitudinal and multilevel data, issues related to the use of control variables, and judging the adequacy of models and latent variable relationships. We conclude with a set of recommendations for how future applications of SEM in management research can be improved.