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Structural equation modeling (SEM) is a very general approach to analyzing data in the presence of measurement error and complex causal relationships. In this tutorial, we describe SEM, with special attention to exploratory factor analysis, confirmatory factor analysis, and multiple indicator multiple cause modeling. The tutorial is motivated by a problem of symptom overlap routinely faced by clinicians and researchers, in which symptoms or test results are common to two or more co-occurring conditions. As a result of such overlap, diagnoses, treatment decisions, and inferences about the effectiveness of treatments for these conditions can be biased. This problem is further complicated by increasing reliance on patient-reported outcomes, which introduces systematic error based on an individual's interpretation of a test questionnaire. SEM provides flexibility in handling this type of differential item functioning and disentangling the overlap. Scales and scoring approaches can be revised to be free of this overlap, leading to better care. This tutorial uses an example of depression screening in multiple sclerosis patients in which depressive symptoms overlap with other symptoms, such as fatigue, cognitive impairment, and functional impairment. Details of how MPlus (Muthén & Muthén, Los Angeles, CA, USA) software can be used to address the symptom overlap problem, including data requirements, code and output are described in this tutorial.
Gunzler et al. (Thu,) studied this question.