{"id":3857,"date":"2025-04-09T15:38:37","date_gmt":"2025-04-09T15:38:37","guid":{"rendered":"https:\/\/education.ufl.edu\/research-evaluation-methods\/?p=3857"},"modified":"2026-04-06T20:08:16","modified_gmt":"2026-04-06T20:08:16","slug":"dr-walter-leite-provides-tutorial-on-sensitivity-analysis-methods","status":"publish","type":"post","link":"https:\/\/education.ufl.edu\/research-evaluation-methods\/2025\/04\/09\/dr-walter-leite-provides-tutorial-on-sensitivity-analysis-methods\/","title":{"rendered":"Dr. Walter Leite Provides Tutorial on Sensitivity Analysis Methods"},"content":{"rendered":"

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Dr. Walter Leite will provide a tutorial on sensitivity analysis methods for structural equation modeling at the Modern Modeling Methods Conference, June 26-28, University of Connecticut.<\/p>\n

A Tutorial on Methods for Sensitivity Analysis to Omitted Confounders in Structural Equation Modeling<\/strong><\/h2>\n

A few sensitivity analysis methods for structural equation modeling (SEM) have been developed recently based on using phantom variables to represent a potential omitted confounder. Sensitivity analysis is an important tool to probe the boundaries of the conclusions of a research study. However, these methods have not been widely disseminated in the SEM user community. We will provide a tutorial of methods for sensitivity analysis in SEM implemented in the SEMsens package of the R Statistical Software.\u00a0 The sensitivity analysis shown in the tutorial is for a complex SEM <\/span>of the relationship between job satisfaction and turnover. <\/span>The results of the sensitivity analysis show how conclusions about explanatory theories may be susceptible to unobserved relationships with omitted confounders.<\/span><\/p>\n

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