Maximum Likelihood Analysis of Linear Mediation Models with Treatment–Mediator Interaction

Kai Wang ()
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Kai Wang: The University of Iowa

Psychometrika, 2019, vol. 84, issue 3, No 4, 719-748

Abstract: Abstract This research concerns a mediation model, where the mediator model is linear and the outcome model is also linear but with a treatment–mediator interaction term and a residual correlated with the residual of the mediator model. Assuming the treatment is randomly assigned, parameters in this mediation model are shown to be partially identifiable. Under the normality assumption on the residual of the mediator and the residual of the outcome, explicit full-information maximum likelihood estimates of model parameters are introduced given the correlation between the residual for the mediator and the residual for the outcome. A consistent variance matrix of these estimates is derived. Currently, the coefficients of this mediation model are estimated using the iterative feasible generalized least squares (IFGLS) method that is originally developed for seemingly unrelated regressions (SURs). We argue that this mediation model is not a system of SURs. While the IFGLS estimates are consistent, their variance matrix is not. Theoretical comparisons of the FIMLE variance matrix and the IFGLS variance matrix are conducted. Our results are demonstrated by simulation studies and an empirical study. The FIMLE method has been implemented in a freely available R package iMediate.

Keywords: full-information likelihood; estimation; identification; mediation analysis; sensitivity analysis (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11336-019-09670-9

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