title: Assessing Omitted Confounder Bias in Multilevel Mediation Models.
abstract: To draw valid inference about an indirect effect in a mediation model, there must be no omitted confounders. No omitted confounders means that there are no common causes of hypothesized causal relationships. When the no-omitted-confounder assumption is violated, inference about indirect effects can be severely biased and the results potentially misleading. Despite the increasing attention to address confounder bias in single-level mediation, this topic has received little attention in the growing area of multilevel mediation analysis. A formidable challenge is that the no-omitted-confounder assumption is untestable. To address this challenge, we first analytically examined the biasing effects of potential violations of this critical assumption in a two-level mediation model with random intercepts and slopes, in which all the variables are measured at Level 1. Our analytic results show that omitting a Level 1 confounder can yield misleading results about key quantities of interest, such as Level 1 and Level 2 indirect effects. Second, we proposed a sensitivity analysis technique to assess the extent to which potential violation of the no-omitted-confounder assumption might invalidate or alter the conclusions about the indirect effects observed. We illustrated the methods using an empirical study and provided computer code so that researchers can implement the methods discussed.
authors.affiliation: a School of Psychology , Georgia Institute of Technology.
authors.forename: Davood
authors.initials: D
authors.lastname: Tofighi
authors.affiliation: b Department of Management, Mendoza College of Business , University of Notre Dame.
authors.forename: Ken
authors.initials: K
authors.lastname: Kelley
mesh_terms: D000465:Algorithms; D035841:Behavioral Research / Q000379:methods; D016000:Cluster Analysis; D015986:Confounding Factors, Epidemiologic; D015233:Models, Statistical; D055361:Multilevel Analysis / Q000379:methods*
keywords: Mediation analysis; bias; confounder; omitted variable; sensitivity analysis