A Bayesian approach to crossed-random-effects mediation analysis for zero-inflated mediators and binary outcomes

In crossed random effects designs, observations are nested in the combination of two random factors, e.g., subjects and stimuli. Such designs are popular in experimental research in social sciences. Crossed random effects models (CREM) can accommodate the random effects of both subjects and stimuli. Based on academic vocabulary research, the model of interest in the current study was a mediation model with crossed random effects, a zero-inflated mediator, and a binary outcome. With maximum likelihood estimation (MLE), the mediation model could not converge, which was consistent with the previous finding on analyzing CREM with MLE (Huang & Anderson, 2020). Therefore, the current study investigated whether Bayesian estimation can be a viable alternative as suggested by previous research. The simulation results indicated that Bayesian estimates were essentially unbiased and precise. There were only two out of 180 models did not converge; future studies can increase the iterations or use an informative prior to resolving the non-convergence issue. Moreover, a Bayesian method was developed to compute the mediation effect; the results of the Bayesian method closely aligned with the bootstrapping results. Lastly, the application of Bayesian estimation was demonstrated in an academic vocabulary study.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Educational Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Pan, Pei-Yu (Marian)

Contributors dc:contributor

• Anderson, Carolyn Jane
• Anderson, Richard C
• Jiang, Gabriella

• Bayesian estimation, mediation, crossed random effects, zero-inflated mediator, academic vocabulary

Statement dc:rights

• (Copyright 2021 Pei-Yu (Marian) Pan)

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/110639

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/110639