Amanda Kay Montoya


I am an Assistant Professor at UCLA in the Department of Psychology - Quantitative Area. I received my PhD in Quantitative Psychology from the Ohio State University in 2018. My primary adviser was Dr. Andrew Hayes. I completed my M.A. in Psychology and my M.S. in Statistics at Ohio State in 2016. I graduated from the University of Washington with a B.S. in Psychology and a minor in Mathematics in 2013. My research interests include mediation, moderation, conditional process models, structural equation modeling, and meta-science.

Mediation, Moderation, and Conditional Process Analysis
Global School of Empirical Research Methods
08/27/18 - 08/31/18


Contact me to schedule a conference!
Mediation, Moderation, and Conditional Process Analysis I
Global School Empirical Research Methods (GSERM)
University of Ljubjana 
January 16 - 20, 2023
Teaching with a Focus on Diversity, Equity, and Inclusion
Quantitative Brownbag
9:30 - 10:30 AM PST
November 28, 2022 



Submitted to Frontiers in Psychology, coauthored by my graduate student Tristan Tibbe (UCLA), we introduce two bias-corrected bootstrap confidence interval methods for use with the indirect effect, describing their relation to bias assumptions made by the current bias-corrected bootstrap confidence interval and comparing their performance to existing methods used in the area of mediation analysis.

Screen Shot 2021-07-12 at 8.38.25 PM.png

Currently in press at Collabra, in collaboration with Dr. William Leo Donald Krenzer (Duke University) and my graduate student Jessica Fossum (UCLA), we explore how registered reports have mainly been found in psychology, explores the typical time in press for registered reports, and common barriers in adopting registered reports. 


Submitted to Advances in Methods and Practices in Psychology, in collaboration with my graduate student Jessica Fossum (UCLA), we compare power estimates from six commonly used tests of the indirect effect for mediation analysis, concluding that power estimates from the joint significance test, Monte Carlo confidence interval, and percentile bootstrap confidence interval are similar enough to not have to use bootstrapping for power analysis