Amanda Kay Montoya, Ph.D., is Assistant Professor of Psychology at UCLA. Her research in quantitative psychology focuses on mediation, moderation, and conditional process analysis and its applications in repeated-measures, multilevel, and longitudinal data.
Montoya received her Ph.D. from the Ohio State University, working with Dr. Andrew Hayes, developer of the PROCESS macro. Montoya also creates easy-to-use tools for the application of mediation, moderation, and conditional process analysis in repeated measures data, including the MEMORE macro.
Broadly, Montoya’s research focuses on improving the ability of researchers to answer their questions of interest using sound statistical methods and by developing easy-to-use tools to encourage researchers to use the most advanced methods available. Other research topics include structural equation modeling, factor analysis and item response theory. She is also interested in improving our ability to conduct meta-science by developing statistical methods in meta-analysis, and understanding the impact of research practices on our ability to create replicable science.
Montoya has taught at the graduate and undergraduate levels, as well as workshops nationally and internationally. Her teaching style emphasizes real data analysis experience using datasets from published examples, and the development of practical skills required for using these methods in students’ own research. She also stresses the assumptions, limitations, and considerations that researchers need to take into account when applying these methods to real data.
Montoya values critical thinking, development of practical skills, and the ability to translate from real-life research problems to statistical methods and back again. She implements these values by using active learning strategies in the classroom, teaching multiple statistical programming languages, and focusing her teaching and student assessment on implementation and interpretation.
Her research in quantitative psychology focuses on mediation, moderation, and conditional process analysis and its applications in repeated-measures, multilevel, and longitudinal data.
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Mediation, Moderation, and Conditional Process Analysis
Mediation and moderation analyses are widely used in many different fields. Mediation analysis tests hypotheses about mechanisms or processes by which effects occur. Moderation analysis examines questions of contingencies (e.g., for whom, or when), commonly described as an “interaction”.View Details