Date published: 07/01/19
Dr. Montoya and the Quantitative Research Collaboratory (QRClab) are a team of researchers working to create and assess statistical and research methods which assist psychological scientists in answering their research questions about why and when.
A primary focus of our lab is on mediation and moderation analysis, which are statistical methods aimed at helping researchers answer questions about mechanisms (Why does this happen?) and contingencies (When does this happen?). Many research questions in psychology revolve around mechanisms: How does this effect occur, or why does this effect occur? Whether investigating the mechanisms through which behavioral treatments influence mental health, the pathways through which early life experiences impact our personality, or how interpersonal interactions change our attitudes, all these questions rely on mediation analysis for answers. Dr. Montoya’s research focuses on creating and evaluating methods for mediation analysis, particularly for data collected repeatedly for the same individuals. When measuring individuals repeatedly, we gain rich information about intrapersonal change, and more concrete information about the psychological processes that goes on within each participant. However, the data-analytic process is complicated by the dependencies between the observations of the same individuals, requiring complex statistical methods to tease apart within- and between-person variability.
Additionally, in psychology research we must acknowledge that the impact of an intervention or interaction will not be the same across all individuals. We use moderation analysis to model potential predictors of these differences in order to better understand when certain effects occur or for whom. My recent publications in Psychological Methods and Behavioral Research Methods have shown how to conduct mediation and moderation analysis when we have two repeated-measurements of each individual. My macros MEMORE and OGRS, freely available for SPSS and SAS, make conducting these newly developed analytical approaches very easy, requiring only a single command.
Looking into the future, with increasing technological developments allowing researchers to contact their participants in real time throughout the day, data collection practices will only become more complex. Statistical methods need to keep up with the increase in technology. Statistical methods for understanding mechanisms and contingencies for highly repeated-measures designs, such as ecological momentary assessment, are needed. But all the while as statistical methods get more complex, a focus on best practices and creating highly replicable and reliable research must be a priority of psychological science.
Dr. Montoya grew up in Seattle, WA, where she completed her GED and attended college at North Seattle Community College, transferring to the University of Washington. She graduated with a BS in Psychology and a minor in Mathematics in 2013. After a year as a lab manager at the Stereotypes, Identity, and Belonging Lab at the University of Washington, Dr. Montoya began her doctoral studies at The Ohio State University working with Dr. Andrew Hayes. She completed her Masters in Psychology and Masters in Statistics in 2016 and completed her PhD in 2018. Recently, she moved to sunny California to begin as an Assistant Professor at UCLA! More about her research and lab can be found at akmontoya.com or on Twitter @AmandaKMontoya.