Matthew Valente, PhD

Assistant Professor

Contact Info

• Office: CPH 2127
• Academic Email: mjvalente@usf.edu
• Academic Phone: (813) 974-0900
• View My C.V.

Education

Discipline

Quantitative Research Methods

Specialization

• Causal Inference
• Statistical Mediation Analysis
• Preventive Interventions

Biography

Matthew received his PhD from Arizona State University in 2018. He has two main areas of research. First, his research consists of bridging the gap between modern causal mediation methods and currently accepted statistical mediation methods in the health and social sciences. Second, his research involves developing and evaluating statistical mediation analysis methods in the context of longitudinal data and preventive interventions. In addition to his focal research, Matthew has assisted with data analytic issues on several federally funded projects evaluating effects of health intervention programs. Together with Dr. Judith Rijnhart, Dr. Valente runs the Causal Inference in Public Health Research (CIPHR) lab. This lab is focused on the evaluation and application of causal inference methods in public health research. Drs. Valente and Rijnhart hold monthly lab meetings in which causal inference topics are discussed. The students in the CIPHR lab have a wide range of backgrounds and skill levels, including undergraduate students, master-level students, and doctoral-level students with concentrations in epidemiology or biostatistics. Students interested in joining the CIPHR lab can send an email to Dr. Valente (mjvalente@usf.edu) and/or Dr. Rijnhart (jrijnhart@usf.edu). Matthew is currently funded by the Institute of Education Sciences (IES; R305D240044) to develop statistical methods for synthesizing causal mediation effects in single-case experimental designs. The goals of this proposal are to provide single-case researchers with statistical methodology to help understand how interventions produce change in behaviors and how to synthesize this information across cases in a single-case experimental design (SCED) study. The proposed research plan will bridge the gulf between methods traditional in SCEDs research and cutting-edge methods for estimating causal mediation effects.

Research Interests

• Applying advanced statistical methodology aimed at improving health-related outcomes.
• Developing and evaluating modern causal inference methods for mediation analysis including moderated-mediation analysis.
• Developing and evaluating causal mediation analysis methods in the context of randomized experiments with longitudinal data.
• Bridging the gap between new cutting-edge causal mediation methods and currently accepted statistical mediation methods in health-related research.