Welcome to CARMA’s Webcast Lecture

Aug. 26, 2022

Dr. Fred Oswald, Rice University

Multiple Linear Regression: Strengthening Conceptual Knowledge and Practical Skills

12:00 PM – 1:30 PM ET

Dr. Fred Oswald is a professor at Rice University practicing in the area of Psychological Sciences. His main scopes of expertise ranges from Research Methods to Workforce Readiness and finally to Quantitative Methods (Meta-analysis, Psychometrics, Big Data). In 2020, Dr. Oswald was honored as a National Associate by the National Academy of Sciences . His recent published intellectual contributions are as follows:

  • Wu, F. Y., Mulfinger, E., Alexander, III, L., Sinclair, A. L., McCloy, R. A., & Oswald, F. L. (2022). Individual differences at play: An investigation into measuring Big Five facets with game-based assessments. International Journal of Selection and Assessment
  • Hysong, S. J., Arredondo, K., Hughes, A. M., Lester, H. F., Oswald, F. L., Petersen, L. A., et al. (2022). An evidence-based, structured, expert approach to selecting essential indicators of primary care quality
  • Bosnjak, M., Fiebach, C. J., Mellor, D., Mueller, S., O’Connor, D. B., Oswald, F. L., & SokolChang, R. I. (in press). A template for preregistration of quantitative research in psychology: Report of the Joint Psychological Societies Preregistration Task Force. American Psychologist


Multiple linear regression (MLR) is one of the most useful and used methodological tools in the social sciences. This webinar will strengthen attendees’  conceptual knowledge and practical skills in MLR further through a series of topics presented: (a) relationships between descriptive statistics, visualization, and MLR; (b) conducting MLR with interaction and quadratic terms; (c) useful MLR reporting standards; and (d) MLR as the basis for path analysis, factor analysis (EFA, CFA, SEM), and multilevel modeling.


  1. Farway, J. J., (2002). Practical regression and ANOVA using R.
  2. Freedman, D. (1989). Statistical models and shoe leather. Mathematical Social Sciences, 18, 192-192.
  3. Nimon, K., Oswald, F., & Roberts, J. K. (2013; 2020; 2021). yhat [Computer software and manual], Accessible from https://cran.r–project.org. Note: A package for the R statistics language and environment to interpret regression effects

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