Faculty & School/Dept.
Faculty of Health - Department of Psychology
BA - 1996
Gambier, Ohio, USA
PhD - 2002
University of North Carolina at Chapel Hill
See Google Scholar or Research Gate for more.
Flora, D.B. (2018). Statistical Methods for the Social and Behavioural Sciences: A Model-Based Approach. London, UK: SAGE Publications.
Flora, D.B. (in press). Thinking about effect sizes: From the replication crisis to a cumulative psychological science. Canadian Psychology.
Counsell, A., Cribbie, R.A., & Flora, D.B. (2020). Evaluating equivalence testing methods for measurement invariance. Multivariate Behavioral Research, 55, 313-328.
Pek, J., & Flora, D.B. (2018). Reporting effect sizes in original psychological research: A discussion and tutorial. Psychological Methods, 23, 208-225.
Flora, D.B., & Flake, J.K. (2017). The purpose and practice of exploratory and confirmatory factor analysis in psychological research: Decisions for scale development and validation. Canadian Journal of Behavioural Science, 49, 78-88.
Chalmers, R.P., Counsell, A., & Flora, D.B. (2016). It might not make a big DIF: Improved Differential Test Functioning statistics that account for sampling variability. Educational and Psychological Measurement, 76, 114-140.
Flora, D.B., LaBrish, C., & Chalmers, R.P. (2012). Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis. Frontiers in Psychology (Quantitative Psychology and Measurement), 3 (55).
Flora, D.B. (2011). Joint two-part modeling of semicontinuous longitudinal variables: A comparison of approaches. Methodology, 7, 145-156.
Flora, D.B. (2008). Specifying piecewise latent trajectory models for longitudinal data. Structural Equation Modeling, 15, 513-533.
Flora, D.B., Curran, P.J., Hussong, A.M., & Edwards, M.C. (2008). Incorporating measurement non-equivalence in a cross-study latent growth curve analysis. Structural Equation Modeling, 15, 676-704.
Flora, D.B., & Curran, P.J. (2004). An evaluation of alternative methods for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466-491.
Currently available to supervise graduate students: Yes
Currently taking on work-study students, Graduate Assistants or Volunteers: No
Available to supervise undergraduate thesis projects: Yes
I study quantitative methodology for psychological research, primarily focusing on latent variable models for psychometric data and longitudinal data. I also study the use and interpretation of effect size statistics more broadly. Some of the methods I'm interested in include exploratory and confirmatory factor analysis (especially with categorical data), structural equation modeling, latent growth curve models, and item response theory. I have applied these methodologies to research on a variety of topics in psychology.