David B. Flora

Associate Professor

Quantitative Methods

Locations / Contact Info:

333 Behavioural Science - BSB
Keele Campus

Email address(es):

dflora at yorku dot ca

Web site(s):

Statistical Consulting Service

Faculty & School/Dept.

Faculty of Health - Department of Psychology

Degrees

BA - 1996
Kenyon College
Gambier, Ohio, USA

PhD - 2002
University of North Carolina at Chapel Hill
USA

Selected Publications

See Google Scholar or Research Gate.



Flora, D.B. (2018). Statistical Methods for the Social and Behavioural Sciences: A Model-Based Approach. London, UK: SAGE Publications.



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.


Affiliations

Psychometric Society

Supervision

Currently available to supervise graduate students: Yes

Currently taking on work-study students, Graduate Assistants or Volunteers: No

Available to supervise undergraduate thesis projects: No

Current Research

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.