Text-only version of this website Skip to content

Leite, Walter



2015-06-11_Walter_Leite_Portrait-6633_reducedProfessor
Research and Evaluation Methodology Program
School of Human Development and Organizational Studies in Education
Educational Psychology

College of Education
University of Florida
119 E Norman Hall
PO Box 117047
Gainesville, FL 32611
352-273-4302
Fax: 352-392-5929
walter.leite@coe.ufl.edu
education.ufl.edu/leite/

Research Biography

My current research program consists of developing and evaluating statistical methods to strengthen causal inference and understanding of causal mechanisms using quasi-experimental and non-experimental data. My specific methodological interests are in structural equation modeling, multilevel modeling, and propensity score methods, as well as the integration between these three methods. I investigate innovative applications of these methods to educational research performed with large datasets from state departments of education, nationally-representative educational surveys, and massive data sets from virtual learning environments. The methods that I investigate take advantage of large scale longitudinal data to answer causal questions about treatment effects, mediation and moderation. I address obstacles to effective program evaluation with quasi-experimental and non-experimental data such as selection bias, measurement error, and attrition bias.

Degrees

Ph.D. – Department of Educational Psychology, The University of Texas at Austin, 2005, Quantitative Methods

M.A. – The University of Texas at Austin, 2003, Program Evaluation

B.A. – Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil, 2000

Key Professional Appointments

Professor, 2017-Present

Associate Professor,Department of Educational Psychology, University of Florida, 2011-2017

Assistant Professor,Department of Educational Psychology, University of Florida, 2005-2011

Grants

Combining latent growth modeling with propensity score matching to estimate the time-varying effect of student mobility

Using item response theory to improve children’s quality of life assessment

The Use of Inverse Probability-Of-Treatment Weighting with Growth Curve Models for the Estimation of Time-Varying Treatment Effects: The impact of nonlinear growth.

Selected Publications

Leite, W. L. (2015). Latent growth modeling of longitudinal data with propensity score matched groups In Wei Pan, & Haiyan Bai. Propensity Score Analysis: Fundamentals, Developments, and Extensions, (pp. 191-216.) New York: Guilford.

Leite, W. L., Jimenez, F., Kaya, Y., Stapleton, L. M., MacInnes, J. W., & Sandbach, R. (in press). An evaluation of weighting methods based on propensity scores to reduce selection bias in multilevel observational studies. Multivariate Behavioral Research.

Aydin, B., Leite, W. L., & Algina, J. (2014). The Consequences of Ignoring Variability in Measurement Occasions within Data Collection Waves in Latent Growth Models. Multivariate Behavioral Research, 49, 149–160.

Koo, N. & Leite, W. L. (2014). The Impact of Ignoring Time Series Processes in Linear Growth Mixture Modeling. Structural Equation Modeling, 21, 210–224.

Zhang, L., Jin, R., Leite, W. L., & Algina, J. (2014). Additive Models for Multitrait-Multimethod Data with a Multiplicative Trait-Method Relationship: A Simulation Study. Structural Equation Modeling, 21, 68–80.

Marcoulides, G., & Leite, W. L. (2013). Exploratory data mining algorithms for conducting searches in structural equation modeling: A comparison of some fit criteria. In. J. J. McArdle & G. Ritschard. Contemporary issues in exploratory data mining in the behavioral sciences (pp. 150-171). New York, NY: Taylor & Francis.

Bandalos, D. L. & Leite, W. L. (2013). Use of Monte Carlo Studies in Structural Equation Modeling Research. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd Ed.). (pp.564-666 ) Greenwich, CT: Information Age Publishing. Click here for code related to this chapter.

Leite, W. L., Sandbach, R., Jin, R., MacInnes, J., & Jackman, G. A. (2012). An Evaluation of Latent Growth Models for Propensity Score Matched Groups. Structural Equation Modeling, 19, 437–456.

Leite, W. L., & Stapleton, L. (2011). Detecting growth shape misspecifications in latent growth models: An evaluation of fit indices. The Journal of Experimental Education, 79, 361-381.

Jackman, M. G., Leite, W. L., & Cochrane, D. (2011). Estimating latent variable interactions with the unconstrained approach: A comparison of methods to form product indicators for large, unequal numbers of items. Structural Equation Modeling, 18, 274-288.

Leite, W. L., & Zuo, Y. (2011). Modeling latent Interactions at level two in multilevel structural equation models: An evaluation of mean-centered and residual-centered unconstrained Approaches. Structural Equation Modeling, 18, 449-464.

Leite, W. L., & Beretvas, S. N. (2010). The performance of multiple imputation for Likert-type items with missing data. Journal of Modern Applied Statistical Methods, 9(1), 64-74.

Leite, W. L., & Cooper, L. A. (2010). Detecting social desirability bias using factor mixture models. Multivariate Behavioral Research, 45, 271-293.

Leite, W. L., Svinicki, M., & Shi, Y. (2010). Attempted validation of the scores of the VARK: Learning Styles Inventory with multitrait–multimethod confirmatory factor analysis models. Educational and Psychological Measurement. 70, 323-339.

Shi, Y., Leite, W. L., & Algina, J. (2010). The impact of omitting the interaction between crossed factors in cross-classified random effects modeling. British Journal of Mathematical and Statistical Psychology, 63, 1-15.

Selected Links

education.ufl.edu/leite/