Research Terms
Psychometrics Educational Assessment Educational Testing Statistical Computing Survey Methods
Keywords
Causal Inference Confirmatory Factor Analysis Data Mining Multilevel Modeling Propensity Score Analysis Psychometrics Quasi-Experimental Design Structural Equation Modeling Validity
Leite, W. L. (2016). Practical Propensity Score Methods Using R. Thousand Oaks, CA: Sage Publishing.
Leite, W. L., Aydin, B., & Gurel, S. (in press). A comparison of propensity score weighting methods for evaluating the effects of programs with multiple versions. Journal of Experimental Education.
Collier, Z. K. & Leite, W. L. (in press). Testing the Effectiveness of Three-Step Approaches with Auxiliary Variables in Latent Class and Profile Analysis. Structural Equation Modeling: A Multidisciplinary Journal.
Hu, J., & Leite, W. L. (2017). An Evaluation of the Use of Covariates to Assist in Class Enumeration in Linear Growth Mixture Modeling. Behavior Research Methods, 49 (3), 1179-1190.
Kaya, Y., & Leite, W. L. (2017). Assessing change in latent skills across time with longitudinal cognitive diagnosis modeling: An evaluation of model performance. Educational and Psychological Measurement, 77(3) 369–388.
Bruce, T., Leite, W. L., & Smith, S. (2017). A Quasi-Experimental Analysis of School-Wide Violence Prevention Programs. Journal of School Violence, 16, 49-67.
Aydin, B., Leite, W. L, & Algina, J. (2016). The effects of including observed means or latent means as covariates in multilevel models for cluster randomized trials. Educational and Psychological Measurement, 76, 803–823.
Koo, N., Leite, W. L., & Algina, J. (2016). Mediated effects with the parallel process latent growth model: an evaluation of methods for testing mediation in the presence of nonnormal data. Structural Equation Modeling, 23, 32-44.
Leite, W. L., Jimenez, F., Kaya, Y., Stapleton, L. M., MacInnes, J. W., & Sandbach, R. (2015). An evaluation of weighting methods based on propensity scores to reduce selection bias in multilevel observational studies. Multivariate Behavioral Research. 50, 265–284.
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. (2015). An evaluation of weighting methods based on propensity scores to reduce selection bias in multilevel observational studies. Multivariate Behavioral Research.
Park, Y., Gurel, S., Oh, J., Leite, W., & Bettini, E. F. (in press). Literacy related school readiness skills of English language learners in Head Start: An analysis of the School Readiness Survey. Journal of Early Childhood 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.
Matos, D. A. S., Cirino, S. D., Brown, G. T. L., & Leite, W. L. (2013). A avaliação no ensino superior: Concepções múltiplas de estudantes Brasileiros [Assessment in higher education: Multiple conceptions of Brazilian students]. Estudos em avaliação educacional, 24 (54), 172-193.
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.
Adams, A., Ross, D., Swain, C., Dana, N, Leite, W., & Sandbach, R. (2013). Preparing teacher leaders in a job-embedded graduate program: Changes within and beyond the classroom walls.Teacher Education and Practice, 26 (3), 581-597.
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.
Boman IV, J. H., Ward, J. T., Gibson, C. L., & Leite, W. L. (2012) Can a perceptual peer deviance measure accurately measure a peer’s self-reported deviance? Journal of Criminal Justice. 40, 463–471.
Leite, W. L. (2012): Latent Growth Curve Modeling By Kristopher J. Preacher, Aaron L. Wichman, Robert C. MacCallum, and Nancy E. Briggs. Structural Equation Modeling, 19, 152-155.
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.
Ward, J., Gibson, C., Boman, J. & Leite, W. L. (2010). Assessing the validity of the Retrospective Behavioral Self-Control scale: Is the General Theory of Crime stronger than the evidence suggests? Criminal Justice and Behavior. 37, 336-357.
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.
Tuccitto, D. E., Giacobbi Jr., P. R., & Leite, W. L. (2010). The internal structure of positive and negative affect: A confirmatory factor analysis of the PANAS. Educational and Psychological Measurement, 70, 125-141.
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.
Huang, I. C., Shenkman, E. A., Leite, W., Knapp, C. A., Thompson, L. A., & Revicki, D. A. (2009). Agreement was not found in adolescents’ quality of life rated by parents and adolescents. Journal of Clinical Epidemiology, 62, 337-346.
Lane, H. B., Hudson, R. F., Leite, W. L., Kosanovich, M. L., Strout, M. T., Fenty, N. S., & Butler, T. W. (2009). Teacher knowledge about reading fluency and students’ reading fluency growth in Reading First schools. Reading and Writing Quarterly. 25(1), 57-86.
Leite, W. L., Huang, I., & Marcoulides, G. A. (2008). Item selection for the development of short-forms of scales using an Ant Colony Optimization algorithm. Multivariate Behavioral Research.43, 411-431.
Huang, I., Liu, J., Wu, A., Wu, M., Leite, W., & Hwang, C. (2008). Evaluating the reliability, validity and minimally important difference of the Taiwanese version of the Diabetes Quality of Life (DQOL) measurement. Health and Quality of Life Outcomes. 6, 87-99.
Huang I., Hwang, C. Wu M., Leite, W. L., & Wu A. W. (2008). Diabetes-specific or generic measures for health-related quality of life? Evidence from psychometric validation of the D-39 and SF-36. Value in Health, 11, 405-461.
Huang I., Frangakis C., Atkinson, M. J., Willkes, R. J., Leite, W. L., Vogel, W. B., & Wu., A. W. (2008). Addressing ceiling effects in health status measures: A comparison of techniques applied to measures for people with HIV disease. Health Services Research. 43, 327-339.
Leite, W. L. (2007). A comparison of latent growth models for constructs measured by multiple Items.Structural Equation Modeling. 14, 581-610.
Leite, W. L., & Beretvas, S. N. (2005). Validation of scores on the Marlowe-Crowne Social Desirability Scale and the Balanced Inventory of Desirable Responding.Educational and Psychological Measurement, 65, 140-154.
Stapleton, L. M., & Leite, W. L. (2005). A review of syllabi for a sample of structural equation modeling courses. Structural Equation Modeling, 12, 642-664.
Beretvas, S. N., Meyers, J. L., & Leite, W. L. (2002). A reliability generalization study of the Marlowe-Crowne Social Desirability Scale. Educational and Psychological Measurement, 62(4), 570 -589.
Florida Educational Research Association, Secretary; 2010 - 2011
Academy of Management, Member; 2009 - present
American Educational Research Association, Member; 2004 - present
National Council on Measurement in Education, Member; 2004 - present
This presentation provides an overview of methods to evaluate educational programs that do not rely on controlled experiments. It highlights the difficulties of evaluating educational programs, and methods use secondary data to estimate effects of educational programs us.
Subject Areas:
Keywords:
Audience:
Adults
Duration:
2-3 hours
Fee:
Expenses Only
