Associate Professor & QM Area Chair
Office & Hours
Office: SZB 538H
Wednesday: 3:30 - 5:00 pm (Introduction to Statistics)
Thursday: 9:00 - 10:30 am (Structural Equation Modeling)
And by Appointment
The University of Texas at Austin
1912 Speedway, Stop D5800
Austin, TX 78712-1289
download vita (pdf)
My research focuses on the examination and demonstration of various procedures used to model the relationships among variables, such as structural equation modeling (SEM), multilevel modeling (MLM), and item response theory (IRT) with an overarching focus on model specification.
PhD, University of Texas at Austin, 2003
MS, University of Texas at San Antonio, 1998
BA, University of Texas at San Antonio, 1995
Whittaker, T. A., Beretvas, S. N., & Falbo, T. (in press). Dyadic curve-of-factors model: An introduction and illustration of a model for non-exchangeable longitudinal dyadic data. Structural Equation Modeling.
Whittaker, T. A. (in press). The impact of noninvariant intercepts in latent means models. Structural Equation Modeling.
Whittaker, T. A., Chang, W., & Dodd, B. G. (2012). The performance of IRT model selection methods with mixed-format tests. Applied Psychological Measurement, 36(3), 159-180.
Whittaker, T. A., & Furlow, C. F. (2009). The comparison of model selection criteria when selecting among competing hierarchical linear models. Journal of Modern Applied Statistical Methods, 8(1), 173-193.
Whittaker, T. A., & Stapleton, L. M. (2006). The performance of cross-validation indices used to select among competing covariance structure models under multivariate nonnormality conditions. Multivariate Behavioral Research, 41(3), 295-335.
- 2011 Summer Research Assignment - The University of Texas at Austin. An assessment of approaches used to select reference indicators when testing for invariance in structural equation modeling.
- Special Research Grant - The University of Texas at Austin. (Fall, 2008). Dyadic curve-of-factors model: An introduction and illustration of a model for longitudinal dyadic data.
Research Interests and Expertise
My principal methodological research interest deals with the various facets of model specification, including, but not limited to, model comparison/selection and model modification methods. With the use of simulation techniques, I examine the performance of these different model specification approaches under manipulated conditions. With the use of real data sets, I also provide illustrations and demonstrations of alternative model parameterizations for pedagogical purposes. There are numerous models that may be employed to explain the relationships among variables. I am interested in employing models within the structural equation modeling (SEM), multilevel modeling (MLM), and item response theory (IRT) arenas.
My other research interest deals with real-world data applications with these various modeling techniques. This is, in large part, due to requests from applied researchers to help with the methodological aspects of their study given my knowledge in quantitative methods. I enjoy these opportunities to collaborate with researchers in different disciplines because real-world data issues provide me with methodological research ideas as well as interesting examples to use in my courses when I am teaching.