Research Interests and Expertise

Evaluation and innovative application of psychometric and statistical models; multilevel modeling with a focus on extensions to the conventional multilevel model to handle student mobility and other sources of data structure complexities; meta-analytic techniques (particularly, synthesis of single-case research designs’ results).

Current Research Projects and Grants

  • Institute of Education Sciences: Multilevel Synthesis of Single-Case Experimental Data: Further Developments and Empirical Validation (UT principal investigator; funded: $262,622)
  • Institute of Education Sciences: A Meta-Analysis of Parent Involvement Interventions and Family-School Partnerships’ Effects on Student Outcomes (UT principal investigator; submitted: $115,033)
  • Institute of Education Sciences: An Evaluation of Statistical Procedures and Models Used to Handle Student Mobility (principal investigator; under revision)

Boards, Committees, and Associations

  • American Educational Research Association:
    • Division D
    • Hierarchical Linear Modeling Special Interest Group
    • Structural Equation Modeling Special Interest Group
  • Educational Statisticians Special Interest Group
  • National Council on Measurement in Education
  • American Psychological Association

Recent Awards

  • 2009 Regents’ Outstanding Undergraduate Teaching Award

Representative Publications

  • Beretvas, S. N. (2010). Cross-classified and multiple membership models. In J. Hox & J. K. Roberts (Eds.), The handbook of advanced multilevel analysis (pp. 313–334). New York, NY: Routledge.
  • Chung, H., & Beretvas, S. N. (in press). The impact of ignoring multiple-membership data structures in multilevel models. British Journal of Mathematical and Statistical Psychology.
  • Grady, M., & Beretvas, S. N. (2010). Incorporating student mobility in achievement growth modeling: A cross-classified multiple membership growth curve model. Multivariate Behavioral Research, 45, 393–419.
  • Riviello, C., & Beretvas, S. N. (2009). Detecting lag-one autocorrelation in interrupted time series experiments with small datasets. Journal of Modern Applied Statistical Methods, 8, 469–477.
  • Meyers, J. L., & Beretvas, S. N. (2006). The impact of inappropriate modeling of cross-classified data structures. Multivariate Behavioral Research, 41, 473–497.