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Psychological Sciences

Psychological Sciences
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Sonya K. Sterba

Professor of Psychology and Human Development
Director of the Quantitative Methods Program, and Faculty Advisor for the Data Science Institute

My research topics include latent variable models for longitudinal and cross-sectional data, mixture models, and multilevel models, with a focus on advancing psychology research.

Representative Publications

  • Rights, J.D., & Sterba, S.K. (In press). R-squared measures for multilevel models with three or more levels. Multivariate Behavioral Research.
  • Sterba, S.K., & Rights, J.D. (2022)†. R-squared measures for multilevel mixture models with random effects. Structural Equation Modeling, 29, 489-506. †Authors contributed equally.
  • Rights, J.D., & Sterba, S.K. (2021). Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R-squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies. New Directions for Child and Adolescent Development (Special Issue on Developmental Methods), 175, 65-110.
  • Rights, J.D., & Sterba, S.K. (2020). New recommendations on the use of R-squared differences in multilevel model comparisons. Multivariate Behavioral Research, 55, 568-599.
  • Sterba, S.K. (2019). Problems with rationales for parceling that fail to consider parcel-allocation variability. Multivariate Behavioral Research, 54, 264-287.
  • Rights, J.D., & Sterba, S.K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24, 309-338. Online appendix and code available.
  • Preacher, K.J., & Sterba, S.K. (2019). Aptitude-by-treatment interactions in research on educational interventions. Exceptional Children, 85, 248-254.
  • Rights, J.D., & Sterba, S.K. (2018). A framework of R-squared measures for single-level and multilevel regression mixture models. Psychological Methods, 23, 434-457. Online appendix and code available.
  • Lee, W.-y., Cho, S.-J., & Sterba, S.K. (2018). Ignoring a multilevel structure in mixture item response models: Impact on parameter recovery and model selection. Applied Psychological Measurement, 42, 136-154.
  • Rights, J.D., Sterba, S.K., Cho, S-J., & Preacher, K.J. (2018). Addressing model uncertainty in item response theory person scores through model averaging. Behaviormetrika, 45, 495-503.
  • Sterba, S.K. (2017). Pattern mixture models for quantifying missing-data uncertainty in longitudinal invariance testing. Structural Equation Modeling, 24, 283-300.
  • Sterba, S.K. (2017). Partially nested designs in psychotherapy trials: A review of modeling developments. Psychotherapy Research, 27, 47-68.
  • Sterba, S.K. & Rights, J.D. (2017). Effects of parceling on model selection: Parcel-allocation variability in model ranking. Psychological Methods, 22, 47-68.
  • Gottfredson, N.C., Sterba, S.K. & Jackson, K.A. (2017). Explicating the conditions under which multilevel multiple imputation mitigates bias resulting from random coefficient-dependent missing longitudinal data. Prevention Science, 18, 12-19.
  • Rights, J.D. & Sterba, S.K. (2016). The relationship between multilevel models and nonparametric multilevel mixture models: Discrete approximation of intraclass correlation, random coefficient distributions, and residual heteroscedasticity. British Journal of Mathematical and Statistical Psychology, 69, 316-343.
  • Sterba, S.K., & Rights, J.D. (2016). Accounting for parcel-allocation variability in practice:Combining sources of uncertainty and choosing the number of allocations. Multivariate Behavioral Research, 51, 296-313.
  • Sterba, S.K. (2016). A latent transition analysis model for latent-state-dependent nonignorable missingness. Psychometrika, 81, 506-534.
  • Sterba, S.K. (2016). Interpreting and testing interactions in conditional mixture models. Applied Developmental Science, 20, 29-43.
  • Sterba, S.K. (2016). Cautions on the use of multiple imputation when selecting between latent categorical versus continuous models for psychological constructs. Journal of Clinical Child and Adolescent Psychology, 45, 167-175.
  • Sterba, S.K. & Gottfredson, N.C. (2015). Diagnosing global case influence on MAR versus MNAR model comparisons. Structural Equation Modeling, 22, 294-307.
  • Lachowicz, M.J., Sterba, S.K. & Preacher, K.J. (2015). Investigating multilevel mediation with fully or partially nested data. Group Processes & Intergroup Relations, 18, 274-289.
  • Sterba, S.K. (2014). Handling missing covariates in conditional mixture models under missing at random assumptions. Multivariate Behavioral Research, 49, 614-632.
  • Sterba, S.K. (2014). Fitting nonlinear latent growth models with individually-varying time points. Structural Equation Modeling, 21, 630-647.
  • Sterba, S.K., Preacher, K.J., Forehand, R., Hardcastle, E.J., Cole, D.A. & Compas, B.E. (2014). Structural equation modeling approaches for analyzing partially nested data. Multivariate Behavioral Research, 49, 93-118.
  • Sterba, S.K., & Bauer, D.J. (2014). Predictions of individual change recovered with latent class or random coefficient growth models. Structural Equation Modeling, 21, 342-360. 
  • Sterba, S.K. (2013). Understanding linkages among mixture models. Multivariate Behavioral Research, 48, 775-815.
  • Sterba, S.K. & Pek, J. (2012). Individual influence on model selection. Psychological Methods, 17, 582-599.
  • Sterba, S.K., Baldasaro, R.E. & Bauer, D.J. (2012). Factors affecting the adequacy and preferability of semiparametric groups-based approximations of continuous growth trajectories. Multivariate Behavioral Research, 40, 590-634.
  • Bauer, D.J. & Sterba, S.K. (2011). Fitting multilevel models with ordinal outcomes: Performance of alternative specifications and methods of estimation. Psychological Methods, 16, 373-390.
  • Sterba, S.K. (2011). Implications of parcel-allocation variability for comparing fit of item-solutions and parcel-solutions. Structural Equation Modeling, 18, 554-577.
  • Panter, A.T. & Sterba, S.K. (2011). Handbook of Ethics in Quantitative Methodology. Multivariate Applications Series. Taylor & Francis/Routledge.
  • Sterba, S.K. & Bauer, D.J. (2010). Statistically evaluating person-oriented principles revisited: Reply to Molenaar (2010), von Eye (2010), Ialongo (2010) and Mun, Bates and Vaschillo (2010). Development & Psychopathology, 22, 287-294.
  • Sterba, S.K. & Bauer, D.J. (2010). Matching method with theory in person-oriented developmental psychopathology research. Development & Psychopathology, 22, 239-254.
  • Sterba, S.K. & MacCallum, R.C. (2010). Variability in parameter estimates and model fit across random allocations of items to parcels. Multivariate Behavioral Research, 45, 322-358.
  • Sterba, S.K. Copeland, W., Egger, H., Costello, J., Erkanli, A. & Angold, A. (2010). Longitudinal dimensionality of adolescent psychopathology: Testing the differentiation hypothesis. Journal of Child Psychology and Psychiatry, 51, 871-884.
  • Sterba, S.K. (2009). Alternative model-based and design-based frameworks for inference from samples to populations: From polarization to integration. Multivariate Behavioral Research, 44, 711-740.