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Jason Rights

Graduate Student
Research Area: Quantitative Methods

My research focus is on methodological advances in mixture modeling, multilevel modeling, and structural equation modeling. I am particularly interested in model evaluation, the relationships between different modeling approaches, and, moreover, how knowledge of such relationships and integration of methods can prove useful in practice. To aid researchers in applying my work, I develop software (primarily in R) that is openly available for public use. 

Representative Publications


Rights, J.D., & Sterba, S.K. (In press). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods.

Cole, D.A., Goodman, H.J., Garber, J., Cullum, K.A., Cho, S.-J., Rights, J.D., Felton, J.W., Jacquez, F.M., Korelitz, K.E., & Simon, H.F.M. (in press). Validating parent and child forms of the Parent Perception Inventory. Psychological Assessment. 

Rights, J.D.& Sterba, S.K. (In press). A framework of R-squared measures for single-level and multilevel regression mixture models. Psychological Methods.

Cole, D.A., Martin, J.M., Jacquez, F.M., Tram, J.M., Zelkowitz, R., Nick, E.A., & Rights, J.D. (2017). Time-varying and time-invariant dimensions of depression in children and adolescents: Implications for cross-informant agreement. Journal of Abnormal Psychology, 126, 635-651.

Sterba, S.K., & Rights, J.D. (2017). Effects of parceling on model selection: Parcel-allocation variability in model ranking. Psychological Methods, 22, 47-68.

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 Psychology69, 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.



Rights, J.D., & Sterba, S.K. (May, 2017). A framework of R-squared measures for single-level and multilevel regression mixture models. Modern Modeling Methods Conference, Storrs, CT.

Rights, J.D., & Sterba, S.K. (August, 2016). The relationship between multilevel models and nonparametric multilevel mixture models. In Advances in Mixture Modeling symposium with D. Steinley, J. Harring & K. Grimm. American Psychological Association, Denver, CO.

Sterba, S.K. & Rights, J.D. (August, 2016). Challenges and opportunities in psychological applications of mixture models. American Psychological Association Convention (Division 5), Denver, CO.

Rights, J.D., & Sterba, S.K. (July, 2016). Approximating level-1 and level-2 heteroscedasticity with nonparametric multilevel mixture models. International Meeting of the Psychometric Society, Asheville, NC. 

Sterba, S.K., & Rights, J.D. (November, 2015, Invited). Quantifying hidden costs of parceling in latent variable modeling. University of Notre Dame, Psychology Department Colloquia.

Sterba, S.K., & Rights, J.D. (October, 2015). Effects of parceling on model selection: Parcel-allocation variability in model ranking. Society of Multivariate Experimental Psychology Conference, Redondo Beach, CA.


Rights, J.D., & Sterba, S.K. (July, 2016). Algorithm for choosing the number of random item-to-parcel allocations in SEM. International Meeting of the Psychometric Society. Asheville, NC. 

Seaman, K. L., Forman-Alberti, A. B., Rights, J. D., Howard D. V., & Howard J. H. (2013, April). Age-related differences in the effect of statistical structure on learning in a sequentially–cued prediction task. Cognitive Neuroscience Society annual meeting. San Francisco, CA.

Rights, J. D., & Prinstein, M. J. (April , 2011). Depression among best friends: An examination of depression socialization and friendship quality. University of North Carolina Psychology Departmental Honors Poster Session. Chapel Hill, NC.


PAVranking: This R function quantifies and assesses the consequences of parcel-allocation variability for model ranking of structural equation models. Available in the semToolspackage at

poolMAlloc: This R function employs an iterative algorithm to choose the number of random item-to-parcel allocations needed to meet user-defined stability criteria for a fitted structural equation model. Available in the semTools package at

npmmApproximation: This R function outputs implied fixed effects, random effect (co)variances, and residual variance components for a given nonparametric multilevel regression mixture model. Available at

regMixR2: This R function computes and outputs R-squared measures and analytic decompositions of variance for single-level and multilevel regression mixture models. Available at