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Blozis, S. A., & Craft, M. (2023). Alternative residual covariance structures in mixed-effects models: Addressing intra- and inter-individual heterogeneity. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02133-1

Blozis, S. A. (2023). Bayesian pattern-mixture models for drop out and intermittently missing data in longitudinal data analysis. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02128-y

Blozis, S. A. (2022). A latent variable mixed-effects location scale model for longitudinal data with an application to daily diary data. Psychometrika, 87, 1548–1570.

Blozis, S. A. (2022). Bayesian two-part multilevel model for longitudinal media use data. Journal of Marketing Analytics, 10, 311–328.

Blozis, S. A., & Harring, J. R. (2021). Fitting nonlinear mixed-effects models with alternative residual covariance structures. Sociological Methods & Research, 50(2), 531-566.

Harring, J. R., Strazzeri, M. M., & Blozis, S. A. (2021). Piecewise latent growth models: Beyond modeling linear-linear processes. Behavior Research Methods, 53, 593-608. https://doi.org/10.3758/s13428-020-01420-5

Blozis, S. A., McTernan, M., Harring, J., & Zheng, Q. (2020). Two-part mixed-effects location scale models. Behavior Research Methods, 52, 1836–1847.https://doi.org/10.3758/s13428-020-01359-7

Harring, J. R., & Blozis, S. A. (2016). A note on recurring misconceptions when fitting nonlinear mixed models. Multivariate Behavioral Research, 51(6), 805-817.

Blozis, S. A., & Harring, J. R. (2016). On the estimation of nonlinear mixed-effects models and latent curve models for longitudinal data. Structural Equation Modeling, 23(6), 904-920.

Blozis, S. A., & Harring, J. (2015). Understanding individual-level change through the basis functions of a latent curve model. Sociological Methods & Research.

McTernan, M., & Blozis, S. A. (2015). Longitudinal models for  ordinal data with many zeros and varying numbers of response categories. Structural Equation Modeling, 22(2), 216-226. 

Blozis, S. A., & Villarreal, R. (2014). Analytic approaches to the Multigroup Ethnic Identity Measure (MEIM). Applied Psychological Measurement, 38(7), 577-580. 

Harring, J. R., & Blozis, S. A. (2014). Fitting correlated residual error structures in nonlinear mixed-effects models using SAS PROC NLMIXED. Behavioral Research Methods, 46(2), 372-384.

Xu, S., Blozis, S.A., & Vandewater, E.A. (2014). On fitting a multivariate two-part latent growth model. Structural Equation Modeling, 21(1), 131-148.

Blozis, S. A., Ge, X., Xu, S., Natsuaki, M. N., Shaw, D. S., Neiderhiser, J. M., Scaramella, L. V., Leve, L. D., & Reiss, D. (2013). Sensitivity analysis of multiple informant models when data are not missing at random. Structural Equation Modeling, 20(2), 283-298.

Xu, S., & Blozis, S. A. (2011). Sensitivity Analysis of a Mixed Model for Incomplete Longitudinal Data. Journal of Educational & Behavioral Statistics, 36, 237-256.

Blozis, S.A., & Cho, Y.I. (2008). Coding and centering of time in latent curve models in the presence of interindividual time heterogeneity. Structural Equation Modeling, 15(3), 413-433.

Blozis, S.A., Harring, J.R., & Mels, G. (2008). Using LISREL to fit nonlinear latent curve models. Structural Equation Modeling, 15(2), 356-379.

Blozis, S.A. (2007). A Newton procedure for a conditionally linear mixed-effects model. Behavior Research Methods, 39, 695-708.

Blozis, S.A. (2007). On fitting nonlinear latent curve models to multiple variables measured longitudinally. Structural Equation Modeling, 14(2), 179-201.

Blozis, S.A. (2004). Structured latent curve models for the study of change in multivariate repeated measures. Psychological Methods, 9(4), 334–353.

Blozis, S.A., & Cudeck, R. (1999). Conditionally linear mixed-effects models with latent variable covariates. Journal of Educational & Behavioral Statistics, 24, 245-270.