Selected manuscripts and associated files are listed in chronological order, with most recent publications listed first

Belzak, W.C.M. & Bauer, D.J. (in press). Using regularization to identify measurement bias across multiple background characteristics: a penalized expectation-maximization algorithm. Journal of Educational and Behavioral Statistics. DOI: 10.3102/10769986231226439

Brandt, H., Chen, S.M., Bauer, D.J., (in press). Bayesian penalty methods for evaluating measurement invariance in moderated nonlinear factor analysis. Psychological Methods.

Chen, S.M. & Bauer, D.J. (in press). Modeling construct change over time amidst potential changes in construct measurement: a longitudinal moderated factor analysis approach. Psychological Methods. DOI: 10.1037/met0000685

Also Available: Supplemental Material

MacNeish, D. & Bauer, D.J. (in press). Reducing Incidence of Non-Positive Definite Covariance Matrices in Mixed Effect Models. Multivariate Behavioral Research. DOI: 10.1080/00273171.2020.1830019

McNeish, D., Bauer, D.J., Dumas, D., Clements, D.H., Cohen, J.R., Lin, W., Sarama, J. & Sheridan, M.A. (in press). Modeling individual differences in the timing of change onset and offset. Psychological Methods.

Chen, S.M., Bauer, D.J., Belzak W.M. & Brandt H. (2022). Advantages of spike and slab priors for detecting differential item functioning relative to other Bayesian regularizing priors and frequentist lasso. Structural Equation Modeling: A Multidisciplinary Journal, 29, 122-139. DOI: 10.1080/10705511.2021.1948335

Hussong, A.M., Bauer, D.J., Giordano, M.L. & Curran, P.J. (2022). Harmonizing altered measures in integrative data analysis: a methods analogue study. Behavior Research Methods, 53 1031-1045. DOI: 10.3758/s13428-020-01472-7

Curran, P.J., *Georgeson, A.R., Bauer, D.J., and Hussong, A.H. (2021). Psychometric models for scoring multiple reporter scales: Applications to integrative data analysis in prevention science and beyond. International Journal of Behavioral Development, 45, 40-50. DOI: 10.1177/0165025419896620

Urban, C. J. & Bauer, D. J. (2021). A deep learning algorithm for high-dimensional exploratory item factor analysis. Psychometrika, 86, 1-29. DOI: 10.1007/s11336-021-09748-3

Also Available: Code to reproduce experiments

Belzak, W.C.M. & Bauer, D.J. (2020). Improving the assessment of measurement invariance: using regularization to select anchor items and identify differential item functioning. Psychological Methods, 25, 673-690. DOI: 10.1037/met0000253

Bauer, D.J., Belzak, W.C.M. & Cole, V.T. (2020). Simplifying the assessment of measurement invariance over multiple background variables: using regularized moderated nonlinear factor analysis to detect differential item functioning. Structural Equation Modeling: A Multidisciplinary Journal, 27, 43-55. doi: 10.1080/10705511.2019.1642754.

Also Available: Supplemental results showing parameter-level false/true positive rates and supplemental instructions on how to implement Reg-DIF in the SAS NLMIXED procedure

Belzak, W.C.M. & Bauer, D.J. (2019). Interaction effects may actually be nonlinear effects in disguise: A review of the problem and potential solutions. Addictive Behaviors, 94, 99-108. doi: 10.1016/j.addbeh.2018.09.018

Cole, V.T., Bauer, D.J. & Hussong, A.M. (2019). Assessing the robustness of mixture models to measurement non-invariance. Addictive Behaviors, 94, 882-905. doi: 10.1080/00273171.2019.1596781. PMCID: PMC7247772

Gottfredson N.C., Cole V.T., Giordano M.L., Bauer D.J., Hussong A.M., & Ennett S.T. (2019). Simplifying the implementation of modern scale scoring methods with an automated R package: Automated moderated nonlinear factor analysis (aMNLFA). Addictive Behaviors, 94, 65-73. doi: 10.1016/j.addbeh.2018.10.031. PMCID: PMC6483881

Also Available: aMNLFA R package

Hussong, A.M., Gottfredson, N.C., Bauer, D.J., Curran, P.J., Haroon, M., Chandler, R., Kahana, S.Y., Delaney, J.A.C., Altice, F.L, Beckwith, C.G., Feaster, D.J., Flynn, P.M., Gordon, M.S., Knight, K., Kuo, I., Ouellet, L.J., Quan, V.M., Seal, D.W., Springer, S.A. (2019). Approaches for creating comparable measures of alcohol use symptoms: Harmonization with eight studies of criminal justice populations. Drug & Alcohol Dependence, 194, 59-68. doi: 10.1016/j.drugalcdep.2018.10.003. PMCID: PMC6312501

Curran, P.J., Cole, V.T., Bauer, D.J., Rothenberg, A.W., & Hussong, A.M. (2018). Recovering predictor-criterion relations using covariate-informed factor score estimation. Structural Equation Modeling: A Multidisciplinary Journal, 25, 860-875. DOI: 10.1080/10705511.2018.1473773

Also Available: Appendix 1 (GLM results), Appendix 2 (cell means).

Curran, P.J., Cole, V.T., Giordano, M., Georgeson, A.R., Hussong, A.M., & Bauer, D.J. (2018). Advancing the study of adolescent substance use through the use of integrative data analysis. Evaluation and the Health Professions, 41, 216-245. doi: 10.1177/0163278717747947.

Bauer, D.J. (2017). A more general model for testing measurement invariance and differential item functioning. Psychological Methods, 22, 507-526. doi: 10.1037/met0000077. PMCID: PMC5140785

Also Available: Supplemental material demonstrating how to fit the model in Mplus.

Cole, V., Bauer, D.J., Hussong, A.M. & Giordano, M.L. (2017). An empirical assessment of the sensitivity of mixture models to changes in measurement. Structural Equation Modeling: A Multidisciplinary Journal, 24, 159-179, doi: 10.1080/10705511.2016.1257354.

Dean, D., Bauer, D.J., & Prinstein, M.J. (2017). Friendship dissolution within social networks modeled through multilevel event history analysis. Multivariate Behavioral Research, 52, 71-289. doi: 10.1080/00273171.2016.1267605

Cole, V. & Bauer, D.J. (2016). A note on the use of mixture models for individual prediction. Structural Equation Modeling: A Multidisciplinary Journal, 23, 615-631, doi: 10.1080/10705511.2016.1168266.

Curran, P.J., Cole, V., Bauer, D.J., Hussong, A.M., & Gottfredson, N. (2016). Improving factor score estimation through the use of observed background characteristics. Structural Equation Modeling: A Multidisciplinary Journal. Advance Online Publication: doi: 10.1080/10705511.2016.1220839.

Also Available: Appendix A1 (GLM results), Appendix A2 (Score correlations at N=1000 & 2000), and Appendix A3 (RMSE results).

Dean, D.O., Cole, V. & Bauer, D.J. (2015). Delineating prototypical patterns of substance use initiations over time. Addiction, 110, 585-594, doi:10.1111/add.12816.

Also Available: Example code for fitting the model in Mplus

Curran, P.J., McGinley, J.S., Bauer, D.J., Hussong, A.M., Burns, A., Chassin, L., Sher, K., & Zucker, R. (2014). A moderated nonlinear factor model for the development of commensurate measures in integrative data analysis. Multivariate Behavioral Research, 49, 214-231, doi: 10.1080/00273171.2014.889594.

Also Available: Full MNLFA model can now be estimated with Mplus v. 7.3 or higher (including discrete items), see Annotated Mplus Example.

Dean, D.O., Bauer, D.J. & Shanahan, M.J. (2014). A discrete-time multiple event process survival mixture (MEPSUM) model. Psychological Methods, 19, 251-264, doi: 10.1037/a0034281.

Also Available: Supplemental material showing how to reformat the data, fit the model, and compute and plot the survival functions and lifetime distribution functions.

Gottfredson, N.C., Bauer, D.J. & Baldwin, S.A. (2014). Modeling change in the presence of non-randomly missing data: evaluating a shared parameter mixture model. Structural Equation Modeling: A Multidisciplinary Journal, 21, 196-209. doi: 10.1080/10705511.2014.882666.

Gottfredson, N.C., Bauer, D.J., Baldwin, S.A. & Okiishi, J. (2014). Using a shared parameter mixture model to estimate change during treatment when treatment termination is related to recovery speed. Journal of Consulting and Clinical Psychology, 82, 813-827. doi: 10.1037/a0034831.

Sterba, S.K. & Bauer, D.J. (2014). Predictions of individual change recovered with latent class or random coefficient growth models. Structural Equation Modeling: A Multidisciplinary Journal, 21, 1-19. doi: 10.1080/10705511.2014.915189

Bauer, D.J., Gottfredson, N.C., Dean, D., & Zucker, R.A. (2013). Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects. Psychological Methods, 18, 1-14. doi:10.1037/a0030639. PMCID:PMC3638804

Also Available: Supplemental material demonstrating how to fit dynamic group models with SAS and SAS macro for specifying the stabilizing banded covariance structure.

Bauer, D.J., Howard, A.L., Baldasaro, R.E., Curran, P.J., Hussong, A.M., Chassin, L., & Zucker, R.A. (2013). A trifactor model for integrating ratings across multiple informants. Psychological Methods, 18, 475-493. doi: 10.1037/a0032475. PMCID: PMC3964937

Also Available: Example Mplus Input File

Baldasaro, R.E., Shanahan, M.J. & Bauer, D.J. (2013). Psychometric properties of the mini-IPIP in a large, nationally representative sample of young adults. Journal of Personality Assessment, 95, 74-84. doi:10.1080/00223891.2012.700466

Hussong, A.M., Curran, P.J. & Bauer, D.J. (2013). Integrative data analysis in clinical psychology research. Annual Review of Clinical Psychology, 9, 61-89. doi:10.1146/annurev-clinpsy-050212-185522

Bauer, D.J., Baldasaro, R. & Gottfredson, N.C. (2012). Diagnostic procedures for detecting nonlinear relationships between latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 19, 157-177. doi:10.1080/10705511.2012.659612

Also available: The plotSEMM Rshiny application for generating plots developed by Pek & Chalmers (2015), and accompanying Mplus files

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, 47, 590-634. doi:10.1080/00273171.2012.692639

Also Available: Online appendix with additional results, details on literature review, and example SAS code for computing marginal means and (co)variances of random effects from SPGM estimates.

Baldwin, S.A., Bauer, D.J., Stice, E. & Rohde, P. (2011). Evaluating models for partially clustered designs. Psychological Methods, 16, 149-165. doi:10.1037/a0023464

Also Available: Supplemental material with SAS code for fitting the models.

Bauer, D.J. (2011). Evaluating individual differences in psychological processes. Current Directions in Psychological Science, 20, 115-118. doi:10.1177/0963721411402670

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. doi:10.1037/a0025813 PMCID:PMC3252624

Also Available: Online appendix with additional results.

Curran, P.J. & Bauer, D.J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583-619. doi:10.1146/annurev.psych.093008.100356 PMCID:PMC3059070

Pek, J., Losardo, D. & Bauer, D.J. (2011). Confidence intervals for a semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling, 18, 537-553. doi:10.1080/10705511.2011.607072

Also available: The plotSEMM Rshiny application for generating plots developed by Pek & Chalmers (2015), and accompanying Mplus files

Bauer, D.J. & Reyes, H.L.M. (2010). Modeling variability in individual development: differences of degree or kind?. Child Development Perspectives, 4, 114-122. doi:10.1111/j.1750-8606.2010.00129.x

Sterba, S.K. & Bauer, D.J. (2010) Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology, 22, 239-254. doi:10.1017/S0954579410000015

Note: Above manuscript accompanied by commentaries and rejoinder

Bauer, D.J. (2009). A note on comparing the estimates of models for cluster-correlated or longitudinal data with binary or ordinal outcomes. Psychometrika, 74, 97-105. doi:10.1007/s11336-008-9080-1

Bauer, D.J. & Cai, L. (2009). Consequences of unmodeled nonlinear effects in multilevel models. Journal of Educational and Behavioral Statistics, 34, 97-114. doi:10.3102/1076998607310504

Also Available: Appendices of analytic derivations, Detailed results of simulations

Bauer, D.J. & Hussong, A.M (2009). Psychometric approaches for developing commensurate measures across independent studies: traditional and new models. Psychological Methods, 14, 101-125. doi:10.1037/a0015583 PMCID:PMC2780030

Also Available: Supplemental material covering data preparation, model fitting, plotting of results, and scoring for moderated nonlinear factor analysis in SAS.

Also Available: Full MNLFA model can now be estimated with Mplus v. 7.3 or higher (including discrete items), see Annotated Mplus Example

Pek, J., Sterba, S.K., Kok, B.E. & Bauer, D.J. (2009). Estimating and visualizing nonlinear relations among latent variables: a semiparametric approach. Multivariate Behavioral Research, 44, 407-436. doi:10.1080/00273170903103290

Also available: The plotSEMM Rshiny application for generating plots developed by Pek & Chalmers (2015), and accompanying Mplus files

Bauer, D.J., Sterba, S.K. & Hallfors, D.D. (2008). Evaluating group-based interventions when control participants are ungrouped. Multivariate Behavioral Research, 43, 210-236. doi:10.1080/00273170802034810 PMCID:PMC2853949

Also Available: Demonstration within SAS, Demonstration within SPSS

Hussong, A., Bauer, D.J. & Chassin, L. (2008). Telescoped trajectories from alcohol initiation to disorder in children of alcoholic parents. Journal of Abnormal Psychology, 117, 63-78. doi:10.1037/0021-843X.117.1.63 PMCID:PMC2842006

Kamata, A. & Bauer, D.J. (2008). A note on the relationship between factor analytic and item response theory models . Structural Equation Modeling: A Multidisciplinary Journal, 15, 136-153 doi:10.1080/10705510701758406.

Kamata, A., Bauer, D.J. & Miyazaki, Y. (2008). Multilevel measurement modeling. In A.A. O’Connell & D.B. McCoach (Eds.) Multilevel Modeling of Educational Data (pp. 345-388). Charlotte, NC: Information Age Publishing.

Bauer, D.J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 42, 757-786. doi:10.1080/00273170701710338

Bauer, D.J., & Shanahan, M.J. (2007). Modeling complex interations: person-centered and variable-centered approaches. In T.D. Little, J.A. Bovaird & N.A. Card (Eds.) Modeling Contextual Effects in Longitudinal Studies (pp. 255-284). Mahwah, NJ: Lawrence Earlbaum Associates.

Also Available: Simulated data file, Mplus input file.

Curran, P.J., & Bauer, D.J. (2007). Building path diagrams for multilevel models. Psychological Methods, 12, 283-297. doi:10.1037/1082-989X.12.3.283

Meade, A.W. & Bauer, D.J. (2007). Power and precision in confirmatory factor analytic tests of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14, 611-635. doi:10.1080/10705510701575461

Bauer, D.J., Preacher, K.J. & Gil, K.M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations.. Psychological Methods, 11, 142-163. doi:10.1037/1082-989X.11.2.142

Also Available: FAQ regarding centering for separating within- and between-cluster effects.

Also Available: Online utility for conducting tests of random indirect effects using the Monte Carlo method.

Also Available for SAS users: Instructions on fitting models with random indirect effects in SAS, example SAS code, a macro for testing random indirect effects, and a simulated example data file.

Also Available for SPSS users: Instructions on fitting models with random indirect effects in SPSS, an Excel worksheet for testing random indirect effects, and a simulated example data file.

Also Available for HLM users: Instructions on fitting models with random indirect effects in HLM (version 6.06 and below), an Excel worksheet for testing random indirect effects, and a simulated example data file.

Hipp, J.R. & Bauer, D.J. (2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11, 36-53. doi:10.1037/1082-989X.11.1.36.

Curran, P.J., Bauer, D.J, & Willoughby, M.T. (2006). Testing and probing interactions in hierarchical linear growth models. In C.S. Bergeman & S.M. Boker (Eds.), The Notre Dame Series on Quantitative Methodology, Volume 1: Methodological Issues in Aging Research (pp. 99-129). Mahwah, NJ: Lawrence Erlbaum Associates.

Also Available: Web page for calculating and ploting simple slopes, regions of significance and confidence bands.

Preacher, K.J., Curran, P.J. & Bauer, D.J. (2006). Computational tools for probing interaction effects in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437-448. doi:10.3102/10769986031004437

Also Available: Web page for calculating and ploting simple slopes, regions of significance and confidence bands.

Bauer, D.J. (2005). The role of nonlinear factor-to-indicator relationships in tests of measurement equivalence. Psychological Methods, 10, 305-316. doi:10.1037/1082-989X.10.3.305

Bauer, D.J. (2005). A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 4, 513-535. doi:10.1207/s15328007sem1204_1

Also Available: R ‘shiny’ plotSEMM web application

Bauer, D.J. & Curran, P.J. (2005). Probing interactions in fixed and multilevel regression: inferential and graphical techniques. Multivariate Behavioral Research, 40, 373-400. doi:10.1207/s15327906mbr4003_5

Also Available: Web page for calculating and ploting simple slopes, regions of significance and confidence bands.

Hipp, J.R., Bauer, D.J. & Bollen, K.A. (2005). Conducting tetrad tests of model fit and contrasts of tetrad-nested models: a new SAS macro. Structural Equation Modeling, 12, 76-93. doi:10.1207/s15328007sem1201_4

Also Available: Software and documentation.

Bauer, D.J. & Curran, P.J. (2004). The integration of continuous and discrete latent variable models: potential problems and promising opportunities. Psychological Methods, 9, 3-29. doi:10.1037/1082-989X.9.1.3

Bollen, K.A. & Bauer, D.J. (2004). Automating the selection of instrumental variables. Sociological Methods & Research, 32, 425-452. doi:10.1177/0049124103260341

Also Available: SAS/IML program for selecting IVs when using 2SLS estimator for SEMs

Curran, P.J., Bauer, D.J., & Willoughby, M.T. (2004). Testing and probing main effects and interactions in latent curve analysis. Psychological Methods, 9, 220-237. doi:10.1037/1082-989X.9.2.220

Also Available: Web page for calculating and ploting simple slopes, regions of significance and confidence bands.

Hipp, J.R., Bauer, D.J., Curran, P.J. & Bollen, K.A. (2004). Crimes of opportunity or crimes of emotion? Testing two explanations of seasonal change in crime. Social Forces, 82, 1333-1372. doi:10.1353/sof.2004.0074

Shanahan, M.J. & Bauer, D.J. (2004). Developmental properties of transactional models: the case of life-events and mastery from adolescence to young adulthood. Development and Psychopathology, 16, 1095-1117. doi:10.1017/S0954579404040155

Bauer, D.J. (2003). Estimating multilevel linear models as structural equation models. Journal of Educational and Behavioral Statistics, 28, 135-167. doi:10.3102/10769986028002135

Also Available: Tabled Estimates and Program Files for empirical examples.

Bauer, D.J. & Curran, P.J. (2003a). Distributional assumptions of growth mixture models: Implications for over-extraction of latent trajectory classes. Psychological Methods, 8, 338-363. doi:10.1037/1082-989X.8.3.338.

Also Available: Monte Carlo Technical Appendix.

Bauer, D.J. & Curran, P.J. (2003b). Overextraction of latent trajectory classes: Much ado about nothing? Reply to Rindskopf (2003), Muthén (2003), and Cudeck and Henly (2003). Psychological Methods, 8, 384-393. doi:10.1037/1082-989X.8.3.384