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December 20, 2014
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Papers Using Special Mplus Features

References on this page are ordered by date. References can also be viewed ordered by topic.



  • Dunn, C., Masyn, K.E., Jones, S.M., Subramanian, S.V., & Koenen, K.C. (2014). Measuring psychosocial environments using individual responses: an application of multilevel factor analysis to examining students in schools. Prevention Science. DOI 10.1007/s11121-014-0523-x
    download paper show abstract

  • Asparouhov, T. & Muthén, B. (2014). General random effect latent variable modeling: Random subjects, items, contexts, and parameters. Version 2. Forthcoming in the edited book "Advances in Multilevel Modeling for Educational Research: Addressing Practical Issues Found in Real-World Applications”.
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  • Asparouhov, T. & Muthén, B. (2012). Comparison of computational methods for high dimensional item factor analysis.
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  • Kelava, A., Nagengast, B., Brandt, H. (2014). A nonlinear structural equation mixture modeling approach for nonnormally distributed latent predictor variables. Structural Equation Modeling: A Multidisciplinary Journal, 21:3, 468-481, DOI: 10.1080/10705511.2014.915379
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  • Lugtig, P. (2014). Separating stayers, fast attriters, gradual attriters, and lurkers. Sociological Methods & Research, 43(4) 699-723. DOI: 10.1177/0049124113520305
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  • Morgan, G. B. (2014). Mixed mode latent class analysis: An examination of fit index performance for classification. Structural Equation Modeling: A Multidisciplinary Journal, DOI: 10.1080/10705511.2014.935751
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  • Wang, L., and Preacher, K. J. (2014). Modern mediation analysis using Bayesian methods. Structural Equation Modeling: A Multidisciplinary Journal. DOI: 10.1080/10705511.2014.935256
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  • Bruyneel, L., Baoyue, L., Squires, A., Spotbeen, S., Meuleman, B., Lesaffre, E., & Sermeus, W. (2014). Bayesian multilevel MIMIC modeling for studying measurement invariance in cross-group comparisons. Forthcoming in Medical Care, doi: 10.1097/MLR.0000000000000164.
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  • Feingold, A. (2014). Confidence interval estimation for standardized effect sizes in multilevel and latent growth modeling. Journal of Consulting and Clinical Psychology, pre-print article.
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  • Morin, A.J.S., Arens, A.K., & Marsh, H.W. (Accepted, 22 August 2014). A Bifactor Exploratory Structural Equation Modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling.
    download paper download supplement show abstract

  • Aichholzer, J. (2014). Random intercept EFA of personality scales. Journal of Research in Personality, 53: 1-4.
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  • Barendse, M.T., Oort, F.J., & Timmerman, M.E. (2014). Using exploratory factor analysis to determine the dimensionality of discrete responses. Structural Equation Modeling: A Multidisciplinary Journal, 00: 1-15.
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  • McArdle, J., Hamagami, F., Chang, J.Y., and Hishinuma, E.S. (2014). Longitudinal dynamic analyses of depression and academic achievement in the Hawaiian high schools health survey using contemporary latent variable change models. Structural Equation Modeling: A Multidisciplinary Journal, 21: 1–22.
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  • Kelcey, B., McGinn, D. & Hill, H. (2014). Approximate measure invariance in cross-classified rater-mediated assessments.
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  • Asparouhov T. & Muthén, B. (2014). Multiple-group factor analysis alignment. Structural Equation Modeling: A Multidisciplinary Journal, 21, 1-14. DOI: 10.1080/10705511.2014.919210 An earlier version of this paper was posted as web note 18.
    download paper download Mplus files show abstract

  • Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014): A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal, 21, 439-454.
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  • Muthén, B. & Asparouhov T. (2014). Growth mixture modeling with non-normal distributions. Revised version, October 9, 2014. Statistics in Medicine. DOI: 10.1002/sim.6388.
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  • Asparouhov, T. & Muthén B. (2014). Structural equation models and mixture models with continuous non-normal skewed distributions. Web note 19. Version 2.
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  • Asparouhov, T. & Muthén, B. (2014). Residual associations in latent class and latent transition analysis. Structural Equation Modeling: A Multidisciplinary Journal. DOI:10.1080/10705511.2014.935844
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  • Muthén, B. & Asparouhov T. (2014). Causal effects in mediation modeling: An introduction with applications to latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 12-23. DOI:10.1080/10705511.2014.935843
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  • Muthén, B. & Asparouhov T. (2014). IRT studies of many groups: The alignment method. Frontiers in Psychology, Volume 5, DOI: 10.3389/fpsyg.2014.00978
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  • Whittaker, T.A., Beretvas, S.N., & Falbo, T. (2014) Dyadic curve-of-factors model: An introduction and illustration of a model for longitudinal nonexchangeable dyadic data, Structural Equation Modeling: A Multidisciplinary Journal, 21:2, 303-317, DOI: 10.1080/10705511.2014.882695
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  • Depaoli, S. (2013). Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation. Psychological Methods, 18(2), 186-219. doi: 10.1037/a0031609
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  • Koukounari, A., Donnelly, C.A., Moustaki, I., Tukahebwa, E.M., Kabatereine, N.B., et al. (2013). A latent markov modelling approach to the evaluation of circulating cathodic antigen strips for schistosomiasis diagnosis pre- and post-Praziquantel treatment in Uganda. PLoS Comput Biol 9(12): e1003402. doi:10.1371/journal.pcbi.1003402
    Mplus scripts are available from the author.
    download paper contact first author show abstract

  • Geiser, C., Bishop, J., Lockhart, G., Shiffman, S., & Grenard, J. (2013). Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models. Frontiers in Psychology, 4, 975. doi: 10.3389/fpsyg.2013.00975
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  • Using multivariate multilevel survival analysis to study reliability and change in hazard rates of emotions derived from parent-child dyadic social interaction. Forthcoming in Tom Dishion and Jim Snyder (Eds.), Handbook of Coercion.
    download chapter

  • Asparouhov, T. & Muthén, B. (2014) Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21:3, 329-341. The posted version corrects several typos in the published version. An earlier version of this paper was posted as web note 15.
    download paper download scripts show abstract

  • Masyn, K. E. (2013). Latent class analysis and finite mixture nodeling. In P. Nathan and T. Little (Eds.), The Oxford Handbook of Quantitative Methods (pp. 551-611). New York, NY. Oxford University Press.
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  • Sterba, S. (2013) Understanding linkages among mixture models. Multivariate Behavioral Research, 48:775-815. DOI: 10.1080/00273171.2013.827564
    download paper download appendix show abstract

  • Masyn, K., Petras, H. and Liu, W. (2013). Growth Curve Models with Categorical Outcomes. In Encyclopedia of Criminology and Criminal Justice (pp. 2013-2025). Springer.
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  • Kevin J. Grimm , Joel S. Steele , Nilam Ram & John R. Nesselroade (2013) Exploratory latent growth models in the structural equation modeling framework. Structural Equation Modeling: A Multidisciplinary Journal, 20:4, 568-591, DOI: 10.1080/10705511.2013.824775
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  • Muthén, B. & Asparouhov, T. (2013). New methods for the study of measurement invariance with many groups.
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  • Kam, C., Morin, A. J. S., Meyer, J. P., & Topolnytsky, A. (Accepted). Are commitment profiles stable and predictable? A latent transition analysis. Journal of Management.
    download paper download supplement show abstract

  • Fong, T. C. T., & Ho, R. T. H. (2013). Factor analysis of the hospital depression and anxiety scale: A Bayesian structural equation modeling approach. Quality of Life Research, 10.1007/s11136-013-0429-2.
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  • Prince, M. and Maisto, S. (2013). The clinical course of alcohol use disorders: Using joinpoint analysis to aid in interpretation of growth mixture models. Drug and Alcohol Dependence. DOI: 10.1016/j.drugalcdep.
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  • Stoolmiller, M. and Snyder, J. (2013). Embedding multilevel survival analysis of dyadic social interaction in structural equation models: Hazard rates as both outcomes and predictors. Journal of Pediatric Psychology, doi: 10.1093/jpepsy/jst076.
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  • van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf , J.B., Neyer, F.J. & van Aken, M.A.G. (2013). A gentle introduction to Bayesian analysis: Applications to research in child development. Child Development. DOI: 10.1111/cdev.12169.
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  • van de Schoot, R., Tummers, L., Lugtig, P., Kluytmans, A., Hox, J. & Muthén, B. (2013). Choosing between Scylla and Charybdis? A comparison of scalar, partial and the novel possibility of approximate measurement invariance. Frontiers in Psychology, 4, 1-15. doi: 10.3389/fpsyg.2013.00770.
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  • Zyphur, M & Oswald, F. Bayesian estimation and inference: A user's guide. (2013, August 11) Journal of Management DOI: 10.1177/0149206313501200
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  • Morin, A.J.S., & Marsh, H.W. (2014). Disentangling shape from levels effects in person- centered analyses: An illustration based university teacher multidimensional profiles of effectiveness. Structural Equation Modeling: A Multidisciplinary Journal, 21: 1–21.
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  • Morin, A.J.S., Marsh, H.W., Nagengast, B., & Scalas, L.F. (2014). Doubly latent multilevel analyses of classroom climate: An illustration. The Journal of Experimental Education. DOI: 10.1080/00220973.2013.769412
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  • Feingold, A., Tiberio, S.S., & Capaldi, D.M. (2013). New approaches for examining associations with latent categorical variables: Applications to substance abuse and aggression. Psychology of Addictive Behaviors. DOI: 10.1037/a0031487
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  • Grimm, K., Zhang Z., Hamagami, F. & Mazzocco, M. (2013). Modeling nonlinear change via latent change and latent acceleration frameworks: Examining velocity and acceleration of growth trajectories. Multivariate Behavioral Research, DOI: 10.1080/00273171.2012.755111.
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  • Herrmann, A., & Pfister, H. (2013). Simple measures and complex structures: Is it worth employing a more complex model of personality in Big Five inventories? Journal of Research in Personality. DOI: 10.1016/j.jrp.2013.05.004
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  • James L. Peugh, David DiLillo & Jillian Panuzio (2013) Analyzing mixed-dyadic data using structural equation models. Structural Equation Modeling. Pages: 314-337 DOI: 10.1080/10705511.2013.769395
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  • Nidhi Kohli & Jeffrey R. Harring (2013): Modeling growth in latent variables using a piecewise function. Multivariate Behavioral Research, 48:3,370-397 DOI: 10.1080/00273171.2013.778191
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  • Koukounari, A., Moustaki, I., Grassly, N.C., Blame, I. M., Basáñez, M., Gambhor, M., Mabey, D. C. W., Bailey, R. L., Burton, M. J., Solomon, A. W., & Donnelly, C. A. (2013). Using a nonparametric multilevel latent markov model to evaluate diagnostics for trachoma. American Journal of Epidemiology. DOI: 10.1093/aje/kws345 Click here to view web materials associated with this paper.
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  • Morin, A.J.S., Marsh, H.W., & Nagengast, B. (2013). Chapter 10. Exploratory Structural Equation Modeling. In Hancock, G. R., & Mueller, R. O. (Eds.). (2013). Structural equation modeling: A second course (2nd ed.). Charlotte, NC: Information Age Publishing, Inc. Supplementary materials used in this chapter can be found here and here.
    contact first author

  • Muthén, B. & Asparouhov, T. (2013). BSEM measurement invariance analysis.
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  • Muthén, B. & Asparouhov, T. (2013). Item response modeling in Mplus: A multi-dimensional, multi-level, and multi-timepoint example. Forthcoming in Linden & Hambleton (2013). Handbook of item response theory: Models, statistical tools, and applications.
    download paper download Table 6 output download output files show abstract

  • Sobel, M. & Muthén, B. (2013). Compliance mixture modelling with a zero effect complier class and missing data. Forthcoming in Biometrics.
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  • Kaplan & Depaoli (2012). Bayesian Structural Equation Modeling, excerpt from Handbook of Structural Equation Modeling, The Guilford Press.
    download chapter

  • Asparouhov, T. & Muthén B. (2012). Comparison of computational methods for high dimensional item factor analysis.
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  • Golay, P., Reverte, I., Rossier, J., Favez, N., & Lecerf, T. (2012, November 12). Further insights on the French WISC–IV factor structure through Bayesian structural equation modeling. Psychological Assessment. Advance online publication. DOI: 10.1037/a0030676
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  • Böckenholt, U. (2012, April 30). Modeling Multiple Response Processes in Judgment and Choice. Psychological Methods. Advance online publication. DOI: 10.1037/a0028111
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  • Brown, A. & Maydeu-Olivares, A. (2012). Fitting a Thurstonian IRT model to forced-choice data using Mplus. Behavioral Research Methods, Advance online publication. DOI: 10.3758/s13428-012-0217-x.
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  • Geiser, C. & Lockhart, G. (2012). A comparison of four approaches to account for method effects in latent state-trait analyses. Psychological Methods, Online First Publication, February 6, 2012. doi: 10.1037/a0026977
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  • Hox, J., van de Schoot, R. & Matthijsse, S. (2012). How few countries will do? Comparative survey analysis from a Bayesian perspective. Survey Research Methods, 6:2, 87-93.
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  • Ibironke O., Koukounari A., Asaolu S., Moustaki I., & Shiff C. (2012) Validation of a new test for schistosoma haematobium based on detection of dra1 DNA fragments in urine: Evaluation through Latent Class Analysis. PLoS Negl Trop Dis 6(1): e1464. doi:10.1371/journal.pntd.0001464
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  • Malone, P. S., Northrup, T. F., Masyn, K. E., Lamis, D. A., & Lamont, A. E. (2012). Initiation and persistence of alcohol use in United States Black, Hispanic, and White male and female youth. Addictive Behaviors, 37, 299-305.
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  • Marsh, H. W., Nagengast, B. & Morin, A. J. S. (2012). Measurement invariance of big-five factors over the life span: ESEM tests of gender, age, plasticity, maturity, and La Dolce Vita effects. Developmental Psychology. Advance online publication. doi: 10.1037/a0026913
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  • Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A more flexible representation of substantive theory. Psychological Methods, 17, 313-335. Click ""download paper"" below for the latest version of October 21, 2011. Download the 2nd version dated April 14, 2011. Click here to view the seven web tables referred to in the paper and here to view Mplus inputs, data, and outputs used in this version of paper. Download the 1st version dated September 29, 2010 containing a MIMIC section and more tables, and the corresponding Mplus inputs, data, and outputs here. The seven web tables correspond to tables 8, 10, 17, 18, 19, 20, and 21 of the first version.
    download paper and rejoinder contact first author show abstract

  • Muthén, B. & Asparouhov, T. (2012). Rejoinder to MacCallum, Edwards, and Cai (2012) and Rindskopf (2012): Mastering a new method. Psychological Methods, Vol 17(3), Sep 2012, 346-353. doi: 10.1037/a0029214 Click here to download the Mplus scripts and data that correspond to this paper.
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  • Power, R.A., Muthén, B.,Henigsberg, N., Mors, O., Placentino, A., Mendlewicz, J., Maier, W., McGuffin, P., Lewis, C.M., & Uher, R. (2012). Non-random dropout and the relative efficacy of escitalopram and nortriptyline in treating major depressive disorder. Journal of Psychiatric Research. In press.
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  • Wall, M. M., Guo, J., & Amemiya, Y. (2012). Mixture factor analysis for approximating a nonnormally distributed continuous latent factor with continuous and dichotomous observed variables. Multivariate Behavioral Research, 47:2, 276-313.
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  • Van de Schoot, R., Marjolein Verhoeven & Herbert Hoijtink (2012): Bayesian evaluation of informative hypotheses in SEM using Mplus: A black bear story, European Journal of Developmental Psychology, DOI:10.1080/17405629.2012.732719
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  • Van de Schoot, R., Hoijtink, H., Hallquist, M. N., & Boelen, P.A. (2012). Bayesian evaluation of inequality-constrained hypotheses in SEM models using Mplus. Structural Equation Modeling, 19, 593-609.
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  • Asparouhov, T. & Muthén, B. (2011). Using Bayesian priors for more flexible latent class analysis. Proceedings of the 2011 Joint Statistical Meetings. Submitted for publication. Click here to view Mplus inputs, data, and outputs used in this paper.
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  • Brown, A. & Maydeu-Olivares, A. (2011). Item response modeling of forced-choice questionnaires. Educational and Psychological Measurement, 71:3, 460–502.
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  • Enders, C. (2011). Missing not at random models for latent growth curve analyses. Psychological Methods, 16, 1-16.
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  • Finch, W.H. & Bronk, K.C. (2011). Conducting confirmatory latent class analysis using Mplus. Structural Equation Modeling, 18, 132-151.
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  • Gueorguieva, R., Mallinckrodt, C., & Krystal, J. (2011). Trajectories of depression severity in clinical trials of Duloxetine. Arch Gen Psychiatry, 68(12): 1227-1237.
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  • Leoutsakos, J.S., Muthén, B.O., Breitner, J.C.S. & Lyketsos, C.G. (2011). Effects of NSAID treatments on cognitive decline vary by phase of pre-clinical Alzheimer disease: Findings from the randomized controlled ADAPT trial. International Journal of Geriatric Psychiatry. In Press.
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  • Meeus, W., van de Schoot, R., Keijsers, L. & Branje, S. (2011). Identity statuses as developmental trajectories. A five-wave longitudinal study in early to middle and middle to late adolescents. Journal of Youth and Adolescence.
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  • Meeus, W., van de Schoot, R., Klimstra, T. & Branje, S. (2011). Personality types in adolescence: Change and stability and links with adjustment and relationships: A five-wave longitudinal study in early-to-middle and middle-to-late adolescence. Developmental Psychology, 47 (4), 1181–1195.
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  • Morin, A.J.S., Moullec, G., Maïano, C., Layet, L., Just, J.L. & Ninot, G. (2011). Psychometric properties of the Center for Epidemiologic Studies Depression Scale (CES-D) in French clinical and non-clinical adults. Forthcoming in Epidemiology and Public Health/Revue d’Épidémiologie et de Santé Publique. Mplus inputs used in this paper can be downloaded here.
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  • Muthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus. Click here to view the Technical appendix that goes with this paper and click here for the Mplus input appendix. Click here to view Mplus inputs, data, and outputs used in this paper.
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  • Muthén, B. & Asparouhov, T. (2011). Beyond multilevel regression modeling: Multilevel analysis in a general latent variable framework. In J. Hox & J.K. Roberts (eds), Handbook of Advanced Multilevel Analysis, pp. 15-40. New York: Taylor and Francis.
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  • Muthén, B., Asparouhov, T., Hunter, A. & Leuchter, A. (2011). Growth modeling with non-ignorable dropout: Alternative analyses of the STAR*D antidepressant trial. Psychological Methods, 16, 17-33. Click here to view Mplus outputs used in this paper.
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  • Muthén, B., Brown, C.H., Hunter, A., Cook, I.A. & Leuchter, A.F. (2011). General approaches to analysis of course: Applying growth mixture modeling to randomized trials of depression medication. In P.E. Shrout (ed.), Causality and Psychopathology: Finding the Determinants of Disorders and their Cures (pp. 159-178). New York: Oxford University Press.
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  • Myers, N.D., Ahn, S. & Ying, J. (2011). Sample size and power estimates for a confirmatory factor analytic model in exercise and sport: A Monte Carlo approach. Research Quarterly for Exercise and Sport (Measurement and Evaluation section), 82, 412-423. Click here to view Mplus inputs, data, and outputs used in this paper.
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  • Petras, H., Masyn, K. & Ialongo, N. (2011). The developmental impact of two first grade preventive interventions on aggressive/disruptive behavior in childhood and adolescence: An application of latent transition growth mixture modeling. Accepted for publication in Prevention Science.
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  • Reinecke, J. & Seddig, D. (2011). Growth mixture models in longitudinal research. AStA Advances in Statistical Analysis, 95, 415-434.
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  • Sawatzky, R., Ratner, P.A., Kopec, J.A., & Zumbo, B.D. (2011). Latent variable mixture models: A promising approach for the validation of patient reported outcomes. Quality of Life Research. DOI:10.1007/s11136-011-9976-6.
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  • Schmitt, T. (2011). (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29, 304-321.
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  • Tueller, S.J,, Drotar, S. & Lubke, G.H. (2011). Addressing the problem of switched class labels in latent variable mixture model simulation studies. Structural Equation Modeling, 18, 110-131.
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  • Van de Schoot, R., Hoijtink, H., Hallquist, M. N., & Boelen, P.A. (2011). Bayesian evaluation of inequality-constrained hypotheses in SEM models using Mplus. Structural Equation Modeling. In press.

  • Walton, K.E., Ormel, J. & Krueger, R.F. (2011). The dimensional nature of externalizing behaviors in adolescence: Evidence from a direct comparison of categorical, dimensional, and hybrid models. Forthcoming in Journal of Ab Child Psych. Forthcoming in Journal of Abnormal Child Psychology.
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  • Asparouhov, T. & Muthén, B. (2010). Bayesian analysis of latent variable models using Mplus. Technical Report. Version 4. Click here to view Mplus inputs, data, and outputs used in this paper.
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  • Asparouhov, T. & Muthén, B. (2010). Bayesian analysis using Mplus: Technical implementation. Technical Report. Version 3.
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  • Asparouhov, T. & Muthén, B. (2010). Multiple imputation with Mplus. Technical Report. Version 2. Click here to view Mplus inputs, data, and outputs used in this paper.
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  • Asparouhov, T. & Muthén, B. (2010). Plausible values for latent variables using Mplus. Technical Report.
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  • Bou, J.C. & Satorra, A. (2010). A multigroup structural equation approach: A demonstration by testing variation of firm profitability across EU samples. Organizational Research Methods.
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  • Cho, S., Cohen, A., Kim, S. & Bottge, B. (2010). Latent transition analysis with a mixture item response theory measurement model. Applied Psychological Measurement, 34(7), 483–504.
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  • Clark, S.L. (2010). Mixture modeling with behavioral data. Doctoral dissertation, University of California, Los Angeles.
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  • Dedrick, R & Greenbaum, P. (2010). Multilevel confirmatory factor analysis of a scale measuring interagency collaboration of children’s mental health agencies. Journal of Emotional and Behavioral Disorders, XX(X), 1-14.
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  • Emsley, R., Dunn, G. & White, I. (2010). Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Statistical Methods in Medical Research, 19, 237–270.
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  • Geiser, C., Eid, M., Nussbeck, F.W., Courvoisier, D.S. & Cole, D.A. (2010). Analyzing true change in longitudinal multitrait-multimethod studies: Application of a multimethod change model to depression and anxiety in children. Developmental Psychology, 46, 29-45.
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  • Grimm, K.J., Ram, N. & Estabrook, R. (2010). Nonlinear structured growth mixture models in Mplus and OpenMx. Multivariate Behavioral Research, 45, 887-909. The technical appendix for this paper can be viewed here.
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  • Hayes, A.F. & Preacher, K.J. (2010). Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behavioral Research, 45, 627-660.
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  • Henry, K. & Muthén, B. (2010). Multilevel latent class analysis: An application of adolescent smoking typologies with individual and contextual predictors. Structural Equation Modeling, 17, 193-215. Click here to view figures and syntax for all models.
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  • Hunter, A. M., Muthén, B.O., Cook, I.A. & Leuchter, A. F. (2010). Antidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder. Journal of Psychiatric Research, 44, 90-98.
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  • Jo, B., Ginexi, E. & Ialongo, N. (2010). Handling missing data in randomized experiments with noncompliance. Forthcoming in Prevention Science.
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  • Leite, W. & Cooper, L. (2010). Detecting social desirability bias using factor mixture models. Multivariate Behavioral Research, 45:2, 271-293.
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  • Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J. S., Trautwein, U. & Nagengast, B. (2010). A new look at the big-five factor structure through exploratory structural equation modeling. Psychological Assessment, 22, 471.491.
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  • Masyn, K. E. & Henderson, C.E. (2010). Exploring the latent structures of psychological constructs in social development using the dimensional-categorical spectrum. Social Development, 19, 3, 2010.
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  • Meeus, W., Van de Schoot, R., Keijsers, L., Schwartz, S. J. & Branje, S. (2010). On the progression and stability of adolescent identity formation. A five-wave longitudinal study in early-to-middle and middle-to-late adolescence. Child Development, Volume 81, Number 5, Pages 1565–1581.
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  • Morin, A.J., Mainano, C., Nagengast, B., Marsh, H.W., Morizot, J. & Janosc, M. (2010). General growth mixture analysis of adolescents' developmental trajectories of anxiety: The impact of untested invariance assumptions on substantive interpretations. Forthcoming in Structural Equation Modeling.
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  • Morin, A.J.S., Maïano, C., Marsh, H.W., Janosz, M. & Nagengast, B. (accepted). (2010). The longitudinal interplay of adolescents’ self-esteem and body image: A conditional autoregressive latent trajectory analysis. Accepted for publication in Multivariate Behavioral Research. Click here for supplemental materials.
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  • Muthén, B. (2010). Bayesian analysis in Mplus: A brief introduction. Technical Report. Version 3. Click here to view Mplus inputs, data, and outputs used in this paper.
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  • Petras, H., Masyn, K., Buckley, J., Ialongo, N. & Kellam, S. (2010). Who is most at risk for school removal? A multilevel discrete-time survival analysis of individual and contextual-level influences. Accepted for publication in the Journal of Educational Psychology.
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  • Pickles, P. & Croudace, T. (2010). Latent mixture models for multivariate and longitudinal outcomes. Statistical Methods in Medical Research, 19, 271–289.
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  • Preacher, K., Zyphur, M. & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15, 209-233.
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  • Qureshi, I. & Fang, Y. (2010). Socialization in open source software projects: A growth mixture modeling approach. Organizational Research Methods, 1-31.
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  • Reise, S. P., Moore, T. M., & Maydue-Olivares, A. (2010). Target rotations and assessing the impact of model violations on the parameters of unidimensional item response theory models. Forthcoming in Educational and Psychological Measurement.
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  • Reynolds, M.R., Keith, T.Z. & Beretvas, S.N. (2010). Use of factor mixture modeling to capture Spearman's law of diminishing returns. Intelligence 38, 231–241.
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  • Roesch, S. C. , Aldridge, A. A. , Stocking, S. N. , Villodas, F. , Leung, Q., Bartley, C. E. & Black, L. J. (2010). Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation. Multivariate Behavioral Research, 45, 767-789.
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  • Sass, D.A. & Schmitt, T.A. (2010). A comparative investigation of rotation criteria within exploratory factor analysis. Multivariate Behavioral Research, 45, 73-103.
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  • van de Schoot, R., Hoijtink, H. & Dekovic, M. (2010). Testing inequality constrained hypotheses in SEM models. Structural Equation Modeling, 17, 443-463.
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  • Witkiewitz, K., Maisto, S. A., & Donovan, D. M. (2010). A comparison of methods for estimating change in drinking following alcohol tTreatment. Alcoholism: Clinical & Experimental Research. DOI: 10.1111/j.1530-0277.2010.01308.x
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  • Asparouhov, T. & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397-438.
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  • Benner, A. & Graham, S. (2009). The transition to high school as a developmental process among multiethnic urban youth. Child Development, 80:2, 356–376.
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  • Clark, S. & Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis. Submitted for publication.
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  • Clark, S.L., Muthén, B., Kaprio, J., D’Onofrio, B.M., Viken, R., Rose, R.J., Smalley, S. L. (2009). Models and strategies for factor mixture analysis: Two examples concerning the structure underlying psychological disorders. Submitted for publication.
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  • Feldman, B.J., Masyn, K.E. & Conger, R.D. (2009). New approaches to studying problem behaviors: A comparison of methods for modeling longitudinal, categorical adolescent drinking data. Developmental Psychology, 45, 3, 652-676.
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  • Forero, C.G. & Maydeu-Olivares, A. (2009). Estimation of IRT graded response models: Limited versus full information methods. Psychological Methods, 14:3, 275–299.
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  • Gottfredson, N.C., Panter, A.T., Daye, C.E., Allen, W.F. & Wightman, L.F. (2009). The effects of educational diversity in a national samples of law students: Fitting multilevel latent variable models in data with categorical indicators. Multivariate Behavioral Research, 44, 305-331.
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  • Grimm, K.J. & Ram, N. (2009). A second-order growth mixture model for developmental research. Research in Human Development, 6, 121-143.
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  • Grimm, K.J. & Ram, N. (2009). Nonlinear growth models in Mplus and SAS. Structural Equation Modeling, 16, 676-701. Click here to view related Mplus scripts.
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  • Kerner, B. & Muthén, B. (2009). Growth mixture modelling in families of the Framingham Heart Study. BMC Proceedings, 3, 1-5.
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  • Kim, Y.K. & Muthén, B. (2009). Two-part factor mixture modeling:  Application to an aggressive behavior measurement instrument. Structural Equation Modeling, 16, 602-624.
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  • Marsh, H.W., Lüdtke, O., Trautwein, U., & Morin, A.J.S. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person- and variable- centered approaches to theoretical models of self-concept. Structural Equation Modeling, 16:2,191-225.
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  • Marsh, H.W., Ludtke, O., Robitzsch, A., Trautwein, U., Asparouhov, T., Muthén, B., & Nagengast, B. (2009). Doubly-latent models of school contextual effects: Integrating multilevel and structural equation approaches to control measurement and sampling errors. Multivariate Behavioral Research, 44, 764-802. Click here to view the appendix that goes with this paper.
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  • Marsh, H.W., Muthén, B., Asparouhov, A., Lüdtke, O., Robitzsch, A., Morin, A.J.S., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling, 16, 439-476.
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  • Masyn, K. E. (2009). Discrete-time survival factor mixture analysis for low-frequency recurrent event histories. Research in Human Development, 6, 165-194.
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  • Moosbrugger, H., Schermelleh-Engel, K., Kelava; A. & Klein, A. G (2009). Testing multiple nonlinear effects in structural equation modeling: A comparison of alternative estimation approaches. Invited Chapter in T. Teo & M. S. Khine (Eds.), Structural Equation Modelling in Educational Research: Concepts and Applications. Rotterdam, NL: Sense Publishers.
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  • Muthén, B. & Asparouhov, T. (2009). Multilevel regression mixture analysis. Journal of the Royal Statistical Society, Series A, 172, 639-657.
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  • Muthén, B. & Asparouhov, T. (2009). Growth mixture modeling: Analysis with non-Gaussian random effects. In Fitzmaurice, G., Davidian, M., Verbeke, G. & Molenberghs, G. (eds.), Longitudinal Data Analysis, pp. 143-165. Boca Raton: Chapman & Hall/CRC Press.
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  • Muthén, B. & Brown, H. (2009). Estimating drug effects in the presence of placebo response: Causal inference using growth mixture modeling. Statistics in Medicine, 28, 3363-3385.
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  • Muthén, B., Asparouhov, T., Boye, M., Hackshaw, M. & Naegeli, A. (2009). Applications of continuous-time survival in latent variable models for the analysis of oncology randomized clinical trial data using Mplus. Technical Report. Click here to view Mplus outputs used in this paper.
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  • Pek, J., Sterba, S., Kok, B. & Bauer, D. (2009). Estimating and visualizing nonlinear relations among latent variables: A semiparametric approach. Multivariate Behavioral Research, 44, 407 - 436.
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  • Petras, H & Masyn, K. (2009). General growth mixture analysis with antecedents and consequences of change. To appear in Piquero, A. & Weisburd, D., Handbook of Quantitative Criminology.
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  • Petras, H., Nieuwbeerta, P., & Piquero, A.R. (2009). Participation and frequency during criminal careers over the life span. Accepted for publication in Criminology.
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  • Raykov, T. (2009). Evaluation of convergent and discriminant validity with multitrait-multimethod correlations. Forthcoming in British Journal of Mathematical and Statistical Psychology.
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  • Raykov, T., Dimitrov, D.M. & Asparouhov, T. (2009). Evaluation of scale reliability with binary measures using latent variable modeling. Forthcoming in Structural Equation Modeling.
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  • Stormshak, E.A., Connell, A. & Dishion, T.J. (2009). An adaptive approach to family-centered intervention in schools: Linking intervention engagement to academic outcomes in middle and high school. Prevention Science, 10, 221-235.
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  • Tucker-Drob, E.M. (2009). Differentiation of cognitive abilities across the lifespan. Accepted for publication in Developmental Psychology.
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  • Uher, R., Muthén, B., Souery, D., Mors, O., Jaracz, J., Placentino, A., Petrovic, A., Zobel, A., Henigsberg, N., Rietschel, M., Aitchison, K., Farmer, A. & McGuffin, P. (2009). Trajectories of change in depression severity during treatment with antidepressants. Psychological Medicine, published online October 29, 2009.
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  • Uher, R., Muthén, B., Souery, D., Mors, O., Jaracz, J., Placentino, A., Petrovic, A., Zobel, A., Henigsberg, N., Rietschel, M., Aitchison, K., Farmer, A. & McGuffin, P. (2009). Technical appendix: Methods and results of growth mixture modelling. Psychological Medicine, published online October 29, 2009.
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  • Van Horn, M. L., Jaki, T., Masyn, K., Ramey, S. L., Smith, J., & Antaramian, S. (2009). Assessing differential effects: Applying regression mixture models to identify variations in the influence of family resources on academic achievement. In press, Child Development.
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  • Woods, C.M. (2009). Evaluation of MIMIC-Model Methods for DIF Testing With Comparison to Two-Group Analysis. Multivariate Behavioral Research, 44:1,1-27.
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  • Asparouhov, T. & Muthén, B. (2008). Multilevel mixture models. In Hancock, G. R., & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models, pp. 27-51. Charlotte, NC: Information Age Publishing, Inc. Click here for information about the book.
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  • Bartels, M., Cacioppo, J.T., Hudziak, J.J., & Boomsma, D.I. (2008). Genetic and environmental contributions to stability in loneliness throughout childhood. American Journal of Medical Genetics Part B, 147B, 385-391.
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  • Boscardin, C., Muthén, B., Francis, D. & Baker, E. (2008). Early identification of reading difficulties using heterogeneous developmental trajectories. Journal of Educational Psychology, 100, 192-208.
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  • Cheung, M.W.L. (2008). A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling. Psychological Methods, 13, 182–202.
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  • D’Onofrio, B.M., Hulle, C.A., Waldman, I.D., Rodgers, J. L. Harden, K.P., Rathouz, P.J. & Lahey, B.B. (2008). Smoking during pregnancy and offspring externalizing problems: An exploration of genetic and environmental confounds. Development and Psychopathology, 20, 139-164. Mplus scripts can be obtained from the first author.
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  • Dumenci, L. & Achenbach, T.M. (2008). Effects of estimation methods on making trait-level inferences from ordered categorical items for assessing psychopathology. Psychological Assessment, 20, 55-62.
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  • Hatton, H., Donnellan, M.B., Masyn, K., Feldman, B.J., Larsen-Rife, D., & Conger, R.D. (2008). Family and individual difference predictors of trait aspects of negative interpersonal behaviors during emerging adulthood. Journal of Family Psychology, 22, 448-455.
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  • Jo, B., Asparouhov, T. & Muthén, B. (2008). Intention-to-treat analysis in cluster randomized trials with noncompliance. Statistics in Medicine, 27, 5565-5577.
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  • Jo, B., Asparouhov, T., Muthén, B., Ialongo, N. & Brown, H. (2008). Cluster randomized trials with treatment noncompliance. Psychological Methods, 13, 1-18.
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  • Jung, T. & Wickrama, K.A.S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2, 302-317.
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  • Kaplan, D. (2008). An overview of Markov chain methods for the study of stage-sequential developmental processes. Developmental Psychology, 44, 457-467.
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  • Kreuter, F. & Muthén, B. (2008). Analyzing criminal trajectory profiles: Bridging multilevel and group-based approaches using growth mixture modeling. Journal of Quantitative Criminology, 24, 1-31. Click here to download Mplus input and output files associated with this paper.
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  • Kreuter, F. & Muthén, B. (2008). Longitudinal modeling of population heterogeneity: Methodological challenges to the analysis of empirically derived criminal trajectory profiles. In Hancock, G. R., & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models, pp. 53-75. Charlotte, NC: Information Age Publishing, Inc. Click here for information about the book.
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  • Kreuter, F., Yan, T. & Tourangeau, R. (2008). Good item or bad – can latent class analysis tell?: The utility of latent class analysis for the evaluation of survey questions. Journal of the Royal Statistical Society, Series A, 171, 723-738.
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  • Lüdtke, O., Marsh, H.W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13, 203-229.
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  • Masyn, K. E. (2008). Modeling measurement error in event occurrence for single, non-recurring events in discrete-time survival analysis. In Hancock, G. R., & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models, pp. 105-145. Charlotte, NC: Information Age Publishing, Inc. Click here for information about the book.
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  • Muthén, B. (2008). Latent variable hybrids:  Overview of old and new models. In Hancock, G. R., & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models, pp. 1-24. Charlotte, NC: Information Age Publishing, Inc. Click here for information about the book.
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  • Rathouz, P.J., Hulle, C.A., Rodgers, J.L., Waldman, I.D., & Lahey, B.B. (2008). Specification, testing, and interpretation of gene-by-measured-environment interaction models in the presence of gene-environment correlations. Behavior Genetics, 38, 301-315.
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  • Raykov, T. (2008). “Alpha if item deleted”: A note on loss of criterion validity in scale development if maximizing coefficient alpha. British Journal of Mathematical and Statistical Psychology, 61, 275-285.
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  • Stapleton, L.M. (2008). Variance estimation using replication methods in structural equation modeling with complex sample data. Structural Equation Modeling, 15, 183-210.
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  • Temme, D., Paulssen, M., & Dannewald, T. (2008). Incorporating latent variables into discrete choice models – A simultaneous estimation approach using SEM software. BuR – Business Research, 1, 220-237.
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  • Tolvanen, A. (2008). Latent growth mixture modeling: A simulation study. Doctoral dissertation, Department of Mathematics, University of Jyvaskyla, Finland.
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  • Van Horn, M.L., Fagan, A.A., Jaki, T., Brown, E.C., Hawkins, J.D., Arthur, M.W., Abbott, R.D., & Catalano, R. F. (2008). Using multilevel mixtures to evaluate intervention effects in group randomized trials. Multivariate Behavioral Research, 43(2), 289-326. PMC - In Process.
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  • Asparouhov, T. & Muthén, B. (2007). Testing for informative weights and weights trimming in multivariate modeling with survey data. Proceedings of the 2007 JSM meeting in Salt Lake City, Utah, Section on Survey Research Methods.
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  • Asparouhov, T. & Muthén, B. (2007). Computationally efficient estimation of multilevel high-dimensional latent variable models. Proceedings of the 2007 JSM meeting in Salt Lake City, Utah, Section on Statistics in Epidemiology.
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  • Boomsma, D., Cacioppo, J., Muthén, B., Asparouhov, T. & Clark, S. (2007). Longitudinal genetic analysis for loneliness in Dutch twins. Twin Research and Human Genetics, 10, 267-273.
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  • Bray, B.C. (2007). Examining gambling and substance use: Applications of advanced latent class modeling techniques for cross-sectional and longitudinal data. Doctoral dissertation, Pennsylvania State University.
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  • Grilli, L & Rampichini, C. (2007). Multilevel factor models for ordinal variables. Structural Equation Modeling, 14, 1-25.
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  • Lubke, G. & Muthén, B. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling, 14(1), 26–47.
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  • Lubke, G., Muthén, B., Moilanen, I., McGough, J., Loo, S., Swanson, J., Yang, M., Taanila, A., Hurtig, T., Jarvelin, M. & Smalley, S. (2007). Subtypes versus severity differences in the Attention-Deficit/Hyperactivity disorder in the northern Finnish birth cohort. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 1584-1593.
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  • MacKinnon, D.P., Lockwood, C.M., Brown, C.H., Wang, W., & Hoffman, J.M. (2007). The intermediate endpoint effect in logistic and probit regression. Clinical Trials, 4, 499-513.
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  • Morgan-Lopez, A. A. & Fals-Steward, W. (2007). Analytic methods for modeling longitudinal data from rolling therapy groups with membership turnover. Journal of Consulting and Clinical Psychology, 75, 580-593.
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  • Nylund, K. (2007). Latent transition analysis: Modeling extensions and an application to peer victimization. Doctoral dissertation, University of California, Los Angeles.
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  • Nylund, K.L., Asparouhov, T., & Muthén, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Structural Equation Modeling, 14, 535-569.
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  • Pettit, G.S., Keiley, M.K., Laird, R.D., Bates, J.E. & Dodge, K.A. (2007). Predicting the developmental course of mother-reported monitoring across childhood and adolescence from early proactive parenting, child temperament, and parents’ worries. Journal of Family Psychology, 21, 206-217.
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  • Vazsonyi, A.T. & Keiley, M.K. (2007). Normative developmental trajectories of aggressive behaviors in African American, American Indian, Asian American, Caucasian, and Hispanic children and early adolescents. Journal of Abnormal Child Psychology, 35, 1047-1062.
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  • Witkiewitz, K. & Masyn, K. (2007). Drinking trajectories following an initial lapse. In press, Psychology of Addictive Behaviors.
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  • Zumbo, B.D. (2007). Three generations of DIF analyses:Considering where it has been, where it is now, and where it is going. Language Aseessment Quarterly, 4(2), 223–233.
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  • Asparouhov, T. (2006). General multi-level modeling with sampling weights. Communications in Statistics: Theory and Methods, Volume 35, Number 3, 2006, pp. 439-460(22). An earlier version of this paper appeared as Mplus Web Notes: No. 8 with the title Weighting for unequal probability of selection in multilevel modeling. Refer to Mplus Web Notes: No. 8 for more details.
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  • Asparouhov, T. & Muthén, B. (2006). Comparison of estimation methods for complex survey data analysis.
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  • Asparouhov, T. & Muthén, B. (2006). Multilevel modeling of complex survey data. Proceedings of the Joint Statistical Meeting in Seattle, August 2006. ASA section on Survey Research Methods, 2718-2726.
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  • Asparouhov, T., Masyn, K. & Muthén, B. (2006). Continuous time survival in latent variable models. Proceedings of the Joint Statistical Meeting in Seattle, August 2006. ASA section on Biometrics, 180-187. Click here to download the files associated with this paper.
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  • Coenders, G., Batista-Foguet, J.M. & Saris, W. (2006). Simple, efficient and distribution-free approach to interaction effects in complex structural equation models. Quality & Quantity, 42, 369-396.
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  • Geiser, C., Lehman, W., & Eid, M. (2006). Separating rotators from nonrotators in the Mental Rotation Test: A multigroup latent class analysis. Multivariate Behavioral Research, 41, 261-293.
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  • Guo, J., Wall, M. & Amemiya, Y. (2006). Latent class regression on latent factors. Biostatistics, 7, 145-163. This type of modeling can be done using ML techniques illustrated in the Mplus Version 3 User's Guide (first printed in March 2004), example 7.19. The authors emailed us and apologized for not seeing this Mplus capability earlier and not referencing it in the paper.
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  • Harden, K.P., Turkheimer, E. & Loehlin, J.C. (2006). Genotype by environment interaction in adolescents’ cognitive aptitude. Behavioral Genetics.
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  • Lubke, G. & Neale, M. (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood? Multivariate Behavioral Research, 41(4), 499–532.
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  • Muthén, B. (2006). Should substance use disorders be considered as categorical or dimensional? Addiction, 101 (Suppl. 1), 6-16.
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  • Muthén, B. (2006). The potential of growth mixture modeling. Commentary. Infant and Child Development, 15, 623-625.
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  • Muthén, B. & Asparouhov, T. (2006). Item response mixture modeling: Application to tobacco dependence criteria. Addictive Behaviors, 31, 1050-1066.
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  • Muthén, B., Asparouhov, T. & Rebollo, I. (2006). Advances in behavioral genetics modeling using Mplus: Applications of factor mixture modeling to twin data. Twin Research and Human Genetics, 9, 313-324. Mplus inputs, outputs, and data are not yet available for this article.
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  • Nylund, K.L., Muthén, B., Nishina, A., Bellmore, A. & Graham, S. (2006). Stability and instability of peer victimization during middle school: Using latent transition analysis with covariates, distal outcomes, and modeling extensions. Submitted for publication.
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  • Schaeffer, C.M., Petras, H., Ialongo, N., Masyn, K.E., Hubbard, S., Poduska, J., & Sheppard, K. (2006). A comparison of girl's and boy's aggressive-disruptive behavior trajectories across elementary school: Prediction to young adult antisocial outcomes. Journal of Consulting and Clinical Psychology, 74, 500-510.
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  • Asparouhov, T. (2005). Sampling weights in latent variable modeling. Structural Equation Modeling, 12, 411-434.
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  • Asparouhov, T. & Muthén, B. (2005). Multivariate statistical modeling with survey data. Proceedings of the Federal Committee on Statistical Methodology (FCSM) Research Conference.
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  • Bauer, D.J. (2005). The role of nonlinear factor-to-indicator relationships. Psychological Methods, 10, 305-316. This paper draws on techniques illustrated in the Mplus Version 3 User's Guide (first printed in March 2004), example 5.7.
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  • Bauer, D.J. (2005). A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling, 12, 513-534. This paper draws on techniques illustrated in the Mplus Version 3 User's Guide (first printed in March 2004), example 7.26.

  • Brown, E.C., Catalano, C.B., Fleming, C.B., Haggerty, K.P. & Abbot, R.D. (2005). Adolescent substance use outcomes in the Raising Healthy Children Project: A two-part latent growth curve analysis. Journal of Consulting and Clinical Psychology, 73, 699-710. Mplus outputs used in this paper can be viewed and/or downloaded from the Examples page.
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  • Clark, D., Birmaher, B., Axelson, D., Monk, K., Kalas, C., Ehmann, M., Bridge, J., Wood, S., Muthén, B., & Brent, D. (2005). Fluoxetine for the treatment of childhood anxiety disorders: Open-label, long-term extension to a controlled trial. Journal of the American Academy of Child & Adolescent Psychiatry, 44, 1263-1270.
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  • Dyer, N.G., Hanges, P.J. & Hall, R.J. (2005). Applying multilevel confirmatory factor analysis techniques to the study of leadership. The Leadership Quarterly 16 (2005), 149–167.
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  • Greenbaum, P.E., Del Boca, F.K., Darkes, J., Wang, C. & Goldman, M.S. (2005). Variation in the drinking trajectories of freshman college students. Journal of Consulting and Clinical Psychology, 73, 229-238
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  • Lubke, G.H. & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21-39.
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  • Maydeu-Olivares, A. & Bockenholt, U. (2005). Structural equation modeling of paired-comparison and ranking data. Psychological Methods, 10, 285-304.
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  • Mehta, P. & Neale, M. (2005). People are variables too: Multilevel structural equations modeling. Psychological Methods, 10, 259-284. This paper draws on techniques illustrated in the Mplus Version 3 User's Guide (first printed in March 2004), example 9.10.
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  • Muthén, B. & Masyn, K. (2005). Discrete-time survival mixture analysis. Journal of Educational and Behavioral Statistics, 30, 27-58.
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  • Yuan, K.H. & Hayashi, K. (2005). On Muthén's maximum likelihood for two-level covariance structure models. Psychometrika, 70, 147-167.
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  • Asparouhov, T. (2004). Stratification in multivariate modeling. Mplus Web Notes: No. 9. Click here for more details.
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  • Flora, D.B. & Curran P.J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466-491.
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  • Hox, J. & Lensvelt-Mulders, G. (2004). Randomized response analysis in Mplus. Structural Equation Modeling, 11, 615-620.
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  • Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.
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  • Prescott, C.A. (2004). Using the Mplus computer program to estimate models for continuous and categorical data from twins. Behavior Genetics, 34, 17-40.
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  • van Lier, P.A.C., Muthén, B., van der Sar, R.M. & Crijnen, A.A.M. (2004). Preventing disruptive behavior in elementary schoolchildren: Impact of a universal classroom-based intervention. In Journal of Consulting and Clinical Psychology, 72, 467-478.
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  • Croudace, T.J., Jarvelin, M.R., Wadsworth, M.E. & Jones, P.B. (2003). Developmental typology of trajectories to nighttime bladder control: Epidemiologic application of longitudinal latent class analysis. American Journal of Epidemiology, May 1;157(9):834-42. To request a copy of the paper, contact the first author.
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  • Dunn, G., Maracy, M., Dowrick, C., Ayuso-Mateos, J.L., Dalgard, O.S., Page, H., Lehtinen, V., Casey, P., Wilkinson, C., Vasquez-Barquero, J.L., & Wilkinson, G. (2003). Estimating psychological treatment effects from a randomized controlled trial with both non-compliance and loss to follow-up. British Journal of Psychiatry, 183, 323-331.
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  • Jo, B. & Muthén, B. (2003). Longitudinal studies with intervention and noncompliance: Estimation of causal effects in growth mixture modeling. In S.P. Reise & Duan, N. (eds.) Multilevel Modeling. Methodological Advances, Issues, and Applications (pp.112-139). Mahwah, New Jersey: Lawrence Erlbaum.
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  • MacIntosh, R. & Hashim, S. (2003). Converting MIMIC model parameters to IRT parameters in DIF analysis. Applied Psyhological Measurement, 27, 372-379.
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  • Masyn, K. E. (2003). Discrete-time survival mixture analysis for single and recurrent events using latent variables. Doctoral dissertation, University of California, Los Angeles.
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  • Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling. Psychological Methods, 8, 369-377.
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  • Muthén, B., Jo, B. & Brown, H. (2003). Comment on the Barnard, Frangakis, Hill & Rubin article, Principal stratification approach to broken randomized experiments: A case study of school choice vouchers in New York City. Journal of the American Statistical Association, 98, 311-314. The Muthén et al. article can be downloaded from here. The Barnard et al. article can be found at http://biosun01.biostat.jhsph.edu/~cfrangak/papers/index.html. For background information and analyses using Mplus, see Mplus Web Note #5 and Jo (2002), Sensitivity of causal effects under ignorable and latent ignorable missing-data mechanisms, Draft. Contact the author. The Jo paper can be downloaded from here.
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  • Muthén, B., Khoo, S.T., Francis, D. & Kim Boscardin, C. (2003). Analysis of reading skills development from Kindergarten through first grade: An application of growth mixture modeling to sequential processes. Multilevel Modeling: Methodological Advances, Issues, and Applications. S.R. Reise & N. Duan (Eds). Mahaw, NJ: Lawrence Erlbaum Associates, pp.71-89.
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  • Oxford, M.L., Gilchrist, L.D., Morrison, D.M., Gillmore, M.R., Lohr M.J. & Lewis, S.M. (2003). Alcohol use among adolescent mothers: Heterogeneity in growth curves. Prevention Science, 4, 15-26. To request a copy of the paper, contact the first author.
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  • Schaeffer, C.M., Petras, H., Ialongo, N., Poduska, J. & Kellam, S. (2003). Modeling growth in boys aggressive behavior across elementary school: Links to later criminal involvement, conduct disorder, and antisocial personality disorder. Developmental Psychology, 39, 1020-1035.
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  • Dagne, G.A., Howe, G.W., Brown, C.H., & Muthén, B. (2002). Hierarchical modeling of sequential behavioral data: An empirical Bayesian approach. Psychological Methods, 7, 262-280. Mplus inputs and outputs used in this paper can be viewed and/or downloaded from the Examples page.
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  • Jo, B. (2002). Statistical power in randomized intervention studies with noncompliance. Psychological Methods, 7, 178-193. Mplus inputs and outputs used in this paper can be viewed and/or downloaded from the Examples page.
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  • Jo, B. (2002). Model misspecification sensitivity analysis in estimating causal effects of interventions with noncompliance. Statistics in Medicine, 21, 3161-3181.
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  • Jo, B. (2002). Estimation of intervention effects with noncompliance: Alternative model specifications. Journal of Educational and Behavioral Statistics, 27, 385-409.
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  • Muthén, B. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29, 81-117.
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  • Muthén, B., Brown, C.H., Masyn, K., Jo, B., Khoo, S.T., Yang, C.C., Wang, C.P., Kellam, S., Carlin, J., & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475. Mplus inputs and outputs used in this paper can be viewed and/or downloaded from the Examples page.
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  • Muthén, L.K. & Muthén, B.O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 4, 599-620. Mplus inputs and outputs used in this paper can be viewed and/or downloaded from the Examples page.
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  • Yu, C.Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Doctoral dissertation, University of California, Los Angeles.
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  • Jo, B. & Muthén, B. (2001). Modeling of intervention effects with noncompliance: A latent variable approach for randomized trials. In G. Marcoulides & R.E. Schumacker (eds.) New Developments and Techniques in Structural Equation Modeling (pp. 57-87). Mahwah, New Jersey: Lawrence Erlbaum
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  • Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In Collins, L.M. & Sayer, A. (eds.), New Methods for the Analysis of Change (pp. 291-322). Washington, D.C.: APA.
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  • Muthén, B. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (eds.), New Developments and Techniques in Structural Equation Modeling (pp. 1-33). Lawrence Erlbaum Associates. Mplus inputs and outputs used in this paper can be viewed and/or downloaded from the Examples page.
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  • Stoolmiller, M. (2001). Synergistic interaction of child manageability problems and parent-discipline tactics in predicting future growth in externalizing behavior for boys. Developmental Psychology, 37, 814-825.
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  • Muthén, B. & Muthén, L. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891. Mplus inputs and outputs used in this paper can be viewed and/or downloaded from the Examples page.
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  • Muthén, B. & Muthén, L. (2000). The development of heavy drinking and alcohol-related problems from ages 18 to 37 in a U.S. national sample. Journal of Studies on Alcohol, 61, 290-300.
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  • Muthén, B. & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
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  • Muthén, B. & Curran, P. (1997). General longitudinal modeling of individual differences in experimental designs: a latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402.
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  • Muthén, B., du Toit, S.H.C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Unpublished technical report.
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  • Muthén, B., Khoo, S.T. & Gustafsson, J.E. (1997). Multilevel latent variable modeling in multiple populations. Unpublished technical report.
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  • Muthén, B. & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267-316.
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  • Muthén, B. & Satorra, A. (1995). Technical aspects of Muthén's LISCOMP approach to estimation of latent variable relations with a comprehensive measurement model. Psychometrika, 60, 489-503.
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  • Muthén, B. (1994). Multilevel covariance structure analysis. In J. Hox & I. Kreft (eds.), Multilevel Modeling, a special issue of Sociological Methods & Research, 22, 376-398.
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  • Muthén, B. (1989). Tobit factor analysis. British Journal of Mathematical and Statistical Psychology, 42, 241-250.
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  • Muthén, B. (1989). Latent variable modeling in heterogeneous populations. Psychometrika, 54:4, 557-585.
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  • Muthén, B. (1989). The future of methodological training in educational psychology: The problem of teaching students to use new sophisticated statistical techniques. In M. C. Wittrock, & F. Farley (Eds.), The Future of Educational Psychology ( pp. 181-189). Hillsdale, NJ: Erlbaum Associates.
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  • Muthén, B., Kaplan, D. & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52:3, 431-462.
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  • Muthén, B., Kaplan, (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.
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  • Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115-132.
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  • Muthén, B. (1983). Latent variable structural equation modeling with categorical data. Journal of Econometrics, 22, 43-65.
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  • Muthén, B. (1979). A structural Probit model with latent variables. Journal of the American Statistical Association, 74, 807-811.
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  • Muthén, B. (1978). Contributions to factor analysis of dichotomous variables. Psychometrika, 43, 551-560.
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