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April 20, 2014
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Applications Using Mplus

  • Muthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus. Submitted for publication.
  • 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.
  • Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A more flexible representation of substantive theory. Psychological Methods, 17, 313-335.
  • Muthén, B. (2010). Bayesian analysis in Mplus: A brief introduction. Technical Report. Version 3.
  • Muthén, B., Asparouhov, T., Boye, M.E., Hackshaw, M.D., & Naegeli, A.N. (2009). Applications of continuous-time survival in latent variable models for the analysis of oncology randomized clinical trial data using Mplus. Technical Report.
  • Brown, E.C., Catalano, C.B., Fleming, C.B., Haggerty, K.P. & Abbot, R.D. (2004). Adolescent substance use outcomes in the Raising Healthy Children Project: A two-part latent growth curve analysis. Paper under review.
  • 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.
  • Prescott, C.A. (2004). Using the Mplus computer program to estimate models for continuous and categorical data from twins. Behavior Genetics, 34, 17- 40.
  • Muthén, L.K. and 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. (#97).
  • 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.
  • Jo, B. (2002). Statistical power in randomized intervention studies with noncompliance. Psychological Methods, 7, 178-193.
  • 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. (#82)
  • Muthén, B. & Masyn, K. (2001). Discrete-time survival mixture analysis. (#92)
  • 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. (#86)
  • Muthén, B., Brown, C.H., Masyn, K., Jo, B., Khoo, S.T., Yang, C.C., Wang, C.P., Kellam, S., Carlin, J., & Liao, J. (2000). General growth mixture modeling for randomized preventive interventions. Accepted for publication in Biostatistics. (#87)
  • 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. (#85)
  • Type of Analysis Input file Data file View output
    Muthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus. Submitted for publication. Download this paper. Click here to view the Technical appendix that goes with this paper and click here for the Mplus input appendix.
    1. Table 25 run1.inp N/A run1.out
    2. Table 1, Table 26 run2.inp N/A run2.out
    3. Table 27 run3.inp N/A run3.out
    4. Table 2, Table 28* run4.inp N/A run4.out
    5. Table 3, Table 29* run5.inp N/A run5.out
    6. Table 30* run6.inp 1stn200.dat run6.out
    7. Table 4, Table 31-32* run7.inp N/A run7.out
    8. Table 5, Table 33 run8.inp N/A run8.out
    9. Table 7, Table 34* run9.inp 4cat m.dat run9.out
    10. Table 8, Table 35* run10.inp 4cat m.dat run10.out
    11. Table 36-37 run11.inp N/A run11.out
    12. Table 10, Table 38 run12.inp N/A run12.out
    13. Table 39-40* run13.inp N/A run13.out
    14. Table 11, Table 41* run14.inp N/A run14.out
    15. Table 12, Table 42-43* run15.inp N/A run15.out
    16. Table 14, Table 44-45* run16.inp n200.dat run16.out
    17. Table 15, Table 46-47 run17.inp N/A run17.out
    18. Table 16-17, Table 48-49 run18.inp N/A run18.out
    19. Table 19-20, Table 50-51 run19.inp nombin.dat run19.out
    20. Table 52-53 run20.inp N/A run20.out
    21. Table 21, Table 54 run21.inp N/A run21.out
    22. Table 22, Table 55 run22.inp N/A run22.out
    23. Table 23, Table 56 run23.inp N/A run23.out
    24. Table 24, Table 57 run24.inp N/A run24.out
    *This analysis requires Mplus version 6.12
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    Type of Analysis Input file Data file View output
    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. Download this paper. Click here for an explanation of the runs.
    1. Section 4 MAR 4c run1.inp N/A run1.out
    2. Section 5.1.1 Create yu data run2.inp N/A run2.out
    3. Section 5.1.1 Pattern-mixture run3.inp N/A run3.out
    4. Section 5.1.1 Pattern-mixture using V6 run4.inp N/A run4.out
    5. Section 5.1.2 Roy dropout 4c run5.inp N/A run5.out
    6. Section 5.1.2 Roy dropout 4c V6 run6.inp N/A run6.out
    7. Section 5.2.1 Diggle-Kenward run7.inp N/A run7.out
    8. Section 5.2.1 Diggle-Kenward V6 run8.inp N/A run8.out
    9. Section 5.2.2 Beunckens run9.inp N/A run9.out
    10. Section 6.1 Muthen-Roy run10.inp N/A run10.out
    11. Section 6.2 Diggle-Kenward 4c V6 run11.inp N/A run11.out
    12. Section 7.2 Distal MAR 4c run12.inp N/A run12.out
    13. Section 7.2 Distal Diggle-Kenward 4c V6 run13.inp N/A run13.out
    14. Section 7.2 Distal Muthen-Roy, Step 1 V6 run14.inp N/A run14.out
    15. Section 7.2 Distal Muthen-Roy, Step 2 V6 run15.inp N/A run15.out
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    Type of Analysis Input file Data file View output
    Muthén, B. & Asparouhov, T. (2011). Bayesian SEM: A more flexible representation of substantive theory. Psychological Methods, 17, 313-335. Download the latest version dated October 21, 2011. Download the 2nd version dated April 14, 2011. Click here to view the seven web tables referred to in the 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.
    Table 3, Grant-White ML CFA run1.inp H-S Combined.txt run1.out
    Table 3, Grant-White ML EFA run2.inp H-S Combined.txt run2.out
    Table 3, Pasteur ML CFA run3.inp H-S Combined.txt run3.out
    Table 3, Pasteur ML EFA run4.inp H-S Combined.txt run4.out
    Table 4, Grant-White ML EFA run5.inp H-S Combined.txt run5.out
    Table 4, Pasteur ML EFA run6.inp H-S Combined.txt run6.out
    Table 5, Grant-White Bayes run7.inp H-S Combined.txt run7.out
    Table 5-6, Grant-White Bayes x-load run8.inp H-S Combined.txt run8.out
    Table 5, Pasteur Bayes run9.inp H-S Combined.txt run9.out
    Table 5-6, Pasteur Bayes x-load run10.inp H-S Combined.txt run10.out
    Table 9, ML 0.1 run12.inp N/A run12.out
    Table 10 run13.inp N/A run13.out
    Table 13-14, females, ML EFA run14.inp BHPS OINDRESPX1.DAT run14.out
    Table 15, females, BSEM run15.inp BHPS OINDRESPX1.DAT run15.out

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    Type of Analysis Input file Data file View output
    Muthén, B. (2010). Bayesian analysis in Mplus: A brief introduction. Technical Report. Version 3. Download this paper.
    Table 2 run1.inp mbr2004ATLAS run1.out
    Table 4 run2.inp mbr2004ATLAS run2.out
    Table 6 run3.inp fire.dat run3.out
    Table 8 run4.inp fire.dat run4.out
    CFA: Tables 10-12 run5.inp holzingr.dat run5.out
    CFA + priors: Tables 13-16 run6.inp holzingr.dat run6.out
    CFA + 3 free: Tables 17-20 run7.inp holzingr.dat run7.out
    CFA + 3 free + priors: Table 21 run8.inp holzingr.dat run8.out
    Section 4, ML run9.inp N/A run9.out
    Section 4, IG prior run10.inp N/A run10.out
    Section 4, U prior run11.inp N/A run11.out
    Section 4, Def prior run12.inp N/A run12.out
    Section 5 run13.inp big.dat run13.out
    Section 6 run14.inp N/A run14.out
    Section 6, Figure 15 run15.inp N/A run15.out
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    Type of Analysis Input file Data file View output
    Muthén, B., Asparouhov, T., Boye, M.E., Hackshaw, M.D., & Naegeli, A.N. (2009). Applications of continuous-time survival in latent variable models for the analysis of oncology randomized clinical trial data using Mplus. Technical Report. Download this paper.
    Section 3.1, Cox regression run1.inp N/A run1.out
    Table 6, proportional model run2.inp N/A run2.out
    Table 6, unrestricted model run3.inp N/A run3.out
    Table 6, linear model run4.inp N/A run4.out
    Table 7, non-prop on qol run5.inp N/A run5.out
    Table 10, Xu-Zeger model run6.inp N/A run6.out
    Table 10, observed model run7.inp N/A run7.out
    Table 10, random effects model run8.inp N/A run8.out
    Table 10, growth mixture model run9.inp N/A run9.out
    Table 11, i and s 3 covs qol run10.inp N/A run10.out
    Table 14, efa 5+3 mlr run11.inp N/A run11.out
    Table 14, cfa 5+3 1g 1s run12.inp N/A run12.out
    Table 14, lca 5+3 2c run13.inp N/A run13.out
    Table 14, fma 5+3 fma 2c 1f run14.inp N/A run14.out
    Table 14, mimic with xs run15.inp N/A run15.out
    Figure 16 model run16.inp N/A run16.out
    Figure 22 model run17.inp N/A run17.out
    Figure 23 model run18.inp N/A run18.out
    Table 20, M1 run19.inp N/A run19.out
    Table 20, M2 run20.inp N/A run20.out
    Table 20, M3 run21.inp N/A run21.out
    Table 20, M4 run22.inp N/A run22.out
    Table 20, M5 run23.inp N/A run23.out
    Table 20, M6 run24.inp N/A run24.out
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    Type of Analysis Input file Data file View output
    Brown, E.C., Catalano, C.B., Fleming, C.B., Haggerty, K.P. & Abbot, R.D. (2004). Adolescent substance use outcomes in the Raising Healthy Children Project: A two-part latent growth curve analysis. Paper under review. Download the corresponding set of examples. The data files for these examples are not available for download.
    Two-part growth for alcohol N/A N/A twopartlgm_alc.out
    Two-part growth for cigarettes N/A N/A twopartlgm_cig.out
    Two-part growth for pot N/A N/A twopartlgm_pot.out
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    Type of Analysis Input file Data file View output
    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. Download the corresponding set of examples.
    Example 6: LCGA, pp. 361-362 example6.inp N/A example6.std
    Example 7: Growth Analysis example7.inp N/A example7.std
    Example 8: GMM, pp. 362-363 example8.inp N/A example8.std
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    Type of Analysis Input file Data file View output
    Prescott, C.A. (2004). Using the Mplus computer program to estimate models for continuous and categorical data from twins. Behavior Genetics, 34, 17- 40. Download this paper, the technical appendix for the paper, or the corresponding set of examples. The data file onset99.dat is not available for download.
    Example 1: 2-group univariate twin model for a continuous variable example1.inp example1.dat example1.out
    Example 2 step 1: Estimating thresholds for a 2-group univariate twin model for a binary variable example2_step1.inp example1.dat example2_step1.out
    Example 2: 2-group univariate twin model for a binary variable (DELTA parameterization) example2_delta.inp example1.dat example2_delta.out
    Example 2: 2-group univariate twin model for a binary variable with THETA parameterization example2_theta.inp example1.dat example2_theta.out
    Example 3: 5-group model for transformed age at drinking onset, MF proportional example3.inp onset99.dat example3.out
    Example 4: 5-group model for 3-categ. diagnosis with age regressions & free R example4.inp onset99.dat example4.out
    Example 5: 2-group bivariate model for age at drinking onset and diagnosis example5.inp onset99.dat example5.out
    Example 6: 2-group bivariate mediation model for diagnosis and drinking onset with fixed unreliability for onset among female drinking pairs example6.inp onset99.dat example6.out
    Example 7 step 1: Estimating thresholds for input into THETA parameterization model example7_step1.inp onset99.dat example7_step1.out
    Example 7 step 2: 2-group bivariate model for diagnosis and categorized onset age using threshold values estimated in prior run example7_step2.inp onset99.dat example7_step2.out
    5-group model for age at drinking onset A & C loadings parameterized as square roots to keep non-negative MF_sqroot.inp onset99.dat MF_sqroot.out
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    Type of Analysis Input file Data file View output
    Muthén, L.K. and 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. (#97) Download this paper or the corresponding set of examples.
    CFA model with normally distributed continuous factor indicators without missing data. cfa1.inp N/A cfa1.std
    CFA model with normally distributed continuous factor indicators with missing data. cfa2.inp N/A cfa2.std
    CFA model with non-normal continuous factor indicators without missing data. cfa3.inp N/A cfa3.std
    CFA model with non-normal continuous factor indicators with missing data. cfa4.inp N/A cfa4.std
    Growth model with normally distributed continuous outcomes without missing data without a covariate. growth1.inp N/A growth1.std
    Growth model with normally distributed continuous outcomes without missing data with a covariate that has a regression coefficient of 0.2 for the slope growth factor. growth2.inp N/A growth2.std
    Growth model with normally distributed continuous outcomes with missing data with a covariate that has a regression coefficient of 0.2 for the slope growth factor. growth3.inp N/A growth3.std
    Growth model with normally distributed continuous outcomes without missing data with a covariate that has a regression coefficient of 0.1 for the slope growth factor. growth4.inp N/A growth4.std
    Growth model with normally distributed continuous outcomes with missing data with a covariate that has a regression coefficient of 0.1 for the slope growth factor. growth5.inp N/A growth5.std
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    Type of Analysis Input file Data file View output
    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. Download this paper or the corresponding set of examples. Refer to Web Note 3 for technical background for the Mplus inputs.
    Model 1. Full model. 5 parameters. model1.inp stationary_oddratio.dat model1.std
    Model 2. Equal intercepts and zero residual variances. 2 parameters. model2.inp stationary_oddratio.dat model2.std
    Model 3. Equal intercepts. 4 parameters. model3.inp stationary_oddratio.dat model3.std
    Model 4. Zero residual variances. 3 parameters. model4.inp stationary_oddratio.dat model4.std
    Model 5. Zero residual variances, zero factor variance. 2 parameters. model5.inp stationary_oddratio.dat model5.std
    Model 6. Zero factor variance. 4 parameters. model6.inp stationary_oddratio.dat model6.std
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    Type of Analysis Input file Data file View output
    Jo, B (2002). Statistical power in randomized intervention studies with noncompliance. Psychological Methods, 7, 178-193. Download this paper or the corresponding set of examples. View a description of the Mplus inputs used in this paper.
    Internal Mplus Monte Carlo simulation of CACE power. cacepow.inp N/A cacepow.std
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    Type of Analysis Input file Data file View output
    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. (#82) Download this paper. The data file lsay.dat is not available for download.
    EXAMPLE 1: LSAY example, page 22 unconditional analysis, 1-class model penn1.inp lsay.dat penn1.std
    EXAMPLE 1: LSAY example, page 22 unconditional analysis, 2-class model, class-invariant factor covariance matrix (Psi) and residual covariance matrix (Theta) penn2.inp lsay.dat penn2.std
    EXAMPLE 1: LSAY example, same model as penn2, but illustration of E step iterations getting stuck with poor starting values penn3.inp lsay.dat penn3.std
    EXAMPLE 1: LSAY example, page 23 unconditional analysis, 2-class model, class-invariant factor covariance matrix, class-varying residual covariance matrix penn4.inp lsay.dat penn4.std
    EXAMPLE 1: LSAY example, page 23 unconditional analysis, 2-class model, class-varying factor covariance matrix, class-varying residual covariance matrix penn5.inp lsay.dat penn5.std
    EXAMPLE 1: LSAY example, page 24 conditional analysis, 2-class model, class-varying slopes for mothed and homeres, class-varying factor covariance matrix, class-varying residual covariance matrix penn6.inp lsay.dat penn6.std
    EXAMPLE 1: LSAY example, page 25 conditional analysis, 2-class model, class-varying slopes for mothed and homeres, class-varying factor covariance matrix, class-varying residual covariance matrix, c on mothed and homeres penn7.inp lsay.dat penn7.std
    EXAMPLE 2 and 5: NLSY Heavy Drinking example, pages 25-27 and 30-32. penn8.inp nlsy64.dat penn8.std
    EXAMPLE 3: Analysis of reading skills development: confirmatory analysis of growth curve shapes. Not ready yet.
    EXAMPLE 4: Piecewise growth modeling with individually-varying transition points, simulated data, second replication. penn9.inp wise102.dat penn9.std
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    Type of Analysis Input file Data file View output
    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. (#86) Download the corresponding set of examples. The data files newran.dat and toca.dat are not available for download.
    Section 4: Confirmatory latent class analysis of NLSY ASB items with several latent class variables app5.inp asb.dat app5.std
    Latent class growth analysis of college drinking data with 3 trend classes for AUD combined with 3 classes for TD. app6.inp raw5ww12.dat app6.std
    Growth mixture modeling of reading data. app7.inp newran.dat app7.std
    Growth mixture modeling for reading with a covariate predicting class membership. app8.inp newran.dat app8.std
    Growth mixture modeling of aggression data. app9.inp toca.dat app9.std
    Growth mixture modeling for aggression with a distal outcome predicted by class membership. app10.inp toca.dat app10.std
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    Type of Analysis Input file Data file View output
    Muthén, B. & Masyn, K. (2001). Discrete-time survival mixture analysis. (#92) Download the corresponding set of examples. The data file ggmmfull.dat is not available for download.
    Recidivism analysis: Single-class model using non-proportionality app12.inp recid.dat app12.std
    Recidivism analysis: Single-class model using proportionality app13.inp recid.dat app13.std
    Recidivism analysis: Single-class model using all covariates and proportionality app14.inp recid.dat app14.std
    Recidivism analysis: Single-class model using all covariates, proportionality, and constant hazard app15.inp recid.dat app15.std
    Recidivism analysis: Two-class model using long-term survivors app16.inp recid.dat app16.std
    School removal analysis: 3-class growth mixture model combined with survival app17.inp ggmmfull.dat app17.std
    School removal analysis: 3-class growth mixture model combined with 2-class survival (5 classes) app18.inp ggmmfull.dat app18.std
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    Type of Analysis Input file Data file View output
    Muthén, B., Brown, C.H., Masyn, K., Jo, B., Khoo, S.T., Yang, C.C., Wang, C.P., Kellam, S., Carlin, J., & Liao, J. (2000). General growth mixture modeling for randomized preventive interventions. Accepted for publication in Biostatistics. (#87) Download the corresponding set of examples. The data file toca.dat is not available for download.
    Growth mixture modeling for aggression allowing treatment effects to vary across latent trajectory classes. app11.inp toca.dat app11.std
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    Type of Analysis Input file Data file View output
    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. (#85) Download the corresponding set of examples.
    EXAMPLE 1: Latent class analysis of the 17 NLSY ASB items, no covariates (4 classes) app1.inp asb.dat app1.std
    EXAMPLE 1: Latent class analysis of 9 selected NLSY ASB items, 3 covariates (4 classes) app2.inp asb.dat app2.std
    EXAMPLE 2 and 3: Growth mixture modeling of NLSY cohort 64 with covariates centered at 25: four-class model of heavy drinking with classes predicting alcohol dependence app3.inp big.dat app3.std
    EXAMPLE 4: GGMM of NLSY relating 4 heavy drinking classes to 4 antisocial classes (16-class run) app4.inp asbhd.dat app4.std
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