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Applications Using Mplus

Type of Analysis Input file Data file View output
Muthén, B., Asparouhov, T., Hunter, A. & Leuchter, A. (2010). Growth modeling with non-ignorable dropout: Alternative analyses of the STAR*D antidepressant trial. Submitted for publication. 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

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

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

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

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

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. (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. & 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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Type of Analysis Input file Data file View output
Muthén, B. (1998). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. Forthcoming in Collins, L.M. & Sayer, A. (Eds.), New Methods for the Analysis of Change. Washington, D.C.: APA. (#82) Download the corresponding set of examples. 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
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
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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