MPLUS Analysis Examples Replication Chapter 13
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1 MPLUS Analysis Examples Replication Chapter 13 Mplus includes all input code and output in the *.out file. This document contains all code and selected output from the weighted multilevel models presented in Chapter 13. Because the SEM model output is discussed in detail in the book, we omit that example here. See Chapter 13 and the Mplus documentation for details on multilevel models and SEM methods. All data preparation and management was done using SAS and then read into Mplus v7.4 using a text file format produced by SAS. 1
2 Mplus VERSION /15/2017 5:13 PM INPUT INSTRUCTIONS TITLE: ASDA 2 EXAMPLE MULTILEVEL MODEL EXAMPLE NO WGTS PISA 2000 DATA Data: FILE IS "P:\ASDA 2\Data sets\additional Example Data Sets\pisa_mplus.txt"; Variable: NAMES ARE both_for college female high_school id_school isei one_for pass_read test_lang w_fstuwt wnrschbw; missing are. ; usevar = isei id_school college ; within = college ; cluster = id_school ; Analysis: type is twolevel random ; estimator=mlr ; Model: %within% randoms1 isei on college ; %between% isei ; INPUT READING TERMINATED NORMALLY ASDA 2 EXAMPLE MULTILEVEL MODEL EXAMPLE NO WGTS PISA 2000 DATA SUMMARY OF ANALYSIS Number of groups 1 Number of observations 2069 Number of dependent variables 1 Number of independent variables 1 Number of continuous latent variables 1 Observed dependent variables Continuous ISEI Observed independent variables Continuous latent variables RANDOMS1 Variables with special functions Cluster variable ID_SCHOO Within variables Estimator MLR Information matrix OBSERVED Maximum number of iterations 100 Convergence criterion Maximum number of EM iterations 500 Convergence criteria for the EM algorithm Loglikelihood change 0.100D-02 Relative loglikelihood change Derivative Minimum variance Maximum number of steepest descent iterations 20 Maximum number of iterations for H Convergence criterion for H1 Optimization algorithm EMA Input data file(s) P:\ASDA 2\Data sets\additional Example Data Sets\pisa_mplus.txt Input data format FREE SUMMARY OF DATA Number of missing data patterns 1 Number of clusters 148 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value
3 PROPORTION OF DATA PRESENT Covariance Coverage ISEI ISEI THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 5 Loglikelihood H0 Value H0 Scaling Correction Factor for MLR Information Criteria Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC (n* = (n + 2) / 24) MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Within Level Residual ISEI Between Level ISEI RANDOMS ISEI RANDOMS QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix (ratio of smallest to largest eigenvalue) 0.112E Stoner Ave. Los Angeles, CA Tel: (310) Fax: (310) Web: Support: Support@StatModel.com Copyright (c) Muthen & Muthen 3
4 Mplus VERSION /16/2017 7:56 AM INPUT INSTRUCTIONS TITLE: ASDA 2 EXAMPLE MULTILEVEL MODEL WITH WGT AS COVARIATE PISA 2000 DATA Data: FILE IS "P:\ASDA 2\Data sets\additional Example Data Sets\pisa_mplus.txt"; Variable: NAMES ARE both_for college female high_school id_school isei one_for pass_read test_lang w_fstuwt wnrschbw; missing are. ; usevar = isei id_school college w_fstuwt ; within = college w_fstuwt ; cluster = id_school ; Analysis: type is twolevel random ;! DEFINE A TWOLEVEL MODEL WITH RANDOM EFFECTS estimator=mlr ;! ESTIMATOR IS MLR ; Model: %within% randoms1 isei on college ; isei on w_fstuwt ; %between% isei ; INPUT READING TERMINATED NORMALLY ASDA 2 EXAMPLE MULTILEVEL MODEL WITH WGT AS COVARIATE PISA 2000 DATA SUMMARY OF ANALYSIS Number of groups 1 Number of observations 2069 Number of dependent variables 1 Number of independent variables 2 Number of continuous latent variables 1 Observed dependent variables Continuous ISEI Observed independent variables W_FSTUWT Continuous latent variables RANDOMS1 Variables with special functions Cluster variable ID_SCHOO Within variables W_FSTUWT Estimator MLR Information matrix OBSERVED Maximum number of iterations 100 Convergence criterion Maximum number of EM iterations 500 Convergence criteria for the EM algorithm Loglikelihood change 0.100D-02 Relative loglikelihood change Derivative Minimum variance Maximum number of steepest descent iterations 20 Maximum number of iterations for H Convergence criterion for H1 Optimization algorithm EMA Input data file(s) P:\ASDA 2\Data sets\additional Example Data Sets\pisa_mplus.txt 4
5 Input data format FREE SUMMARY OF DATA Number of missing data patterns 1 Number of clusters 148 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value PROPORTION OF DATA PRESENT Covariance Coverage ISEI W_FSTUWT ISEI W_FSTUWT THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 6 Loglikelihood H0 Value H0 Scaling Correction Factor for MLR Information Criteria Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC (n* = (n + 2) / 24) MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Within Level ISEI ON W_FSTUWT Residual ISEI Between Level ISEI RANDOMS ISEI RANDOMS QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix (ratio of smallest to largest eigenvalue) 3463 Stoner Ave. Los Angeles, CA Tel: (310) Fax: (310) Web: Support: Support@StatModel.com Copyright (c) Muthen & Muthen 0.142E-03 5
6 Mplus VERSION /16/2017 8:08 AM INPUT INSTRUCTIONS TITLE: ASDA 2 EXAMPLE MULTILEVEL MODEL WITH WGT/EFFECTIVE SCALING PISA 2000 DATA Data: FILE IS "P:\ASDA 2\Data sets\additional Example Data Sets\pisa_mplus.txt"; Variable: NAMES ARE both_for college female high_school id_school isei one_for pass_read test_lang w_fstuwt wnrschbw; missing are. ; weight = conwt ; wtscale=ecluster ; bweight = wnrschbw ; usevar = isei id_school college wnrschbw w_fstuwt conwt ; within = college w_fstuwt ; cluster = id_school ; Define: conwt= w_fstuwt / wnrschbw ; Analysis: type is twolevel random ;!TWOLEVEL MODEL WITH RANDOM EFFECTS ; Model: %within% randoms1 isei on college ; %between% isei ; *** WARNING in MODEL command Variable is uncorrelated with all other variables: W_FSTUWT *** WARNING in MODEL command At least one variable is uncorrelated with all other variables in the model. Check that this is what is intended. 2 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS ASDA 2 EXAMPLE MULTILEVEL MODEL WITH WGT/EFFECTIVE SCALING PISA 2000 DATA SUMMARY OF ANALYSIS Number of groups 1 Number of observations 2069 Number of dependent variables 2 Number of independent variables 1 Number of continuous latent variables 1 Observed dependent variables Continuous ISEI W_FSTUWT 6
7 Observed independent variables Continuous latent variables RANDOMS1 Variables with special functions Cluster variable ID_SCHOO Weight variable (effective cluster-size scaling) CONWT Between weight variable (sample-size scaling) WNRSCHBW Within variables W_FSTUWT Estimator MLR Information matrix OBSERVED Maximum number of iterations 100 Convergence criterion Maximum number of EM iterations 500 Convergence criteria for the EM algorithm Loglikelihood change 0.100D-02 Relative loglikelihood change Derivative Minimum variance Maximum number of steepest descent iterations 20 Maximum number of iterations for H Convergence criterion for H1 Optimization algorithm EMA Input data file(s) P:\ASDA 2\Data sets\additional Example Data Sets\pisa_mplus.txt Input data format FREE SUMMARY OF DATA Number of missing data patterns 1 Number of clusters 148 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value PROPORTION OF DATA PRESENT Covariance Coverage ISEI W_FSTUWT ISEI W_FSTUWT
8 THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 7 Loglikelihood H0 Value H0 Scaling Correction Factor for MLR Information Criteria Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC (n* = (n + 2) / 24) MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Within Level W_FSTUWT W_FSTUWT ********* ********* Residual ISEI Between Level ISEI RANDOMS ISEI RANDOMS QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix (ratio of smallest to largest eigenvalue) 0.234E Stoner Ave. Los Angeles, CA Tel: (310) Fax: (310) Web: Support: Support@StatModel.com Copyright (c) Muthen & Muthen 8
9 Mplus VERSION /16/2017 8:11 AM INPUT INSTRUCTIONS TITLE: ASDA 2 EXAMPLE MULTILEVEL MODEL WITH WGT/SIZE SCALING PISA 2000 DATA Data: FILE IS "P:\ASDA 2\Data sets\additional Example Data Sets\pisa_mplus.txt"; Variable: NAMES ARE both_for college female high_school id_school isei one_for pass_read test_lang w_fstuwt wnrschbw; missing are. ; weight = conwt ;!wtscale=sample ; bweight = wnrschbw ; bwtscale = sample ; usevar = isei id_school college wnrschbw w_fstuwt conwt ;!between= college ; within = college w_fstuwt ; cluster = id_school ; Define: conwt= w_fstuwt / wnrschbw ; Analysis: type is twolevel random ;!TWOLEVEL MODEL WITH RANDOM EFFECTS ; Model: %within% randoms1 isei on college ; %between% isei ; *** WARNING in MODEL command Variable is uncorrelated with all other variables: W_FSTUWT *** WARNING in MODEL command At least one variable is uncorrelated with all other variables in the model. Check that this is what is intended. 2 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS ASDA 2 EXAMPLE MULTILEVEL MODEL WITH WGT/SIZE SCALING PISA 2000 DATA SUMMARY OF ANALYSIS Number of groups 1 Number of observations 2069 Number of dependent variables 2 Number of independent variables 1 Number of continuous latent variables 1 Observed dependent variables Continuous ISEI W_FSTUWT Observed independent variables Continuous latent variables RANDOMS1 Variables with special functions 9
10 Cluster variable ID_SCHOO Weight variable (cluster-size scaling) CONWT Between weight variable (sample-size scaling) WNRSCHBW Within variables W_FSTUWT Estimator MLR Information matrix OBSERVED Maximum number of iterations 100 Convergence criterion Maximum number of EM iterations 500 Convergence criteria for the EM algorithm Loglikelihood change 0.100D-02 Relative loglikelihood change Derivative Minimum variance Maximum number of steepest descent iterations 20 Maximum number of iterations for H Convergence criterion for H1 Optimization algorithm EMA Input data file(s) P:\ASDA 2\Data sets\additional Example Data Sets\pisa_mplus.txt Input data format FREE SUMMARY OF DATA Number of missing data patterns 1 Number of clusters 148 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value PROPORTION OF DATA PRESENT Covariance Coverage ISEI W_FSTUWT ISEI W_FSTUWT
11 THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 7 Loglikelihood H0 Value H0 Scaling Correction Factor for MLR Information Criteria Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC (n* = (n + 2) / 24) MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Within Level W_FSTUWT W_FSTUWT ********* ********* Residual ISEI Between Level ISEI RANDOMS ISEI RANDOMS QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix (ratio of smallest to largest eigenvalue) 0.235E Stoner Ave. Los Angeles, CA Tel: (310) Fax: (310) Web: Support: Support@StatModel.com Copyright (c) Muthen & Muthen 11
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