Mplus VERSION 5 MUTHEN & MUTHEN 09/18/2008 8:04 PM INPUT INSTRUCTIONS title: Negative emotions -> Heuristic processing data: file is hp.dat; variable: names are sid pe1 pe2 pe3 ne1 ne2 ne3 hp1 hp2 hp3; usevariables ne1-ne3 hp1-hp3; classes = class(2); analysis: type = mixture; estimator = mlr; starts = 500 10; model: %overall% NE by ne1@1 ne2* ne3*; [ne1@0]; HP by hp1@1 hp2* hp3*; [hp1@0]; %class#1% [NE*]; [HP*]; NE* HP*; HP on NE; %class#2% [NE*]; [HP*]; NE* HP*; HP on NE; INPUT READING TERMINATED NORMALLY Negative emotions -> Heuristic processing SUMMARY OF ANALYSIS Number of groups 1 Number of observations 507 Number of dependent variables 6 Number of independent variables 0 Number of continuous latent variables 2 Number of categorical latent variables 1 Observed dependent variables Continuous NE1 NE2 NE3 HP1 HP2 HP3 Continuous latent variables NE HP Categorical latent variables CLASS Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 1000 Convergence criterion 0.100D-05 Optimization Specifications for the EM Algorithm Maximum number of iterations 500 Convergence criteria Loglikelihood change 0.100D-06 Relative loglikelihood change 0.100D-06 Derivative 0.100D-05 Optimization Specifications for the M step of the EM Algorithm for Categorical Latent variables Number of M step iterations 1 M step convergence criterion 0.100D-05 Basis for M step termination ITERATION Optimization Specifications for the M step of the EM Algorithm for Censored, Binary or Ordered Categorical (Ordinal), Unordered Categorical (Nominal) and Count Outcomes Number of M step iterations 1 M step convergence criterion 0.100D-05 Basis for M step termination ITERATION Maximum value for logit thresholds 15 Minimum value for logit thresholds -15 Minimum expected cell size for chi-square 0.100D-01 Optimization algorithm EMA Random Starts Specifications Number of initial stage random starts 500 Number of final stage optimizations 10 Number of initial stage iterations 10 Initial stage convergence criterion 0.100D+01 Random starts scale 0.500D+01 Random seed for generating random starts 0 Input data file(s) hp.dat Input data format FREE RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES 167 perturbed starting value run(s) did not converge in the initial stage optimizations. Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers: -658.986 281462 285 -658.986 562716 300 -658.986 437181 135 -658.986 21345 199 -658.986 260601 36 -658.986 948615 140 -658.986 137377 397 -658.986 801065 393 -658.986 120506 45 -658.986 224950 455 THE MODEL ESTIMATION TERMINATED NORMALLY TESTS OF MODEL FIT Loglikelihood H0 Value -658.986 H0 Scaling Correction Factor 1.042 for MLR Information Criteria Number of Free Parameters 25 Akaike (AIC) 1367.973 Bayesian (BIC) 1473.685 Sample-Size Adjusted BIC 1394.332 (n* = (n + 2) / 24) FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES BASED ON THE ESTIMATED MODEL Latent Classes 1 155.91938 0.30753 2 351.08062 0.69247 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Classes 1 155.91932 0.30753 2 351.08068 0.69247 CLASSIFICATION QUALITY Entropy 0.656 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP Class Counts and Proportions Latent Classes 1 126 0.24852 2 381 0.75148 Average Latent Class Probabilities for Most Likely Latent Class Membership (Row) by Latent Class (Column) 1 2 1 0.937 0.063 2 0.099 0.901 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Latent Class 1 NE BY NE1 1.000 0.000 999.000 999.000 NE2 1.155 0.043 27.113 0.000 NE3 1.097 0.054 20.210 0.000 HP BY HP1 1.000 0.000 999.000 999.000 HP2 0.984 0.066 14.956 0.000 HP3 0.824 0.055 14.940 0.000 HP ON NE 0.019 0.032 0.590 0.555 Means NE 2.678 0.089 30.026 0.000 Intercepts NE1 0.000 0.000 999.000 999.000 NE2 -0.335 0.079 -4.246 0.000 NE3 0.031 0.097 0.318 0.750 HP1 0.000 0.000 999.000 999.000 HP2 0.080 0.036 2.198 0.028 HP3 0.051 0.029 1.730 0.084 HP 0.392 0.097 4.060 0.000 Variances NE 0.492 0.063 7.809 0.000 Residual Variances NE1 0.127 0.012 10.358 0.000 NE2 0.045 0.012 3.836 0.000 NE3 0.239 0.023 10.488 0.000 HP1 0.018 0.002 8.790 0.000 HP2 0.019 0.002 9.482 0.000 HP3 0.020 0.002 11.831 0.000 HP 0.023 0.004 6.298 0.000 Latent Class 2 NE BY NE1 1.000 0.000 999.000 999.000 NE2 1.155 0.043 27.113 0.000 NE3 1.097 0.054 20.210 0.000 HP BY HP1 1.000 0.000 999.000 999.000 HP2 0.984 0.066 14.956 0.000 HP3 0.824 0.055 14.940 0.000 HP ON NE -0.364 0.062 -5.905 0.000 Means NE 1.573 0.027 58.248 0.000 Intercepts NE1 0.000 0.000 999.000 999.000 NE2 -0.335 0.079 -4.246 0.000 NE3 0.031 0.097 0.318 0.750 HP1 0.000 0.000 999.000 999.000 HP2 0.080 0.036 2.198 0.028 HP3 0.051 0.029 1.730 0.084 HP 1.124 0.095 11.892 0.000 Variances NE 0.066 0.009 7.208 0.000 Residual Variances NE1 0.127 0.012 10.358 0.000 NE2 0.045 0.012 3.836 0.000 NE3 0.239 0.023 10.488 0.000 HP1 0.018 0.002 8.790 0.000 HP2 0.019 0.002 9.482 0.000 HP3 0.020 0.002 11.831 0.000 HP 0.021 0.003 7.577 0.000 Categorical Latent Variables Means CLASS#1 -0.812 0.155 -5.225 0.000 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.159E-04 (ratio of smallest to largest eigenvalue) Beginning Time: 20:04:39 Ending Time: 20:09:03 Elapsed Time: 00:04:24 MUTHEN & MUTHEN 3463 Stoner Ave. Los Angeles, CA 90066 Tel: (310) 391-9971 Fax: (310) 391-8971 Web: www.StatModel.com Support: Support@StatModel.com Copyright (c) 1998-2007 Muthen & Muthen