Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:20 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a two-level multiple
indicator growth model with categorical
outcomes (three-level analysis)
DATA: FILE IS ex9.15.dat;
VARIABLE: NAMES ARE u11 u21 u31 u12 u22 u32 u13 u23
u33 clus;
CATEGORICAL = u11-u33;
CLUSTER = clus;
ANALYSIS: TYPE IS TWOLEVEL;
ESTIMATOR = WLSM;
MODEL:
%WITHIN%
f1w BY u11
u21-u31 (1-2);
f2w BY u12
u22-u32 (1-2);
f3w BY u13
u23-u33 (1-2);
iw sw | f1w@0 f2w@1 f3w@2;
%BETWEEN%
f1b BY u11
u21-u31 (1-2);
f2b BY u12
u22-u32 (1-2);
f3b BY u13
u23-u33 (1-2);
[u11$1 u12$1 u13$1] (3);
[u21$1 u22$1 u23$1] (4);
[u31$1 u32$1 u33$1] (5);
ib sb | f1b@0 f2b@1 f3b@2;
[f1b-f3b@0 ib@0 sb];
f1b-f3b (6);
SAVEDATA: SWMATRIX = ex9.15sw.dat;
*** WARNING
One or more individual-level variables have no variation within a
cluster for the following clusters.
Variable Cluster IDs with no within-cluster variation
U11 1 2 4 5 6 8 9 10 12 13 15 17 18 19 21 23 24 25 26 31 32 34 35 36 37 38 40 42
43 45 46 50 54 56 60 67 77 84
U21 1 5 8 12 15 17 20 23 27 31 32 33 35 36 38 39 42 44 45 46 47 50 54 60 63 88 89
U31 1 2 3 5 8 9 12 13 14 15 17 20 23 25 34 35 36 37 38 39 40 43 44 45 47 49 50 56
60 61 67 71 81 83 88
U12 1 2 8 9 10 14 16 17 19 20 22 24 25 30 33 35 37 40 41 44 45 46 48 49 50 52 54
55 65 69 87
U22 2 6 8 9 12 14 15 16 17 18 20 21 22 24 27 29 30 32 33 34 35 37 39 40 41 42 43
44 45 46 47 48 50 53 54 55 58 59 60 69 82 87
U32 1 7 8 12 16 17 19 20 22 25 26 29 30 31 32 35 36 37 38 39 42 46 48 50 51 59 60
69 74 88
U13 1 2 3 6 8 9 10 11 12 13 14 16 17 18 20 21 22 24 25 28 30 31 32 33 35 37 38 39
41 42 45 46 48 50 52 53 54 55 56 60 62 64 65 67 71 72 75 77 80 85
U23 1 2 6 8 9 10 11 12 13 14 15 17 18 19 20 21 22 23 25 28 29 30 31 32 33 35 36
37 39 40 41 42 45 46 48 52 54 56 57 59 60 63 65 71 73 75 76 77 78 82 85 90
U33 1 4 8 9 10 12 14 17 20 21 22 25 27 30 33 35 37 38 39 41 42 44 45 46 47 51 52
53 54 56 63 65 67 72 74
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
this is an example of a two-level multiple
indicator growth model with categorical
outcomes (three-level analysis)
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 9
Number of independent variables 0
Number of continuous latent variables 10
Observed dependent variables
Binary and ordered categorical (ordinal)
U11 U21 U31 U12 U22 U32
U13 U23 U33
Continuous latent variables
F1W F2W F3W IW SW F1B
F2B F3B IB SB
Variables with special functions
Cluster variable CLUS
Estimator WLSM
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-02
Relative loglikelihood change 0.100D-05
Derivative 0.100D-02
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-02
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-02
Basis for M step termination ITERATION
Maximum value for logit thresholds 10
Minimum value for logit thresholds -10
Minimum expected cell size for chi-square 0.100D-01
Optimization algorithm FS
Integration Specifications
Type STANDARD
Number of integration points 7
Dimensions of numerical integration 2
Adaptive quadrature ON
Link PROBIT
Cholesky ON
Input data file(s)
ex9.15.dat
Input data format FREE
SUMMARY OF DATA
Number of clusters 90
Average cluster size 5.556
Estimated Intraclass Correlations for the Y Variables
Intraclass Intraclass Intraclass
Variable Correlation Variable Correlation Variable Correlation
U11 0.493 U21 0.203 U31 0.421
U12 0.270 U22 0.341 U32 0.360
U13 0.434 U23 0.426 U33 0.397
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U11
Category 1 0.534 267.000
Category 2 0.466 233.000
U21
Category 1 0.386 193.000
Category 2 0.614 307.000
U31
Category 1 0.622 311.000
Category 2 0.378 189.000
U12
Category 1 0.282 141.000
Category 2 0.718 359.000
U22
Category 1 0.242 121.000
Category 2 0.758 379.000
U32
Category 1 0.448 224.000
Category 2 0.552 276.000
U13
Category 1 0.184 92.000
Category 2 0.816 408.000
U23
Category 1 0.160 80.000
Category 2 0.840 420.000
U33
Category 1 0.314 157.000
Category 2 0.686 343.000
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 25
Chi-Square Test of Model Fit
Value 70.639*
Degrees of Freedom 65
P-Value 0.2949
Scaling Correction Factor 0.6909
for WLSM
* The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
for chi-square difference testing in the regular way. MLM, MLR and WLSM
chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
and ULSMV difference testing is done using the DIFFTEST option.
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.013
CFI/TLI
CFI 0.996
TLI 0.995
Chi-Square Test of Model Fit for the Baseline Model
Value 1354.865
Degrees of Freedom 72
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value for Within 0.067
Value for Between 0.142
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
IW |
F1W 1.000 0.000 999.000 999.000
F2W 1.000 0.000 999.000 999.000
F3W 1.000 0.000 999.000 999.000
SW |
F1W 0.000 0.000 999.000 999.000
F2W 1.000 0.000 999.000 999.000
F3W 2.000 0.000 999.000 999.000
F1W BY
U11 1.000 0.000 999.000 999.000
U21 0.900 0.162 5.572 0.000
U31 0.756 0.128 5.924 0.000
F2W BY
U12 1.000 0.000 999.000 999.000
U22 0.900 0.162 5.572 0.000
U32 0.756 0.128 5.924 0.000
F3W BY
U13 1.000 0.000 999.000 999.000
U23 0.900 0.162 5.572 0.000
U33 0.756 0.128 5.924 0.000
SW WITH
IW -0.287 0.238 -1.203 0.229
Variances
IW 1.724 0.607 2.842 0.004
SW 0.351 0.234 1.500 0.134
Residual Variances
F1W 1.134 0.639 1.775 0.076
F2W 0.548 0.375 1.459 0.145
F3W 0.746 0.658 1.134 0.257
Between Level
IB |
F1B 1.000 0.000 999.000 999.000
F2B 1.000 0.000 999.000 999.000
F3B 1.000 0.000 999.000 999.000
SB |
F1B 0.000 0.000 999.000 999.000
F2B 1.000 0.000 999.000 999.000
F3B 2.000 0.000 999.000 999.000
F1B BY
U11 1.000 0.000 999.000 999.000
U21 0.900 0.162 5.572 0.000
U31 0.756 0.128 5.924 0.000
F2B BY
U12 1.000 0.000 999.000 999.000
U22 0.900 0.162 5.572 0.000
U32 0.756 0.128 5.924 0.000
F3B BY
U13 1.000 0.000 999.000 999.000
U23 0.900 0.162 5.572 0.000
U33 0.756 0.128 5.924 0.000
SB WITH
IB -0.147 0.192 -0.765 0.444
Means
IB 0.000 0.000 999.000 999.000
SB 1.044 0.189 5.523 0.000
Intercepts
F1B 0.000 0.000 999.000 999.000
F2B 0.000 0.000 999.000 999.000
F3B 0.000 0.000 999.000 999.000
Thresholds
U11$1 -0.079 0.185 -0.431 0.667
U21$1 -0.534 0.154 -3.467 0.001
U31$1 0.566 0.151 3.756 0.000
U12$1 -0.079 0.185 -0.431 0.667
U22$1 -0.534 0.154 -3.467 0.001
U32$1 0.566 0.151 3.756 0.000
U13$1 -0.079 0.185 -0.431 0.667
U23$1 -0.534 0.154 -3.467 0.001
U33$1 0.566 0.151 3.756 0.000
Variances
IB 0.580 0.356 1.628 0.103
SB 0.293 0.223 1.313 0.189
Residual Variances
U11 2.774 1.239 2.238 0.025
U21 0.061 0.320 0.191 0.849
U31 1.357 0.479 2.834 0.005
U12 0.157 0.380 0.413 0.680
U22 0.593 0.477 1.242 0.214
U32 0.668 0.332 2.013 0.044
U13 1.297 1.130 1.148 0.251
U23 1.122 0.742 1.512 0.130
U33 0.799 0.456 1.753 0.080
F1B 0.390 0.186 2.094 0.036
F2B 0.390 0.186 2.094 0.036
F3B 0.390 0.186 2.094 0.036
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.337E-03
(ratio of smallest to largest eigenvalue)
SAVEDATA INFORMATION
Within and between sample statistics with Weight matrix
Save file
ex9.15sw.dat
Save format Free
Beginning Time: 23:20:13
Ending Time: 23:20:18
Elapsed Time: 00:00:05
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) 1998-2022 Muthen & Muthen
Back to examples