Mplus CFA Example 5.1
来自OBHRM百科
示意图
代码与注释
TITLE: this is an example of a CFA with ! 这是标题，总共两行内容，第一行
continuous factor indicators; ! 第二行，想要多少行，就写多少行
DATA: FILE IS ex5.1.dat; ! 读数据文件，文件名要正确，文件路径与对应的分析程序在同一目录下；或标明绝对路径，比如：c:\mplus\ex5.1.dat。
VARIABLE: NAMES ARE y1-y6; ! 读取数据，该数据文件中包括6个变量的数据，变量名称可以自己定。比如，可以是y1-y6，也可以是item1-item6
MODEL: f1 BY y1-y3; ! 定义第1个因素，该因素的测量指标有：y1,y2,y3。如果上面为item1-item6，则修改为item1-item3
f2 BY y4-y6; ! 定义第2个因素，该因素的测量指标有：y4,y5,y6
结果
Mplus VERSION 7.4 ！ Mplus的版本信息
MUTHEN & MUTHEN ！ Mplus作者信息
10/26/2015 7:54 PM ！ 分析时间
INPUT INSTRUCTIONS ！输入的命令语言，会全部显示，下面几行就是输入的命令语句
TITLE: this is an example of a CFA with
continuous factor indicators
DATA: FILE IS ex5.1.dat;
VARIABLE: NAMES ARE y1-y6;
MODEL: f1 BY y1-y3;
f2 BY y4-y6;
INPUT READING TERMINATED NORMALLY ！数据读取正常结束，表明数据文件没问题。
this is an example of a CFA with ！TITLE，标题
continuous factor indicators
SUMMARY OF ANALYSIS ！分析总体情况
Number of groups 1 ！1组数据，也就是数据没有分组
Number of observations 500 ！样本量500
Number of dependent variables 6 ！（因）变量6个
Number of independent variables 0 ！（自）变量0个
Number of continuous latent variables 2 ！潜变量2个
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4 Y5 Y6
Continuous latent variables
F1 F2
Estimator ML
Information matrix OBSERVED
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Input data file(s)
ex5.1.dat
Input data format FREE
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 19
Loglikelihood
H0 Value -4906.609
H1 Value -4904.661
Information Criteria
Akaike (AIC) 9851.218
Bayesian (BIC) 9931.295
Sample-Size Adjusted BIC 9870.988
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit
Value 3.896
Degrees of Freedom 8
P-Value 0.8664
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
90 Percent C.I. 0.000 0.027
Probability RMSEA <= .05 0.995
CFI/TLI
CFI 1.000
TLI 1.013
Chi-Square Test of Model Fit for the Baseline Model
Value 596.921
Degrees of Freedom 15
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.014
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
F1 BY
Y1 1.000 0.000 999.000 999.000
Y2 1.126 0.099 11.368 0.000
Y3 1.019 0.089 11.482 0.000
F2 BY
Y4 1.000 0.000 999.000 999.000
Y5 1.059 0.129 8.199 0.000
Y6 0.897 0.105 8.531 0.000
F2 WITH
F1 -0.030 0.052 -0.582 0.560
Intercepts
Y1 -0.022 0.063 -0.354 0.723
Y2 0.026 0.062 0.410 0.682
Y3 0.035 0.062 0.555 0.579
Y4 -0.022 0.064 -0.350 0.726
Y5 -0.016 0.058 -0.271 0.786
Y6 0.048 0.058 0.824 0.410
Variances
F1 0.907 0.125 7.254 0.000
F2 0.760 0.133 5.734 0.000
Residual Variances
Y1 1.064 0.096 11.120 0.000
Y2 0.798 0.100 7.972 0.000
Y3 1.010 0.095 10.597 0.000
Y4 1.290 0.119 10.871 0.000
Y5 0.854 0.111 7.710 0.000
Y6 1.066 0.097 11.024 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.409E-01
(ratio of smallest to largest eigenvalue)
Beginning Time: 19:54:46
Ending Time: 19:54:46
Elapsed Time: 00:00:00
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