因子数決定(性役割自己概念尺度)
Run MATRIX procedure:
************ 因子数判定 ************
nvars = 14
Velicer's Minimum Average Partial (MAP) Test:
Velicer's Average Squared Correlations
.0000 .0856
1.0000 .0446
* 2.0000 .0232
3.0000 .0303
4.0000 .0376
5.0000 .0490
6.0000 .0637
7.0000 .0853
8.0000 .1146
9.0000 .1527
10.0000 .2093
11.0000 .3062
12.0000 .4712
13.0000 1.0000
The smallest average squared correlation is
.0232
MAP says :The number of components is
2
Standard Error Scree (Zoski & Jurs, 1996, EPM, p 443):
Standard Error Scree
se EigenVal
1.0000 .7859 4.3131
* 2.0000 .3786 2.4615
3.0000 .0517 1.0058
4.0000 .0475 .9531
5.0000 .0264 .7849
6.0000 .0246 .7082
7.0000 .0251 .6740
8.0000 .0148 .5533
9.0000 .0143 .5165
10.0000 .0096 .4973
11.0000 .0105 .4549
12.0000 .0130 .4188
compared to
.0714
SE Scree says: The number of components is
2
PARALLEL ANALYSIS:
Principal Components(PA1) and Principal Axis(PAsmc)
Specifications for this Run:
Ncases 241
Nvars 14
Ndatsets 100
Percent 95
Random Data Eigenvalues
Root eval PA1 M PA1 95% evalsmc PAsmc M PAsmc 95
1.0000 4.3131 1.4259 1.5131 3.7062 .4923 .5882
-> 2.0000 2.4615 1.3293 1.3929 1.8830 .3900 .4653
3.0000 1.0058 1.2452 1.2946 .4069 .3021 .3592
=> 4.0000 .9531 1.1823 1.2341 .3120 .2347 .2937
5.0000 .7849 1.1203 1.1643 .1657 .1713 .2220
6.0000 .7082 1.0598 1.0983 .0807 .1087 .1532
7.0000 .6740 1.0090 1.0491 .0108 .0552 .0945
8.0000 .5533 .9570 1.0027 -.0437 .0040 .0507
9.0000 .5165 .9066 .9476 -.0740 -.0456 -.0078
10.0000 .4973 .8595 .9000 -.1198 -.0915 -.0568
11.0000 .4549 .8110 .8531 -.1746 -.1371 -.1009
12.0000 .4188 .7586 .8011 -.2026 -.1865 -.1501
13.0000 .3485 .7001 .7466 -.2160 -.2398 -.2023
14.0000 .3100 .6354 .6929 -.2503 -.2972 -.2516
PA 1 says: The number of components is
2
PA SMC says: The number of factors is
4
それぞれのお薦め因子数
MAP 2
PA1 2
PA SMC 4
SE scree 2
------ END MATRIX -----