因子数決定(製品関与)
Run MATRIX procedure:
************ 因子数判定 ************
nvars = 15
Velicer's Minimum Average Partial (MAP) Test:
Velicer's Average Squared Correlations
.0000 .1331
1.0000 .0693
* 2.0000 .0473
3.0000 .0476
4.0000 .0537
5.0000 .0616
6.0000 .0764
7.0000 .0920
8.0000 .0999
9.0000 .1215
10.0000 .1586
11.0000 .2099
12.0000 .3066
13.0000 .4676
14.0000 1.0000
The smallest average squared correlation is
.0473
MAP says :The number of components is
2
Standard Error Scree (Zoski & Jurs, 1996, EPM, p 443):
Standard Error Scree
se EigenVal
1.0000 .9901 5.4813
2.0000 .4114 2.7347
3.0000 .1503 1.4581
* 4.0000 .0790 1.0455
5.0000 .0477 .7921
6.0000 .0438 .6897
7.0000 .0407 .5762
8.0000 .0424 .5475
9.0000 .0258 .4150
10.0000 .0207 .3370
11.0000 .0174 .2771
12.0000 .0056 .2076
13.0000 .0008 .1693
compared to
.0667
SE Scree says: The number of components is
4
PARALLEL ANALYSIS:
Principal Components(PA1) and Principal Axis(PAsmc)
Specifications for this Run:
Ncases 56
Nvars 15
Ndatsets 100
Percent 95
Random Data Eigenvalues
Root eval PA1 M PA1 95% evalsmc PAsmc M PAsmc 95
1.0000 5.4813 1.9923 2.2003 5.1323 1.2824 1.5292
-> 2.0000 2.7347 1.7288 1.8910 2.3770 1.0038 1.2049
=> 3.0000 1.4581 1.5535 1.6840 1.1124 .8189 .9897
4.0000 1.0455 1.3970 1.5105 .6638 .6480 .8046
5.0000 .7921 1.2680 1.3567 .4394 .5095 .6339
6.0000 .6897 1.1497 1.2506 .2805 .3848 .5055
7.0000 .5762 1.0368 1.1296 .1968 .2637 .3731
8.0000 .5475 .9331 1.0225 .1885 .1559 .2591
9.0000 .4150 .8321 .9194 .0277 .0544 .1470
10.0000 .3370 .7317 .8063 -.0312 -.0392 .0338
11.0000 .2771 .6471 .7231 -.0505 -.1210 -.0582
12.0000 .2076 .5661 .6472 -.1352 -.1960 -.1402
13.0000 .1693 .4782 .5644 -.1815 -.2714 -.2256
14.0000 .1467 .3927 .4800 -.2004 -.3366 -.2914
15.0000 .1223 .2928 .3644 -.2156 -.4077 -.3684
PA 1 says: The number of components is
2
PA SMC says: The number of factors is
3
それぞれのお薦め因子数
MAP 2
PA1 2
PA SMC 3
SE scree 4
------ END MATRIX -----