Analisis Statistik Faktor-Faktor yang Mempengaruhi Indeks Prestasi Mahasiswa
LAMPIRAN B. HASIL DATA UJICOBA KUESIONER
4
4
4
4
4
4
21
3 3 2011
4
4
4
4
4
3
4
3
3
5
4
3 3 2010
3
4
4
4
3
23
3 2 2009
4
3
5
3
3
2
4
3
3
22
4 5 2011
20
2
4
3
3
3
4
3
3
3
2
17
3 3 2010
2 2 2010
2
3
2
2
2
2
4
2
18
4
19
4
4
3
3
4
4
3
2 3 2010
3
4
4
4
2
3
3
4
3
4
4
3
3
29
3 3 2010
4
2
4
4
4
4
4
4
3
28
4 4 2009
4
4
3
4
5
3
4
2 2 2009
4
2
3
5
4
3
3
3
4
30
2 5 2010
5
4
4
4
4
4
3
4
3
4
3
4
25
4 4 2011
3
5
3
3
3
3
4
3
4
24
3 4 2009
3
3
4
4
4
3
4
27
4 4 2011
5
3
4
4
4
4
5
4
4
26
4 2 2011
4
4
2
No X_1 X_2 X_3 X_4 X_5 X_6 X_7 X_8 X_9 X_10 X_11 Angkatan
1
4
4
4
3
6
4 3 2010
2
3
3
4
4
2
4
3
4
5
3 4 2009
4
4
4
5
4
3
8
4 2 2009
5
4
5
4
4
2
4
3
2
7
3 4 2010
4
3
4
3
4
3 4 2011
4
4
3
4
3
3
2
4
3
3
4
4
4
4
3
3
4
4
3
3
5
3
3
3
2
3
4
4 4 2011
5
4 2 2011
5
4
4
4
4
3
3
3
4
4
3 4 2010
5
5 2 2011
5
4
4
4
4
3
3
3
4
13
3 4 2011
4
2
4
4
3
14
2
3
3
4
2
3
4
3
3
4
3
3
15
3 2 2010
4
2
4
4
2
3
2
2
4
3
2
10
3 3 2009
4
2
4
4
4
3
4
3
4
9
3 2 2009
5
3
4
2
3
3
2
12
3 2 2011
3
2
3
2
2
3
2
2
2
11
1 3 2011
3
2
4
2
16
LAMPIRAN C. HASIL PENSKALAAN DATA UJICOBA KUESIONER
No X_1 X_2 X_3 X_4 X_5 X_6 X_7 X_8 X_9 X_10 X_11 Angkatan
1 3,782 2,267 1,000 3,632 3,412 2,856 3,572 2,016 2,704 2,999 2,862 2011 2 2,378 2,267 2,508 2,309 3,412 2,856 3,572 2,016 1,820 4,192 1,000 2011 3 2,378 2,267 2,508 3,632 3,412 2,856 5,167 4,172 3,926 4,192 2,862 2011 4 2,378 1,000 1,000 2,309 2,114 1,765 2,201 2,952 2,704 2,999 2,862 2009 5 3,782 2,267 2,508 1,000 3,412 1,765 3,572 2,016 1,000 4,192 2,006 2010 6 2,378 3,544 2,508 3,632 3,412 2,856 3,572 2,016 2,704 2,999 2,862 2010 7 1,000 2,267 2,508 1,000 3,412 4,370 5,167 2,952 3,926 4,192 1,000 2009 8 2,378 3,544 2,508 3,632 3,412 2,856 3,572 2,016 3,926 2,999 1,000 2009 9 3,782 2,267 2,508 3,632 2,114 2,856 3,572 1,000 2,704 2,999 2,006 2009 10 1,000 1,000 1,000 2,309 1,000 1,000 3,572 1,000 1,820 1,000 2,006 2011
11 1,000 1,000 1,000 1,000 1,000 1,000 2,201 1,000 1,820 2,999 1,000 2011 12 2,378 1,000 1,000 1,000 2,114 2,856 3,572 1,000 2,704 2,999 2,862 2011 13 3,782 2,267 4,067 2,309 3,412 2,856 3,572 2,952 3,926 5,454 1,000 2011 14 2.378 1,000 1,000 2.309 1,000 2,856 3,572 1,000 2,704 2,999 1,000 2010 15 2,378 2,267 2,508 2,309 2,114 2,856 2,201 1,000 2,704 2,999 2,862 2010 16 2,378 1,000 2,508 1,000 1,000 1,000 1,000 2,016 1,000 1,910 1,000 2010 17 2,378 1,000 1,000 2,309 2,114 2,856 2,201 2,016 1,000 2,999 2,006 2010 18 2,378 2,267 2,508 2,309 2,114 1,000 3,572 2,952 2,704 1,910 2,006 2010 19 2,378 3,544 2,508 2,309 2,114 2,856 3,572 2,952 1,000 2,999 2,006 2010 20 2,378 2,267 2,508 2,309 3,412 2,856 3,572 2,952 2,704 2,999 2,006 2011 21 3,782 3,544 2,508 3,632 3,412 4,370 3,572 2,952 3,926 4,192 4,030 2011 22 2,378 2,267 2,508 1,000 2,114 1,765 2,201 2,016 2,704 2,999 1,000 2009 23 2,378 3,544 2,508 3,632 3,412 2,856 3,572 2,952 1,820 2,999 2,862 2009 24 3,782 2,267 2,508 2,309 2,114 1,765 2,201 4,172 1,820 4,192 2,862 2011 25 3,782 2,267 2,508 2,309 2,114 2,856 3,572 2,952 2,704 4,192 1,000 2011 26 3,782 3,544 4,067 3,632 3,412 2,856 3,572 2,016 3,926 4,192 2,862 2011 27 3,782 2,267 2,508 2,309 2,114 2,856 2,201 2,952 2,704 4,192 2,862 2009 28 2,378 3,544 2,508 2,309 3,412 2,856 3,572 1,000 2,704 2,999 2,006 2010 29 3,782 3,544 4,067 2,309 3,412 2,856 3,572 2,952 3,926 1,910 4,030 2010 30 3,782 2,267 2,508 2,309 3,412 4,370 2,201 1,000 2,704 1,910 1,000 2009
LAMPIRAN D. HASIL PENGOLAHAN DATA UJICOBA
MENGGUNAKAN SPSS 16.0 Reliability Case Processing Summary N % Cases Valid
30 100.0 Excluded a .0 Total 30 100.0
a. Listwise deletion based on all variables in the procedure.
Reliability Statistics Cronbach's Alpha
Cronbach's Alpha Based on Standardized Items N of Items
.844 .845
11 Item Statistics Mean Std. Deviation N
X_1 2.75500 .892810
30 X_2 2.31190 .913922
30 X_3 2.31203 .872132
30 X_4 2.40000 .910382
30 X_5 2.61447 .885166
30
Inter-Item Correlation Matrix
X_1 X_2 X_3 X_4 X_5 X_6 X_7 X_8 X_9 X_10 X_11
X_1 1.000 .353 .479 .347 .366 .297 -.094 .238 .205 .366 .333X_2 .353 1.000 .680 .551 .706 .447 .377 .286 .385 .210 .351 X_3 .479 .680 1.000 .198 .540 .255 .185 .373 .438 .348 .109 X_4 .347 .551 .198 1.000 .435 .352 .336 .194 .384 .097 .442 X_5 .366 .706 .540 .435 1.000 .617 .520 .314 .488 .398 .265 X_6 .297 .447 .255 .352 .617 1.000 .412 .062 .499 .353 .147 X_7 -.094 .377 .185 .336 .520 .412 1.000 .238 .496 .293 .110 X_8 .238 .286 .373 .194 .314 .062 .238 1.000 .204 .445 .306 X_9 .205 .385 .438 .384 .488 .499 .496 .204 1.000 .293 .217 X_10 .366 .210 .348 .097 .398 .353 .293 .445 .293 1.000 -.006 X_11 .333 .351 .109 .442 .265 .147 .110 .306 .217 -.006 1.000
Item-Total Statistics Scale Mean if Item Deleted
Scale Variance if Item Deleted
Corrected Item-
Total Correlation
Squared Multiple Correlation Cronbach's Alpha if Item Deleted
X_1 25.64430 33.892 .453 .544 .837 X_2 26.08740 31.373 .703 .766 .816 X_3 26.08727 32.888 .575 .729 .827 X_4 25.99930 33.040 .529 .527 .831 X_5 25.78483 31.108 .761 .679 .812 X_6 25.78483 32.930 .546 .530 .829
LAMPIRAN E. HASIL DATA KUESIONER
3
4
3
3
3
3
2
4
3
22
23
4 5 2011
5
4
4
5
4
4
4
3 2 2009
3
4
4
3
5
3
3
3
3
4
3
24
4
3 4 2009
3
4
4
4
4
4
4
4
21
25
3
4
4
4
2
3
3
4
3
18
19
3 3 2010
2
3
3
4
3
3
3
2 3 2010
3
3 3 2011
3
4
4
4
4
4
3
4
3
20
4
3 3 2010
2
4
4
4
3
3
4
4 4 2011
4
3
3
2 2 2009
4
2
3
5
4
3
4
4
3
30
2 5 2010
5
4
4
4
4
3
31
4
4
3
2 3 2010
3
4
4
2
3
3
4
3
4
32
2 3 2010
5
4
4
5
4
2
5
4
3
4
5
3
4
4
4
4
5
4
26
27
4 2 2011
4
4
4
4
3
3
4
4 4 2011
4
29
4
3 3 2010
4
2
4
4
4
3
4
3
3
28
4 4 2009
4
4
3
4
3
3
4
2
S1 MATEMATIKA USU
No X_1 X_2 X_3 X_4 X_5 X_6 X_7 X_8 X_9 X_10 X_11 Angkatan
1
6
3
4
4
4
4
4
4
3
4 3 2010
3 4 2010
2
3
4
3
4
2
4
3
4
7
5
8
3
4
4
4
4
4
4
3
4 2 2009
2
5
4
5
5
4
2
4
3
4
3 4 2009
3 2 2009
3 4 2011
4
4
4
3
4
3
3
2
4
3
3
4
4
4
4
3
3
4
3
4 2 2011
4
4
4
3
3
3
3
3
2
3
4 4 2011
3
5
5
5
4
4
4
4
3
3
5
9
2 2 2010
3
4
2
4
4
2
3
3
2
14
15
5 2 2011
5
4
4
4
4
3
5
3 2 2010
3
4
3
2
3
2
2
2
2
4
2
16
3
3 4 2010
4
2
3
4
3
3
4
3
13
4
10
2
4
2
2
3
3
2
2
3 3 2009
1 3 2011
4
2
4
4
3
4
4
3
3
11
3 4 2011
3
4
2
4
4
3
2
3
2
12
2
3 2 2011
3
2
3
2
2
2
3
2
17
52
3
2
3
77
3 2 2010
5
4
3
5
5
4
3
3
3
76
2 4 2009
5
5
2
2
4
3
4
2
2
3
5
3
2
5
2
4
79
4 2 2011
2
3
4
3
3
3
4
4
3
78
3 2 2011
4
3
3
4
3
3
5
5
3
3
4
2
4
72
2 3 2010
3
3
3
5
4
3
4
3
2
71
3 3 2011
3
4
4
4
75
74
3 3 2011
5
5
5
4
4
4
4
3
3
3 4 2009
4 2 2010
4
4
3
3
3
3
3
2
3
73
4
4
4
4
4
4
5
3
4
86
4 2 2010
4
4
4
4
5
3
4
4
4
85
4 4 2010
2
5
3
4
3
1
4 5 2011
4
2
4
4
4
3
4
4
3
88
4
5
4
3
4
3
3
4
3
4
87
4 4 2009
3
3
2 3 2009
81
5
4
4
5
4
4
4
4
4
3 3 2010
82
5
3
3
4
5
3
4
2
3
80
4 5 2009
3
4
4
4
4
84
3 4 2011
1
4
4
4
4
4
4
3
3
83
3 2 2011
4
3
3
3
3
2
4
4
4
3
5
4
3
5
3
4
4
3
58
3 4 2009
4
4
3
5
4
4
4
3
3
57
3 4 2009
5
4
3 4 2010
4
5
61
5 3 2009
5
4
5
4
4
3
4
4
60
59
4 3 2010
5
3
4
4
4
3
4
3
4
4
5
2
3
54
3 4 2010
4
4
4
4
4
4
4
3
4
53
3 4 2010
5
4
3
5
4
4
4
3
2
4
3
2
3
5
4
3
56
1 3 2009
4
4
4
2
3
3
3
2
2
55
3 4 2010
2
3
4
2
4
2
3
3
3
3 3 2009
2
3
3
4
3
3
3
2
67
3
2 2 2009
4
3
2
2
2
2
4
2
3
68
3
3 4 2010
3
4
3
3
70
3 3 2011
4
4
4
4
3
4
4
4
3
69
2 3 2011
2
4
4
2
3
3
66
2
2
3
5
3
4
63
3 4 2009
4
2
4
4
2
4
3
2
3
62
3 2 2009
3
2
3
2
2
3
4
2
2
3
4
3
3
4
3
3
65
3 2 2011
4
4
4
4
2
3
3
2
3
64
5 2 2011
5
4
3 3 2011
LAMPIRAN F. HASIL PENSKALAAN DATA KUESIONER
No X_1 X_2 X_3 X_4 X_5 X_6 X_7 X_8 X_9 X_10 X_11 Angkatan
1 3,748 3,560 2,332 3,729 4,028 2,723 3,666 1,977 3,381 3,053 2,883 2011 2 2,387 3,560 3,767 2,361 4,028 2,723 3,666 1,977 2,609 4,213 1,000 2011 3 2,387 3,560 3,767 3,729 4,028 2,723 5,253 4,287 4,580 4,213 2,883 2011 4 2,387 2,452 2,332 2,361 2,920 1,777 2,288 2,982 3,381 3,053 2,883 2009 5 3,748 3,560 3,767 1,000 4,028 1,777 3,666 1,977 2,000 4,213 2,038 2010 6 2,387 4,677 3,767 3,729 4,028 2,723 3,666 1,977 3,381 3,053 2,883 2010 7 1,000 3,560 3,767 1,000 4,028 4,027 5,253 2,982 4,580 4,213 1,000 2009 8 2,387 4,677 3,767 3,729 4,028 2,723 3,666 1,977 4,580 3,053 1,000 2009 9 3,748 3,560 3,767 3,729 2,920 2,723 3,666 1,000 3,381 3,053 2,038 2009 10 1,000 2,452 2,332 2,361 2,000 1,000 3,666 1,000 2,609 1,000 2,038 2011
11 1,000 2,452 2,332 1,000 2,000 1,000 2,288 1,000 2,609 3,053 1,000 2011 12 2,387 2,452 2,332 1,000 2,920 2,723 3,666 1,000 3,381 3,053 2,883 2011 13 3,748 3,560 5,292 2,361 4,028 2,723 3,666 2,982 4,580 5,536 1,000 2011 14 2,387 2,452 2,332 2,361 2,000 2,723 3,666 1,000 3,381 3,053 1,000 2010 15 2,387 3,560 3,767 2,361 2,920 2,723 2,288 1,000 3,381 3,053 2,883 2010 16 2,387 2,452 3,767 1,000 2,000 1,000 1,000 1,977 2,000 1,997 1,000 2010 17 2,387 2,452 2,332 2,361 2,920 2,723 2,288 1,977 2,000 3,053 2,038 2010 18 2,387 3,560 3,767 2,361 2,920 1,000 3,666 2,982 3,381 1,997 2,038 2010 19 2,387 4,677 3,767 2,361 2,920 2,723 3,666 2,982 2,000 3,053 2,038 2010 20 2,387 3,560 3,767 2,361 4,028 2,723 3,666 2,982 3,381 3,053 2,038 2011 21 3,748 4,677 3,767 3,729 4,028 4,027 3,666 2,982 4,580 4,213 3,935 2011 22 2,387 3,560 3,767 1,000 2,920 1,777 2,288 1,977 3,381 3,053 1,000 2009 23 2,387 4,677 3,767 3,729 4,028 2,723 3,666 2,982 2,609 3,053 2,883 2009 24 3,748 3,560 3,767 2.361 2,920 1,777 2,288 4,287 2,609 4,213 2,883 2011 25 3,748 3,560 3,767 2,361 2,920 2,723 3,666 2,982 3,381 4,213 1,000 2011 26 3,748 4,677 5,292 3,729 4,028 2,723 3,666 1,977 4,580 4,213 2,883 2011 27 3,748 3,560 3,767 2,361 2,920 2,723 2,288 2,982 3,381 4,213 2,883 2009 28 2,387 4,677 3,767 2,361 4,028 2,723 3,666 1,000 3,381 3,053 2,038 2010 29 3,748 4,677 5,292 2,361 4,028 2,723 3,666 2,982 4,580 1,997 3,935 2010 30 3,748 3,560 3,767 2,361 4,028 4,027 2,288 1,000 3,381 1,997 1,000 2009 31 2,387 4,677 3,767 1,000 4,028 4,027 3,666 2,982 4,580 1,997 2,038 2010 32 2,387 3,560 3,767 2,361 2,920 1,000 3,666 2,982 2,609 1,997 2,038 2010
52 2,387 3,560 3,767 3,729 4,028 4,027 2,288 2,982 4,580 3,053 2,883 2010 53 2,387 3,560 3,767 3,729 4,028 2,723 3,666 2,982 3,381 3,053 2,883 2010 54 1,000 4,677 3,767 2,361 4,028 1,000 3,666 1,977 2,000 3,053 2,883 2010 55 1,000 2,452 2,332 2,361 2,000 1,000 3,666 2,982 3,381 1,000 2,038 2009 56 2,387 4,677 5,292 2,361 2,920 4,027 3,666 2,982 4,580 3,053 2,883 2009 57 2,387 3,560 3,767 3,729 4,028 4,027 2,288 2,982 4,580 3,053 2,883 2009 58 2,387 4,677 3,767 2,361 5,406 1,777 3,666 2,982 3,381 3,053 2,883 2010 59 3,748 3,560 3,767 2,361 4,028 2,723 3,666 1,977 4,580 4,213 2,038 2010 60 5,420 4,677 3,767 2,361 4,028 2,723 5,253 2,982 4,580 5,536 2,038 2009 61 1,000 2,452 2,332 1,000 2,000 1,000 2,288 1,000 2,609 3,053 1,000 2009 62 2,387 2,452 2,332 1,000 2,920 2,723 3,666 1,000 3,381 3,053 2,883 2009 63 3,748 3,560 5,292 2,361 4,028 2,723 3,666 2,982 4,580 5,536 1,000 2011 64 2,387 2,452 2,332 2,361 2,000 2,723 3,666 1,000 3,381 3,053 1,000 2011 65 2,387 3,560 3,767 2,361 2,920 2,723 2,288 1,000 2,000 3,053 2,883 2010 66 2,387 2,452 3,767 1,000 2,000 1,000 1,000 1,977 3.381 1,997 1,000 2009 67 2,387 2,452 2,332 2,361 2,920 2,723 2,288 1,977 2,000 3,053 2,038 2009 68 2,387 3,560 3,767 2,361 2,920 1,000 3,666 2,982 2,000 1,997 2,038 2011 69 2,387 4,677 3,767 2,361 2,920 2,723 3,666 2,982 3,381 3,053 2,038 2011 70 2,387 3,560 3,767 2,361 4,028 2,723 3,666 2,982 2,609 3,053 2,038 2011 71 1,000 3,560 3,767 2,361 4,028 4,027 2,288 4,287 2,609 1,997 2,038 2010 72 3,748 2,452 3,767 2,361 2,920 4,027 3,666 1,977 3,381 4,213 1,000 2010 73 2,387 2,452 2,332 2,361 2,920 1,777 2,288 2,982 3,381 3,053 2,883 2009 74 2,387 3,560 3,767 3,729 4,028 2,723 5,253 4,287 4,580 3,053 2,038 2011 75 2,387 2,452 3,767 2,361 4,028 1,000 1,000 4,287 4,580 1,997 2,883 2009 76 2,387 3,560 3,767 2,361 5,406 4,027 2,288 2,982 4,580 3,053 1,000 2010 77 2,387 2,452 3,767 2,361 2,000 1,777 2,288 1,977 3,381 3,053 1,000 2011 78 2,387 4,677 3,767 2,361 2,920 2,723 2,288 1,977 2,000 4,213 1,000 2011 79 3,748 2,452 5,292 1,000 2,920 4,027 3,666 1,977 3,381 1,997 2,038 2009 80 2,387 2,452 3,767 2,361 5,406 2,723 2,288 1,977 4,580 3,053 2,038 2010 81 3,748 4,677 3,767 3,729 4,028 4,027 3,666 2,982 4,580 4,213 3,935 2009 82 2,387 3,560 3,767 1,000 2,920 1,777 2,288 1,977 3,381 3,053 1,000 2011 83 2,387 4,677 3,767 3,729 4,028 2,723 3,666 2,982 1,000 3,053 2,883 2011 84 3,748 4,677 3,767 2,361 1,000 1,777 2,288 4,287 2,000 4,213 2,883 2010 85 3,748 4,677 3,767 2,361 5,406 2,723 3,666 2,982 3,381 4,213 1,000 2010 86 3,748 3,560 5,292 3,729 4,028 2,723 3,666 1,977 4,580 4,213 2,883 2009 87 3,748 3,560 3,767 2,361 2,920 2,723 2,288 2,982 3,381 4,213 3,935 2011 88 2,387 4,677 3,767 2,361 4,028 2,723 3,666 1,000 3,381 3,053 2,038 2011
LAMPIRAN G. HASIL PENGOLAHAN DATA MENGGUNAKAN SPSS 16.0
Factor analysis
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .646
Bartlett's Test of Sphericity Approx. Chi-Square 219.291
df55 Sig. .000
Anti-image Matrices
X_1 X_2 X_3 X_4 X_5 X_6 X_7 X_8 X_9 X_10 X_1 ti-image Correlation X_1 .563 a -.106 -.241 -.219 .186 -.068 .205 .118 -.077 -.489 - X_2 -.106 .620 a -.368 -.152 -.210 -.142 -.196 -.085 .352 .022 - X_3 -.241 -.368 .654 a .243 -.017 .030 -.002 -.146 -.237 .022 - X_4 -.219 -.152 .243 .643 a -.127 -.027 -.049 -.025 -.097 .045 - X_5 .186 -.210 -.017 -.127 .741 a -.262 -.061 .004 -.243 -.111 - X_6 -.068 -.142 .030 -.027 -.262 .742 a -.008 .150 -.302 -.129 X_7 .205 -.196 -.002 -.049 -.061 -.008 .676 a -.066 -.236 -.208 X_8 .118 -.085 -.146 -.025 .004 .150 -.066 .666 a -.157 -.152 -
X_9 -.077 .352 -.237 -.097 -.243 -.302 -.236 -.157 .584
a .077 - X_10 -.489 .022 .022 .045 -.111 -.129 -.208 -.152 .077 .590 a X_11 -.150 -.117 -.094 -.223 -.032 .028 .053 -.228 -.007 .163 .6 easures of Sampling Adequacy(MSA)Communalities Initial Extraction X_1
1.000 .790 X_2 1.000 .484 X_3 1.000 .637 X_4 1.000 .731 X_5 1.000 .600 X_6 1.000 .620 X_7 1.000 .481 X_8 1.000 .569 X_9 1.000 .554 X_10 1.000 .679 X_11 1.000 .681 Extraction Method: Principal
Component Analysis.
Total Variance Explained Comp onent
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.047 27.702 27.702 3.047 27.702 27.702 2.079 18.898 18.898 2 1.463 13.301 41.003 1.463 13.301 41.003 1.685 15.321 34.219 3 1.228 11.161 52.164 1.228 11.161 52.164 1.671 15.187 49.406 4 1.087 9.885 62.049 1.087 9.885 62.049 1.391 12.643 62.049 5 .915 8.314 70.363 6 .873 7.939 78.302 7 .651 5.918 84.220 8 .560 5.091 89.311 9 .505 4.590 93.901 10 .352 3.198 97.099 11 .319 2.901 100.000 Extraction Method: Principal Component Analysis.
Component Matrix
a
Component1
2
3
4 X_1 .525 .445 -.558 .069 X_2 .605 .321 .104 -.065 X_3 .589 .288 .061 -.452 X_4 .456 .117 .077 .710 X_5 .607 -.435 .137 .153 X_6 .586 -.433 -.238 .180 X_7 .463 -.404 .186 -.261 X_8 .422 .217 .482 -.333 X_9 .546 -.493 .112 -.024 X_10 .520 .052 -.604 -.205 X_11 .424 .481 .420 .305 Extraction Method: Principal Component Analysis.
a. 4 components extracted.
a Rotated Component Matrix Component Transformation Matrix
Component Compon ent
1
2
3
4
1
2
3
4 1 .624 .499 .483 .358 X_1 -.064 .133 .835 .268 2 -.776 .440 .294 .344 X_2 .135 .532 .307 .297 3 .087 .516 -.819 .234 X_3 .137 .696 .363 -.053 4 .027 -.539 -.098 .836 X_4 .219 -.064 .122 .815 Extraction Method: Principal Component Analysis. X_5 .732 .100 .038 .228 Rotation Method: Varimax with Kaiser Normalization. X_6 .686 -.118 .333 .155 X_7 .612 .290 -.022 -.148 X_8 .128 .735 -.095 .060 X_9 .732 .127 .029 .032 X_10 .226 .081 .781 -.109 X_11 -.064 .475 -.028 .671 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.
Component Score Coefficient Matrix Component
1
2
3
4 X_1
- .166 -.049 .539 .113 X_2
- .041 .272 .097 .117 X_3
- .039 .433 .151 -.199 X_4 .054 -.210 -.019 .641 X_5 .368 -.050 -.097 .113 X_6
.337 -.224 .148 .060 X_7 .316 .162 -.108 -.206 X_8
- .003 .503 -.181 -.064 X_9 .380 .000 -.085 -.049 X_10 .031 -.051 .514 -.200 X_11
- .131 .239 -.144 .478 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
LAMPIRAN H. PERHITUNGAN ANALISIS FAKTOR MENGGUNAKAN MATRIKS Matriks Korelasi Sederhana
1
2
3
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5
6
7
8
9
10
11 1 1,000 0,274 0,342 0,262 0,044 0,209 -0,022 0,073 0,126 0,488 0,229 2 0,274 1,000 0,437 0,240 0,300 0,229 0,231 0,211 -0,009 0,194 0,281 3 0,342 0,437 1,000 0,013 0,194 0,175 0,152 0,277 0,259 0,199 0,244 4 0,262 0,240 0,013 1,000 0,239 0,190 0,121 0,111 0,170 0,119 0,302
∑ =
5 0,044 0,300 0,194 0,239 1,000 0,442 0,288 0,135 0,392 0,192 0,125 6 0,209 0,229 0,175 0,190 0,442 1,000 0,217 0,008 0,413 0,269 0,058 7 -0,022 0,231 0,152 0,121 0,288 0,217 1,000 0,183 0,312 0,222 0,024 8 0,073 0,211 0,277 0,111 0,135 0,008 0,183 1,000 0,206 0,151 0,286 9 0,126 -0,009 0,259 0,170 0,392 0,413 0,312 0,206 1,000 0,143 0,098 10 0,488 0,194 0,199 0,119 0,192 0,269 0,222 0,151 0,143 1,000 0,002
11 0,229 0,281 0,244 0,302 0,125 0,058 0,024 0,286 0,098 0,002 1,000 Dengan bantuan software MATLAB (Matrix Laboratory), didapat nilai karakteristik (eigen value) dan vektor karakteristik (eigen vector) dari matrik korelasi sederhana ( .
Matriks Eigen Value
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2
3
4
5
6
7
8
9
10
11 1 3,047 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 2 0,000 1,463 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 3 0,000 0,000 1,228 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 4 0,000 0,000 0,000 1,087 0,000 0,000 0,000 0,000 0,000 0,000 0,000
L =
5 0,000 0,000 0,000 0,000 0,915 0,000 0,000 0,000 0,000 0,000 0,000 6 0,000 0,000 0,000 0,000 0,000 0,873 0,000 0,000 0,000 0,000 0,000 7 0,000 0,000 0,000 0,000 0,000 0,000 0,651 0,000 0,000 0,000 0,000 8 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,560 0,000 0,000 0,000 9 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,505 0,000 0,000 10 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,352 0,000
11 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,319
Matriks Eigen Vector
1
2
3
4
5
6
7
8
9
10
11 1 0,301 0,368 -0,504 0,066 -0,197 0,072 -0,166 0,040 0,047 -0,528 -0,402 2 0,346 0,266 0,094 -0,062 0,640 -0,080 0,050 0,116 -0,189 -0,341 0,462 3 0,337 0,238 0,055 -0,433 0,120 0,420 -0,268 0,275 0,111 0,509 -0,171 4 0,261 0,097 0,069 0,680 -0,087 -0,312 -0,142 0,478 -0,073 0,310 -0,013
V =
5 0,348 -0,359 0,124 0,147 0,243 0,094 0,433 0,045 0,610 -0,105 -0,267 6 0,336 -0,358 -0,215 0,172 0,116 0,292 0,177 -0,272 -0,647 0,167 -0,180 7 0,265 -0,334 0,168 -0,251 0,117 -0,566 -0,519 -0,227 -0,020 -0,048 -0,265 8 0,242 0,180 0,435 -0,320 -0,417 -0,224 0,490 0,166 -0,295 -0,104 -0,170 9 0,313 -0,407 0,101 -0,023 -0,466 0,306 -0,271 0,158 0,057 -0,264 0,493 10 0,298 0,043 -0,545 -0,197 -0,179 -0,370 0,259 -0,193 0,197 0,346 0,376
11 0,243 0,398 0,379 0,293 -0,143 0,140 -0,096 -0,682 0,156 0,091 0,085 Matriks loading factor ( ) diperoleh dengan mengalikan matriks eigen vector dengan akar dari matriks eigen value. Atau dalam persamaan matematis ditulis
√ .
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2
3
4
5
6
7
8
9
10
11 1 1,746 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 2 0,000 1,210 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 3 0,000 0,000 1,108 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 4 0,000 0,000 0,000 1,043 0,000 0,000 0,000 0,000 0,000 0,000 0,000
√ . =
5 0,000 0,000 0,000 0,000 0,956 0,000 0,000 0,000 0,000 0,000 0,000 6 0,000 0,000 0,000 0,000 0,000 0,934 0,000 0,000 0,000 0,000 0,000 7 0,000 0,000 0,000 0,000 0,000 0,000 0,807 0,000 0,000 0,000 0,000 8 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,748 0,000 0,000 0,000 9 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,711 0,000 0,000 10 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,593 0,000
11 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,565
Matriks Factor Loading
1
2
3
4
5
6
7
8
9
10
11 1 0,525 0,445 -0,558 0,069 -0,189 0,067 -0,134 0,030 0,033 -0,313 -0,227 2 0,605 0,322 0,104 -0,065 0,612 -0,075 0,041 0,087 -0,134 -0,202 0,261 3 0,589 0,288 0,061 -0,451 0,115 0,392 -0,216 0,205 0,079 0,302 -0,097 4 0,456 0,117 0,077 0,710 -0,083 -0,292 -0,115 0,357 -0,052 0,184 -0,007
=
5 0,607 -0,435 0,137 0,153 0,233 0,088 0,350 0,034 0,434 -0,062 -0,151 6 0,586 -0,433 -0,238 0,180 0,111 0,273 0,143 -0,204 -0,460 0,099 -0,101 7 0,463 -0,404 0,186 -0,262 0,112 -0,529 -0,418 -0,170 -0,014 -0,028 -0,150 8 0,422 0,217 0,482 -0,333 -0,399 -0,209 0,395 0,124 -0,209 -0,062 -0,096 9 0,546 -0,493 0,112 -0,024 -0,446 0,286 -0,219 0,118 0,040 -0,156 0,279 10 0,520 0,052 -0,604 -0,205 -0,171 -0,346 0,209 -0,144 0,140 0,205 0,213
11 0,424 0,481 0,420 0,305 -0,137 0,130 -0,078 -0,510 0,111 0,054 0,048 Keterangan : Angka yang dicetak tebal adalah nilai factor loading yang memiliki eigen value lebih besar dari satu.
Matriks Rotated Factor Loading diperoleh dengan mengalikan matriks factor loading dengan matriks transformasi (Component
Transformation Matrix ). Atau dalam persamaan matematis ditulis sebagai : .
0,624 0,499 0,483 0,358
- 0,776 0,440 0,294 0,344
R =
0,087 0,516 -0,819 0,234 0,027 -0,539 -0,098 0,836
0,525 0,445 -0,558 0,069
- 0,065 0,133 0,835 0,268 0,605 0,322 0,104 -0,065
- 0,776 0,440 0,294 0,344
0,135 0,532 0,307 0,297 0,589 0,288 0,061 -0,451
0,137 0,695 0,363 -0,053 0,456 0,117 0,077 0,710 0,624 0,499 0,483 0,358 0,219 -0,064 0,122 0,815 0,607 -0,435 0,137 0,153 x
=
0,732 0,100 0,038 0,228 0,586 -0,433 -0,238 0,180 0,087 0,516 -0,819 0,234 0,686 -0,118 0,333 0,155 0,463 -0,404 0,186 -0,262 0,027 -0,539 -0,098 0,836 0,612 0,290 -0,022 -0,149 0,422 0,217 0,482 -0,333
0,128 0,735 -0,094 0,060 0,546 -0,493 0,112 -0,024
0,732 0,127 0,029 0,032 0,520 0,052 -0,604 -0,205
0,226 0,081 0,781 -0,109 0,424 0,481 0,420 0,305
- 0,064 0,475 -0,027 0,671
Invers Matriks Korelasi Sederhana
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2
3
4
5
6
7
8
9
10
11 1 1,730 -0,179 -0,397 -0,328 0,302 -0,109 0,306 0,173 -0,130 -0,795 -0,225 2 -0,179 1,650 -0,591 -0,223 -0,333 -0,222 -0,286 -0,122 0,576 0,036 -0,172 3 -0,397 -0,591 1,567 0,347 -0,027 0,047 -0,003 -0,205 -0,377 0,033 -0,134 4 -0,328 -0,223 0,347 1,300 -0,178 -0,038 -0,064 -0,031 -0,141 0,063 -0,291
=
5 0,302 -0,333 -0,027 -0,178 1,520 -0,395 -0,086 0,006 -0,381 -0,169 -0,044 6 -0,109 -0,222 0,047 -0,038 -0,395 1,493 -0,011 0,205 -0,470 -0,195 0,039 7 0,306 -0,286 -0,003 -0,064 -0,086 -0,011 1,294 -0,085 -0,342 -0,292 0,069 8 0,173 -0,122 -0,205 -0,031 0,006 0,205 -0,085 1,248 -0,224 -0,210 -0,291 9 -0,130 0,576 -0,377 -0,141 -0,381 -0,470 -0,342 -0,224 1,623 0,121 -0,010 10 -0,795 0,036 0,033 0,063 -0,169 -0,195 -0,292 -0,210 0,121 1,531 0,230
11 -0,225 -0,172 -0,134 -0,291 -0,044 0,039 0,069 -0,291 -0,010 0,230 1,306
Matriks Koefisien Bobot Faktor
1
2
3
4 1 -0,166 -0,049 0,539 0,113 2 -0,041 0,272 0,097 0,117 3 -0,039 0,433 0,151 -0,199 4 0,054 -0,210 -0,019 0,641 5 0,368 -0,050 -0,097 0,113
B =
6 0,337 -0,224 0,148 0,060 7 0,316 0,162 -0,108 -0,206 8 -0,003 0,503 -0,181 -0,064 9 0,380 0,000 -0,085 -0,049
10 0,031 -0,051 0,514 -0,200 11 -0,131 0,239 -0,144 0,478 Untuk menghitung dan , maka diperlukan matriks korelasi sederhana dan matriks korelasi parsial yang semua entrinya telah dikuadratkan. Berikut ini akan disajikan matriks korelasi sederhana dan matriks korelasi parsial yang semua entrinya telah dikuadratkan.
Matriks Korelasi Parsial
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2
3
4
5
6
7
8
9
10
11 1 -0,106 -0,241 -0,219 0,186 -0,068 0,205 0,118 -0,077 -0,489 -0,150 2 -0.,106 -0,368 -0,152 -0,210 -0,142 -0,196 -0,085 0,352 0,022 -0,117 3 -0,241 -0,368 0,243 -0,017 0,030 -0,002 -0,146 -0,237 0,022 -0,094 4 -0,219 -0,152 0,243 -0,127 -0.027 -0,049 -0,025 -0,097 0,045 -0,223
= [ ] =
5 0,186 -0,210 -0,017 -0,127 -0,262 -0,061 0,004 -0,243 -0,111 -0,032 6 -0,068 -0,142 0,030 -0,027 -0,262 -0,008 0,150 -0,302 -0,129 0,028 7 0,205 -0,196 -0,002 -0,049 -0,061 -0,008 -0,066 -0,236 -0,208 0,053 8 0,118 -0,085 -0,146 -0,025 0,004 0,150 -0,066 -0,157 -0,152 -0,228 9 -0,077 0,352 -0,237 -0,097 -0,243 -0,302 -0,236 -0,157 0,077 -0,007 10 -0,489 0,022 0,022 0,045 -0,111 -0,129 -0,208 -0,152 0,077 0,163
11 -0,150 -0,117 -0,094 -0,223 -0,032 0,028 0,053 -0,228 -0,007 0,163
1
2
3
4
5
6
7
8
9
10
11 Jumlah 1 0,011 0,058 0,048 0,035 0,005 0,042 0,014 0,006 0,239 0,023 0,480 2 0,011 0,135 0,023 0,044 0,020 0,038 0,007 0,124 0,000 0,014 0,418 3 0,058 0,135 0,059 0,000 0,001 0,000 0,021 0,056 0,000 0,009 0,341 4 0,048 0,023 0,059 0,016 0,001 0,002 0,001 0,009 0,002 0,050 0,211
= [ ] =