Lampiran 1 Hasil Perhitungan Menggunakan Program SPSS 17.0
Inter-Item Correlation Matrix
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 Y
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha Based on
Standardized
Items N of Items
.734 .822 12
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .601 Bartlett's Test of Sphericity Approx. Chi-Square 78.863
df 55
3
Anti-image Matrices
Communalities
5
Total Variance Explained
Compon ent
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 2.848 25.891 25.891 2.848 25.891 25.891 1.955 17.772 17.772
2 1.434 13.040 38.931 1.434 13.040 38.931 1.810 16.459 34.230
3 1.304 11.853 50.784 1.304 11.853 50.784 1.472 13.380 47.610
4 1.027 9.333 60.116 1.027 9.333 60.116 1.376 12.506 60.116
5 .832 7.562 67.678
6 .811 7.375 75.053
7 .733 6.662 81.715
8 .664 6.033 87.748
9 .603 5.479 93.227
10 .455 4.139 97.366
11 .290 2.634 100.000
Component Matrixa
Component
1 2 3 4
VAR00001 .639 -.006 .237 -.299 VAR00002 .455 .207 -.191 -.694 VAR00003 .554 .492 -.251 -.158 VAR00004 .276 .794 -.169 .286 VAR00005 .444 -.264 .548 -.024 VAR00006 .522 -.507 -.287 -.096 VAR00007 .556 -.284 -.253 .145 VAR00008 .607 -.039 -.079 .477 VAR00009 .403 .266 .600 .172 VAR00010 .517 -.194 -.417 .246 VAR00011 .522 -.048 .365 .017 Extraction Method: Principal Component Analysis.
7
Rotated Component Matrixa
Component
1 2 3 4
VAR00001 .197 .541 .011 .471 VAR00002 .064 .052 .071 .870 VAR00003 .215 .083 .559 .421 VAR00004 .008 .006 .901 .067 VAR00005 .122 .711 -.215 .034 VAR00006 .681 .079 -.289 .261 VAR00007 .668 .133 -.001 .106 VAR00008 .616 .315 .321 -.146 VAR00009 -.102 .720 .299 -.056 VAR00010 .719 -.020 .133 .057 VAR00011 .183 .600 .041 .115 Extraction Method: Principal Component Analysis.
9
Lampiran 3 Hasil Penskalaan Data
Succesive
Interval
4 5 4 4 4 4 4 5 4 4 4
3.523 3.393 3.815 2.168 2.495 3.509 3.480 4.866 2.548 3.687 3.855
3.523 2.196 5.105 3.346 3.961 3.509 3.480 4.866 4.173 2.515 3.855
3.523 2.196 3.815 3.346 2.495 3.509 2.281 4.866 2.548 3.687 2.697
3.523 2.196 3.815 2.168 2.495 2.316 2.281 2.793 2.548 3.687 2.697
3.523 2.196 2.528 2.168 2.495 4.746 3.480 2.793 2.548 3.687 2.697
2.128 2.196 3.815 1.000 3.961 4.746 3.480 2.793 2.548 2.515 3.855
3.523 3.393 3.815 3.346 1.000 3.509 4.746 2.793 1.000 3.687 2.697
3.523 3.393 3.815 1.000 3.961 3.509 3.480 4.866 2.548 2.515 2.697
3.523 2.196 3.815 3.346 2.495 2.316 3.480 2.793 2.548 3.687 4.866
3.523 3.393 3.815 2.168 2.495 4.746 2.281 2.793 2.548 4.937 3.855
2.128 2.196 2.528 2.168 3.961 3.509 3.480 2.793 2.548 4.937 3.855
2.128 2.196 2.528 1.000 2.495 3.509 2.281 2.793 2.548 3.687 3.855
3.523 3.393 3.815 3.346 2.495 3.509 4.746 2.793 1.000 2.515 2.697
3.523 2.196 3.815 1.000 3.961 3.509 2.281 2.793 1.000 2.515 3.855
2.128 2.196 3.815 3.346 2.495 3.509 4.746 2.793 1.000 3.687 2.697
3.523 3.393 3.815 3.346 3.961 3.509 2.281 4.866 2.548 3.687 3.855
3.523 2.196 3.815 2.168 2.495 2.316 3.480 2.793 2.548 3.687 4.866
3.523 2.196 2.528 1.000 2.495 4.746 3.480 2.793 2.548 2.515 2.697
5.210 2.196 2.528 2.168 3.961 2.316 3.480 2.793 2.548 2.515 4.866
1.539 1.000 2.528 1.000 1.000 1.000 1.000 1.000 1.000 1.539 2.697
5.210 3.393 5.105 3.346 2.495 3.509 2.281 2.793 4.173 2.515 4.866
3.523 2.196 5.105 2.168 2.495 3.509 3.480 3.945 2.548 3.687 2.697
3.523 1.000 2.528 2.168 2.495 3.509 2.281 2.793 2.548 2.515 2.697
3.523 3.393 2.528 2.168 3.961 2.316 2.281 2.793 2.548 2.515 3.855
3.523 1.000 3.815 3.346 2.495 2.316 3.480 4.866 1.000 3.687 2.697
3
3.523 2.196 3.815 2.168 3.961 2.316 2.281 2.793 2.548 2.515 2.697
3.523 1.000 2.528 1.000 2.495 3.509 3.480 2.793 2.548 2.515 2.697
5.210 3.393 2.528 1.000 2.495 2.316 3.480 2.793 2.548 3.687 2.697
3.523 3.393 2.528 2.168 2.495 2.316 2.281 2.793 2.548 4.937 2.697
3.523 1.000 2.528 3.346 2.495 2.316 2.281 3.945 2.548 3.687 2.697
3.523 2.196 3.815 1.000 3.961 3.509 3.480 3.945 1.000 4.937 2.697
3.523 1.000 3.815 3.346 2.495 2.316 2.281 3.945 2.548 3.687 4.866
3.523 1.000 2.528 2.168 2.495 2.316 3.480 2.793 2.548 2.515 3.855
3.523 1.000 2.528 2.168 2.495 3.509 2.281 2.793 2.548 3.687 2.697
3.523 1.000 2.528 2.168 2.495 4.746 4.746 3.945 2.548 4.937 3.855
2.128 2.196 3.815 3.346 2.495 2.316 3.480 2.793 2.548 2.515 3.855
3.523 1.000 2.528 1.000 2.495 3.509 3.480 4.866 2.548 3.687 4.866
3.523 2.196 2.528 2.168 3.961 2.316 3.480 3.945 2.548 4.937 3.855
1.000 1.000 1.000 1.000 1.000 1.000 1.000 2.793 1.000 1.000 1.000
5.210 2.196 3.815 1.000 3.961 3.509 4.746 4.866 2.548 3.687 3.855
3.523 3.393 2.528 2.168 2.495 2.316 4.746 3.945 4.173 3.687 2.697
2.128 1.000 3.815 3.346 2.495 2.316 3.480 3.945 4.173 3.687 2.697
3.523 3.393 5.105 2.168 1.000 3.509 2.281 3.945 1.000 4.937 2.697
3.523 3.393 3.815 2.168 1.000 3.509 3.480 3.945 2.548 3.687 4.866
3.523 1.000 3.815 1.000 2.495 4.746 3.480 3.945 1.000 3.687 4.866