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A. OUTPUT ANALISIS DATA MENGGUNAKAN ANALISIS KOMPONEN UTAMA MENGGUNAKAN PROSEDUR ROTASI VARIMAX Descriptive Statistics Mean Std. Deviation Analysis N V1 4.32 1.865 25 V2 4.04 1.859 25 V3 3.84 1.818 25 V4 3.92 1.801 25 V5 3.84 1.930 25 V6 4.16 1.841 25 V7 4.20 1.893 25 Correlation Matrix V1 V2 V3 V4 V5 V6 V7 Correlatio n V1 1.000 -.004 .593 .082 .675 -.100 -.338 V2 -.004 1.000 .039 -.248 .048 .582 -.251 V3 .593 .039 1.000 -.259 .396 .020 -.571 V4 .082 -.248 -.259 1.000 .272 .017 .469 V5 .675 .048 .396 .272 1.000 -.110 -.082 V6 -.100 .582 .020 .017 -.110 1.000 .014 V7 -.338 -.251 -.571 .469 -.082 .014 1.000 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .524 Bartlett's Test of Sphericity Approx. Chi-Square 55.816 df 21 Sig. .000
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Analisis Data Menggunakan Analisis Komponen Utama Menggunakan Prosedur Rotasi Varimax & Analisis Faktor Biasa (Jawaban Soal Analasis Faktor)

Jul 27, 2015

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Analisis Data Menggunakan Analisis Komponen Utama Menggunakan Prosedur Rotasi Varimax & Analisis Faktor Biasa (Jawaban Soal Analasis Faktor)
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A. OUTPUT ANALISIS DATA MENGGUNAKAN ANALISIS KOMPONEN UTAMA MENGGUNAKAN PROSEDUR ROTASI VARIMAXDescriptive Statistics Mean V1 V2 V3 V4 V5 V6 V7 4.32 4.04 3.84 3.92 3.84 4.16 4.20 Std. Deviation 1.865 1.859 1.818 1.801 1.930 1.841 1.893 Analysis N 25 25 25 25 25 25 25

Correlation Matrix V1 Correlation V1 V2 V3 V4 V5 V6 V7 1.000 -.004 .593 .082 .675 -.100 -.338 V2 -.004 1.000 .039 -.248 .048 .582 -.251 V3 .593 .039 1.000 -.259 .396 .020 -.571 V4 .082 -.248 -.259 1.000 .272 .017 .469 V5 .675 .048 .396 .272 1.000 -.110 -.082 V6 -.100 .582 .020 .017 -.110 1.000 .014 V7 -.338 -.251 -.571 .469 -.082 .014 1.000

KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square df Sig. 55.816 21 .000 .524

Anti-image Matrices V1 Anti-image Covariance V 1 V 2 V 3 V 4 V 5 V 6 V 7 Anti-image Correlation V 1 V 2 V 3 V 4 V 5 V 6 V 7 a. Measures of Sampling Adequacy(MSA) .185 .301 .432 -.247 -.204 .029 -.665 -.285 -.266 .269 -.526 -.315 -.242 -.324 .520a -.117 .315 .280 .501a -.324 -.333 .297 .599a .280 -.242 .049 .362a .297 .315 -.315 .681a .049 -.333 -.117 -.526 .082 .147 .197 -.133 -.094 .014 -.340 -.136 -.150 .130 -.219 -.144 -.103 -.163 .432 -.056 .168 .139 .584 -.163 -.136 .134 .420 .139 -.103 .021 .484 .134 .168 -.144 .399 V2 .021 V3 -.136 V4 -.056 V5 -.219

Anti-image Matrices V6 Anti-image Covariance V1 V2 V3 V4 V5 V6 V7 Anti-image Correlation V1 V2 V3 V4 V5 V6 V7 a. Measures of Sampling Adequacy(MSA) .014 -.340 -.136 -.150 .130 .540 -.108 .029 -.665 -.285 -.266 .269 .337a -.210 V7 .082 .147 .197 -.133 -.094 -.108 .495 .185 .301 .432 -.247 -.204 -.210 .615a

Communalities Initial V1 V2 V3 V4 V5 V6 V7 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Extraction .830 .811 .753 .798 .809 .818 .772

Extraction Method: Principal Component Analysis.

Total Variance Explained Compo nent 1 2 3 4 5 6 7 Total 2.411 1.820 1.360 .539 .404 .271 .195 Initial Eigenvalues % of Variance 34.438 26.002 19.433 7.703 5.767 3.875 2.781 87.576 93.344 97.219 100.000 Cumulative %

Extraction Method: Principal Component Analysis. Total Variance Explained Initial Compo nent 1 2 3 Eigenvalues Cumulative % 34.438 60.440 79.873 Extraction Sums of Squared Loadings Total 2.411 1.820 1.360 % of Variance 34.438 26.002 19.433 Cumulative % 34.438 60.440 79.873

Extraction Method: Principal Component Analysis. Total Variance Explained Compo nent 1 2 3 Rotation Sums of Squared Loadings Total 2.177 1.828 1.586 % of Variance 31.102 26.120 22.650 Cumulative % 31.102 57.223 79.873

Extraction Method: Principal Component Analysis. Component Matrixa Component 1 V1 V2 V3 V4 V5 V6 V7 .818 .205 .859 -.258 .633 -.021 -.703 2 .361 -.716 -.030 .643 .493 -.600 .400 3 .176 .505 -.120 .564 .406 .677 .344

Extraction Method: Principal Component Analysis.

Total Variance Explained Compo nent 1 2 3 Rotation Sums of Squared Loadings Total 2.177 1.828 1.586 % of Variance 31.102 26.120 22.650 Cumulative % 31.102 57.223 79.873

Extraction Method: Principal Component Analysis. Component Matrixa Component 1 V1 V2 V3 V4 V5 V6 V7 .818 .205 .859 -.258 .633 -.021 -.703 2 .361 -.716 -.030 .643 .493 -.600 .400 3 .176 .505 -.120 .564 .406 .677 .344

a. 3 components extracted.

Reproduced Correlations V1 V 1 V 2 V 3 Reproduced Correlation V 4 V 5 V 6 V 7 V 1 V 2 V 3 Residualb V 4 V 5 V 6 V 7 -.002 V2 V3 V4 V5 V6 V7

.830a -.002 .671 .120 .767 -.114 -.370

-.002 .811a .137 -.228 -.018 .767 -.257

.671 .137 .753a -.309 .479 -.081 -.658

.120 -.228 -.309 .798a .383 .002 .632

.767 -.018 .479 .383 .809a -.034 -.108

-.114 .767 -.081 .002 -.034 .818a .007

-.370 -.257 -.658 .632 -.108 .007 .772a

-.002 -.077 -.038 -.092 .014 .033

-.098 -.020 .067 -.185 .006

-.077 -.098

.051 -.083 .101 .086

-.038 -.020 .051

-.111 .015 -.163

-.092 .067 -.083 -.111

-.076 .026

.014 -.185 .101 .015 -.076

.007

.033 .006 .086 -.163 .026 .007

Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 12 (57,0%) nonredundant residuals with absolute values greater than 0.05.

Rotated Component Matrixa Component 1 V1 V2 V3 V4 V5 V6 V7 .899 .020 .660 .260 .881 -.059 -.292 2 -.136 -.215 -.563 .853 .182 .080 .824 3 -.056 .874 .003 -.057 .003 .899 -.085

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 4 iterations.

Component Transformation Matrix Compo nent 1 2 3 1 .831 .445 .333 2 -.550 .572 .609 3 .080 -.689 .720

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Component Score Coefficient Matrix Component 1 V1 V2 V3 V4 V5 V6 V7 .413 .019 .259 .206 .438 .012 -.061 2 .005 -.046 -.259 .513 .193 .119 .440 3 -.016 .546 -.024 .047 .049 .585 .007

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores.

Component Score Covariance Matrix Compo nent 1 2 3 1 1.000 .000 .000 2 .000 1.000 .000 3 .000 .000 1.000

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores.

SAVE OUTFILE='C:\Users\Rizky\Documents\analisisfaktor1.sav'

/COMPRESSED.

B. Penafsiran Faktor hasil EkstraksiRotated Component Matrixa

Component 1 V1 V2 V3 V4 V5 V6 V7 .899 .020 .660 .260 .881 -.059 -.292 2 -.136 -.215 -.563 .853 .182 .080 .824 3 -.056 .874 .003 -.057 .003 .899 -.085

Dalam Matriks Faktor yang dirotasikan sperti tabil di atas, Terdapat 3 Faktor: Faktor 1 mempunyai koefisien yang tinggi bagi variabel sbb: - V1 ( Memilih menghabiskan waktu di rumah pada sebuah sore yang tenang ketimbang pergi ke pesta) - V3 (Majalah lebih menarik daripada film bioskop) - V5 (Saya Suka tinggal di rumah) Faktor 1 diberi label sebagai sebuah Faktor Perilaku rumah tangga Faktor 2 mempunyai koefisien yang tinggi bagi variabel sbb: - V4 (Saya tidak membeli barang yang diiklankan pada billboard) - V7 (Perusahaan-perusahaan menghabiskan uang banyak untuk iklan) - V3 bernilai negatif yang berarti Majalah tidak lebih menarik daripada film Bioskop Faktor 2 diberi label sebagai sebuah Faktor Tanggapan Konsumen Terhadap Iklan Faktor 3 mempunyai koefisien yang tinggi bagi variabel sbb: - V2 (Saya selalu memeriksa harga untuk item-item kecil) - V6 (Saya menabung dan menguangkan kupon) Faktor 3 diberi label sebagai sebuah Faktor Perilaku Berbelanja

C. Skor Faktor Untuk Tiap Responden

Respond en 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

SKOR FAKTOR 1 1,27063 0,66644 0,69949 -1,03466 -0,83198 -0,94522 -1,13044 -1,14860 1,04186 0,27377 0,31451 0,18918 -0,03405 1,39299 -0,82805 -0,54066 -0,69986 1,29527 1,01289 0,45392 -1,58170 -0,91890 -1,43505 1,19458 1,32363

SKOR FAKTOR SKOR FAKTOR 2 0,42361 1,07257 1,29747 -0,25277 -1,04476 0,51597 1,24967 0,68938 -0,60734 0,49392 1,46810 1,61605 -1,73423 0,06971 -0,11752 -1,51811 1,41086 -0,17524 -1,56463 -0,82511 -0,41347 -0,21345 -0,99019 -0,65847 -0,19200

SKOR FAKTOR 3 -0,93574 1,57931 0,36484 -0,98435 -1,68971 1,50449 0,01896 0,55853 -1,03735 0,28437 0,60231 -1,42428 1,30973 -0,91621 -0,07304 1,06032 -0,57420 -0,33131 -1,36810 0,78353 -1,07562 -0,40974 0,74585 1,15116 0,85622

D. Memilih Variabel Pengganti

Rotated Component Matrixa Component 1 V1 V2 V3 V4 V5 V6 V7 .899 .020 .660 .260 .881 -.059 -.292 2 -.136 -.215 -.563 .853 .182 .080 .824 3 -.056 .874 .003 -.057 .003 .899 -.085

V1 dan V5 memiliki muatan yang tinggi atas faktor 1 dan keduanya memiliki nilai yang hampir sama, akan tetapi V5 (Saya Suka tinggal di rumah) dipilih sebaga variabel pengganti karena dianggap dapat mewakili V1 dan V3 Untuk faktor 2 , V7 (perusahaan menghabiskan banyak uang untuk iklan) dipilih sebagai variable pengganti Untuk Faktor 3 , V2 (Saya selalu memeriksa harga untuk item-item kecil)dipilih sebagai variabel pengganti.

E. Uji Kesesuaian Model

Reproduced Correlations V1 V 1 V 2 V 3 Reproduced Correlation V 4 V 5 V 6 V 7 V 1 V 2 V 3 Residualb V 4 V 5 V 6 V 7 -.002 V2 V3 V4 V5 V6 V7

.830a -.002 .671 .120 .767 -.114 -.370

-.002 .811a .137 -.228 -.018 .767 -.257

.671 .137 .753a -.309 .479 -.081 -.658

.120 -.228 -.309 .798a .383 .002 .632

.767 -.018 .479 .383 .809a -.034 -.108

-.114 .767 -.081 .002 -.034 .818a .007

-.370 -.257 -.658 .632 -.108 .007 .772a

-.002 -.077 -.038 -.092 .014 .033

-.098 -.020 .067 -.185 .006

-.077 -.098

.051 -.083 .101 .086

-.038 -.020 .051

-.111 .015 -.163

-.092 .067 -.083 -.111

-.076 .026

.014 -.185 .101 .015 -.076

.007

.033 .006 .086 -.163 .026 .007

Extraction Method: Principal Component Analysis. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 12 (57,0%) nonredundant residuals with absolute values greater than 0.05.

Dari Tabel di atas ada 12 residu (57%) yang nilainya lebih besar dari 0,05. Mengindikasikan bahwa kesesuaian model tidak dapat diterima.

F. ANALISIS FAKTOR BIASAA. [DataSet2] C:\Users\Rizky\Documents\analisisfaktorbiasa1.sav

Descriptive Statistics Mean V1 V2 V3 V4 V5 V6 V7 4.32 4.04 3.84 3.92 3.84 4.16 4.20 Std. Deviation 1.865 1.859 1.818 1.801 1.930 1.841 1.893 Analysis N 25 25 25 25 25 25 25

Correlation Matrix V1 Correlation V1 V2 V3 V4 V5 V6 V7 1.000 -.004 .593 .082 .675 -.100 -.338 V2 -.004 1.000 .039 -.248 .048 .582 -.251 V3 .593 .039 1.000 -.259 .396 .020 -.571 V4 .082 -.248 -.259 1.000 .272 .017 .469 V5 .675 .048 .396 .272 1.000 -.110 -.082 V6 -.100 .582 .020 .017 -.110 1.000 .014 V7 -.338 -.251 -.571 .469 -.082 .014 1.000

KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square df Sig. 55.816 21 .000 .524

Anti-image Matrices V1 Anti-image Covariance V1 V2 V3 V4 V5 V6 V7 Anti-image Correlation V1 V2 V3 V4 V5 V6 V7 a. Measures of Sampling Adequacy(MSA) Anti-image Matrices V6 Anti-image Covariance V1 V2 V3 V4 V5 V6 V7 Anti-image Correlation V1 V2 V3 V4 V5 V6 V7 a. Measures of Sampling Adequacy(MSA) .014 -.340 -.136 -.150 .130 .540 -.108 .029 -.665 -.285 -.266 .269 .337a -.210 V7 .082 .147 .197 -.133 -.094 -.108 .495 .185 .301 .432 -.247 -.204 -.210 .615a .399 .021 -.136 -.056 -.219 .014 .082 .681a .049 -.333 -.117 -.526 .029 .185 V2 .021 .484 .134 .168 -.144 -.340 .147 .049 .362a .297 .315 -.315 -.665 .301 V3 -.136 .134 .420 .139 -.103 -.136 .197 -.333 .297 .599a .280 -.242 -.285 .432 V4 -.056 .168 .139 .584 -.163 -.150 -.133 -.117 .315 .280 .501a -.324 -.266 -.247 V5 -.219 -.144 -.103 -.163 .432 .130 -.094 -.526 -.315 -.242 -.324 .520a .269 -.204

Communalities Initial V1 V2 V3 V4 V5 V6 V7 .601 .516 .580 .416 .568 .460 .505 Extraction .782 .873 .625 .540 .673 .417 .662

Extraction Method: Principal Axis Factoring.

Total Variance Explained Initial Eigenvalues Factor 1 2 3 4 5 6 7 Total 2.411 1.820 1.360 .539 .404 .271 .195 % of Variance 34.438 26.002 19.433 7.703 5.767 3.875 2.781 Cumulative % 34.438 60.440 79.873 87.576 93.344 97.219 100.000 Extraction Sums of Squared Loadings Total 2.100 1.492 .979 % of Variance 29.995 21.317 13.992 Cumulative % 29.995 51.312 65.304

Extraction Method: Principal Axis Factoring. Total Variance Explained Rotation Sums of Squared Loadings Factor 1 2 3 Total 1.863 1.448 1.260 % of Variance 26.615 20.683 18.006 Cumulative % 26.615 47.298 65.304

Extraction Method: Principal Axis Factoring.

Factor Matrixa Factor 1 V1 V2 V3 V4 V5 V6 V7 .807 .209 .773 -.205 .602 -.013 -.634 2 .328 -.763 -.028 .523 .412 -.476 .363 3 .154 .498 -.162 .473 .376 .436 .356

Extraction Method: Principal Axis Factoring. a. Attempted to extract 3 factors. More than 25 iterations required. (Convergence=,011). Extraction was terminated.

Reproduced Correlations V1 Reproduced Correlation V1 V2 V3 V4 V5 V6 V7 Residualb V1 V2 V3 V4 V5 V6 V7 Extraction Method: Principal Axis Factoring. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 5 (23,0%) nonredundant residuals with absolute values greater than 0.05. .001 .004 .003 -.004 .000 .000 -.063 -.041 .050 .005 -.019 -.009 .003 .088 -.013 .002 .057 -.019 -.069 .016 .782a -.005 .590 .079 .679 -.100 -.338 V2 -.005 .873a .102 -.206 -.002 .577 -.232 .001 V3 .590 .102 .625a -.250 .393 -.067 -.559 .004 -.063 V4 .079 -.206 -.250 .540a .270 -.040 .489 .003 -.041 -.009 V5 .679 -.002 .393 .270 .673a -.040 -.098 -.004 .050 .003 .002

Reproduced Correlations V6 Reproduced Correlation V1 V2 V3 V4 V5 V6 V7 Residualb V1 V2 V3 V4 V5 V6 V7 Extraction Method: Principal Axis Factoring. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 5 (23,0%) nonredundant residuals with absolute values greater than 0.05. .024 -.100 .577 -.067 -.040 -.040 .417a -.009 .000 .005 .088 .057 -.069 V7 -.338 -.232 -.559 .489 -.098 -.009 .662a .000 -.019 -.013 -.019 .016 .024

Rotated Factor Matrixa Factor 1 V1 V2 V3 V4 V5 V6 V7 .869 .029 .584 .207 .807 -.060 -.261 2 -.152 -.202 -.532 .701 .147 .032 .766 3 -.067 .912 -.024 -.078 .005 .642 -.077

Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 5 iterations.

Factor Transformation Matrix Factor 1 2 3 1 .842 .416 .343 2 -.532 .543 .650 3 .084 -.730 .679

Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization.

Factor Score Coefficient Matrix Factor 1 V1 V2 V3 V4 V5 V6 V7 .519 .025 .150 .120 .363 .013 -.021 2 -.003 -.040 -.257 .353 .203 .070 .458 3 -.026 .930 .093 .138 -.077 .083 .129

Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization. Factor Scores Method: Regression.

Factor Score Covariance Matrix Factor 1 2 3 1 .862 -.041 -.013 2 -.041 .776 -.047 3 -.013 -.047 .880

Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization. Factor Scores Method: Regression.

F-B. Penafsiran Faktor hasil EkstraksiRotated Factor Matrixa Factor 1 V1 V2 V3 V4 V5 V6 V7 .869 .029 .584 .207 .807 -.060 -.261 2 -.152 -.202 -.532 .701 .147 .032 .766 3 -.067 .912 -.024 -.078 .005 .642 -.077

Dalam Matriks Faktor yang dirotasikan sperti tabil di atas, Terdapat 3 Faktor: Faktor 1 mempunyai koefisien yang tinggi bagi variabel sbb:

-

V1 ( Memilih menghabiskan waktu di rumah pada sebuah sore yang tenang ketimbang pergi ke pesta) - V3 (Majalah lebih menarik daripada film bioskop) - V5 (Saya Suka tinggal di rumah) Faktor 1 diberi label sebagai sebuah Faktor Perilaku rumah tangga Faktor 2 mempunyai koefisien yang tinggi bagi variabel sbb: - V4 (Saya tidak membeli barang yang diiklankan pada billboard) - V7 (Perusahaan-perusahaan menghabiskan uang banyak untuk iklan) - V3 bernilai negatif yang berarti Majalah tidak lebih menarik daripada film Bioskop Faktor 2 diberi label sebagai sebuah Faktor Tanggapan Konsumen Terhadap Iklan Faktor 3 mempunyai koefisien yang tinggi bagi variabel sbb: - V2 (Saya selalu memeriksa harga untuk item-item kecil) - V6 (Saya menabung dan menguangkan kupon) Faktor 3 diberi label sebagai sebuah Faktor Perilaku Berbelanja

F-C.

SKOR FAKTOR UNTUK TIAP RESPONDEN

Respond en 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

SKOR FAKTOR 1 1,04167 0,63713 0,64965 -0,99297 -0,66902 -0,91337 -1,23996 -0,94382 1,05470 0,42465 0,47925 0,02302 -0,01921 1,21414 -0,78470 -0,64597 -0,70807 1,16780 1,00752 0,38148 -1,42668 -0,71267 -1,33281 1,00925 1,29900

SKOR FAKTOR SKOR FAKTOR 2 0,27246 0,85241 1,18467 -0,35712 -0,81730 0,45171 1,07105 0,67387 -0,47721 0,43407 1,31212 1,45281 -1,64455 0,02627 -0,08710 -1,27943 1,14635 -0,15311 -1,37823 -0,77083 -0,17591 0,09974 -0,85216 -0,75793 -0,22661

SKOR FAKTOR 3 -0,76710 1,51904 -0,25177 -1,10148 -1,35636 1,14053 -0,05635 0,59321 -0,67462 -0,44870 1,19886 -0,96789 1,37417 -0,70721 -0,85288 1,22993 -0,67155 -0,23841 -1,45504 0,96095 -0,53973 -0,25615 0,89249 0,55730 0,87873

F- D Memilih Variabel PenggantiRotated Factor Matrixa Rotated Factor Matrixa Factor 1 V1 V2 V3 V4 V5 V6 V7 .869 .029 .584 .207 .807 -.060 -.261 2 -.152 -.202 -.532 .701 .147 .032 .766 3 -.067 .912 -.024 -.078 .005 .642 -.077

V1 dan V5 memiliki muatan yang tinggi atas faktor 1 dan keduanya memiliki nilai yang hampir sama, akan tetapi V5 (Saya Suka tinggal di rumah) dipilih sebaga variabel pengganti karena dianggap dapat mewakili V1 dan V3 Untuk faktor 2 , V7 (perusahaan menghabiskan banyak uang untuk iklan) dipilih sebagai variable pengganti Untuk Faktor 3 , V2 (Saya selalu memeriksa harga untuk item-item kecil)dipilih sebagai variabel pengganti.

F-E. Uji kesesuaian Model

Reproduced Correlations V1 Reproduced Correlation V1 V2 V3 V4 V5 V6 V7 Residualb V1 V2 V3 V4 V5 V6 V7 Extraction Method: Principal Axis Factoring. a. Reproduced communalities b. Residuals are computed between observed and reproduced correlations. There are 5 (23,0%) nonredundant residuals with absolute values greater than 0.05. .001 .004 .003 -.004 .000 .000 -.063 -.041 .050 .005 -.019 -.009 .003 .088 -.013 .002 .057 -.019 -.069 .016 .024 .782a -.005 .590 .079 .679 -.100 -.338 V2 -.005 .873a .102 -.206 -.002 .577 -.232 .001 V3 .590 .102 .625a -.250 .393 -.067 -.559 .004 -.063 V4 .079 -.206 -.250 .540a .270 -.040 .489 .003 -.041 -.009 V5 .679 -.002 .393 .270 .673a -.040 -.098 -.004 .050 .003 .002 V6 -.100 .577 -.067 -.040 -.040 .417a -.009 .000 .005 .088 .057 -.069 V7 -.338 -.232 -.559 .489 -.098 -.009 .662a .000 -.019 -.013 -.019 .016 .024

Dari Tabel di atas ada 5 residu (23%) yang nilainya lebih besar dari 0,05. Mengindikasikan bahwa kesesuaian model dapat diterima.