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.