CONTOH DISERTASI / TESIS
PENGARUH BAURAN PEMASARAN (MIX MARKETING) TERHADAP EKUITAS
MERK (BRANDING EQUITY)
STUDI ANALISIS JALUR DENGAN LISREL
Gambar 7.7
The 8-factor path analysis of the marketing mix
Contoh hipotesis yang diajukan
a.
Terdapat pengaruh langsung positif sponsorship terhadap public relation
management
b.
Terdapat pengaruh langsung positif sponsorship terhadap placement management
c.
Terdapat pengaruh langsung positif promotion management terhadap management
process
d.
Terdapat pengaruh langsung positif management promotion terhadap placement
management
Sponsorship
X1
Promotion
X2
Pricing
X3
Power
X4
Processm
X5
Brand
X6
Public Rel
X7
Place
X8
r12
e4
e3
e1
r25
r56
r75
r76
r78
r48
r47
r28
r37
r36
r17
r18
r23
r34
r13
r24
r14
r45
e.
Terdapat pengaruh langsung positif pricing management terhadap brand
mangement
f.
Terdapat pengaruh langsung positif pricing management terhadap public relation
management
g.
Terdapat pengaruh langsung positif power of the market terhadap management
process
h.
Terdapat pengaruh langsung positif power of the market terhadap public relation
management
i.
Terdapat pengaruh langsung positif power of the market terhadap placement
management
j.
Terdapat pengaruh langsung positif management process terhadap brand
management
k.
Terdapat pengaruh langsung positif public relation management terhadap brand
management
l.
Terdapat pengaruh langsung positif public relation management terhadap
management process
m.
Terdapat pengaruh langsung positif public relation management terhadap placement
management
n.
Terdapat pengaruh tidak langsung sponsorship terhadap management process
melalui public relation management
o.
Terdapat pengaruh tidak langsung sponsorship terhadap brand management
melalui public relation mangement
p.
Terdapat pengaruh tidak langsung positif sponsorship terhadap place management
melalui public relation mangement
q.
Terdapat pengaruh tidak langsung positif promotion management terhadap brand
management melalui process mangement
r.
Hipotesis pertama: Terdapat pengaruh tidak langsung positif pricing management
terhadap process management melalui public relation mangement
s.
Terdapat pengaruh tidak langsung positif pricing management terhadap brand
management melalui public relation mangement
t.
Terdapat pengaruh tidak langsung positif pricing management terhadap place
management melalui public relation mangement
u.
Terdapat pengaruh tidak langsung positif power of market management terhadap
v.
Terdapat pengaruh tidak langsung positif power of market management terhadap
brand management melalui public relation mangement dan process management
w.
Terdapat pengaruh tidak langsung positif power of market management terhadap
place management melalui public relation mangement
x.
Terdapat pengaruh tidak langsung public relation management terhadap brand
management melalui process mangement
1.
Siapkan Menu PRELIS Data
1.1 Input data
Untuk menguji contoh hipotesis penelitian di atas, buka menu PRELIS Data pada editor
LISREL kemudian ikuti langkah sebagai berikut:
Klik File
Klik New
Klik PRELIS Data
Klik OK
Klik Data
Klik Define variabel
Klik Insert
Pada dialog box Add variables ketik X1-X8
Klik OK
Pada dialog box define variabel sudah terisi X1 X2 X3 X4 X5 X6 X7 X8 selanjutnya klik
OK
Gambar 7.8 Menu PRELIS Data
Klik Data
Klik Insert cases
Gambar 7.9 Menu input data PRELIS
Terlihat editor PRELIS Data LISREL yang sudah siap diinput, Klik sel yang akan diisi
data sekali lagi data ini hanya untuk ilustrasi saja bukan hasil penelitian yang sebenarnya,
setelah itu input contoh data berikut:
Tabel 7.3 Contoh Data Penelitian
RESP X1 X2 X3 X4 X5 X6 X7 X8
1 186 113 119 110 164 124 90 109
2 159 72 98 92 188 69 132 102
3 220 162 154 155 178 118 125 145 4 144 105 61 61 225 102 104 113
5 163 121 84 87 202 100 75 123
6 153 96 117 88 149 159 120 151 7 218 157 152 150 193 93 135 94 8 197 136 137 128 158 119 158 105
9 173 103 69 99 223 154 91 91
10 177 104 114 107 191 160 164 150 11 188 129 129 118 229 107 67 83 12 157 110 105 102 163 133 143 82 13 183 127 126 114 182 126 160 56 14 224 163 158 156 168 110 111 97 15 158 122 85 96 162 101 123 87
16 129 95 72 102 134 92 78 97
17 148 118 115 94 153 115 121 89 18 142 124 123 112 147 121 129 107 19 196 135 135 127 201 132 141 122 20 166 100 116 73 171 97 122 68
21 184 66 111 87 189 63 117 82
22 137 96 115 44 142 93 121 39
23 145 114 118 109 150 111 124 104 24 162 124 82 100 167 121 88 95 25 164 106 117 101 169 103 123 96 26 217 152 152 145 222 149 158 140
27 171 59 89 107 176 56 95 102
28 195 135 135 127 200 132 141 122 29 176 115 104 76 181 112 110 71 30 201 140 139 134 206 137 145 129 31 169 101 107 93 174 98 113 88
32 162 91 101 97 167 88 107 92
35 168 110 71 42 173 107 77 37 36 197 137 137 129 202 134 143 124
37 178 95 99 90 183 92 105 85
38 204 143 142 136 209 140 148 131 39 214 151 149 145 219 148 155 140 40 167 116 100 105 172 113 106 100 41 229 166 164 158 234 163 170 153 42 176 103 120 110 181 100 126 105
43 181 103 84 53 186 100 90 48
44 195 134 134 126 200 131 140 121
45 136 83 108 79 141 80 114 74
46 161 64 93 108 166 61 99 103
47 163 98 113 72 168 95 119 67
48 138 113 66 58 143 110 72 53
49 211 150 148 144 216 147 154 139
50 176 120 78 97 181 117 84 92
51 175 110 91 90 180 107 97 85
52 192 133 132 120 197 130 138 115 53 171 119 114 104 176 116 120 99 54 170 111 86 106 175 108 92 101
55 143 106 76 91 148 103 82 86
56 190 128 127 116 195 125 133 111
57 174 67 100 79 179 64 106 74
58 193 133 132 122 198 130 138 117 59 184 99 122 112 189 96 128 107
60 185 77 107 86 190 74 113 81
61 169 57 71 95 174 54 77 90
62 181 120 109 89 186 117 115 84 63 136 106 108 103 141 103 114 98
64 174 114 86 54 179 111 92 49
65 177 121 88 91 182 118 94 86
66 209 144 145 142 214 141 151 137 67 141 105 116 58 146 102 122 53
68 185 101 92 79 190 98 98 74
69 179 113 121 111 184 110 127 106 70 198 139 138 129 203 136 144 124 71 179 112 64 103 184 109 70 98 72 175 130 131 119 180 127 137 114
73 143 104 83 90 148 101 89 85
74 167 125 125 113 172 122 131 108 75 178 106 111 85 183 103 117 80
76 185 94 67 79 190 91 73 74
77 131 97 94 95 136 94 100 90
78 175 119 121 111 180 116 127 106 79 148 100 119 110 153 97 125 105 80 230 164 166 159 235 161 172 154
81 127 70 97 89 132 67 103 84
82 181 80 106 91 186 77 112 86
83 142 126 125 114 147 123 131 109 84 149 113 74 106 154 110 80 101
85 158 107 85 89 163 104 91 84
86 158 115 101 84 163 112 107 79 87 202 141 140 134 207 138 146 129 88 168 124 124 113 173 121 130 108
90 173 87 112 99 178 84 118 94 91 149 98 102 104 154 95 108 99 92 180 108 113 100 185 105 119 95
93 146 79 95 86 151 76 101 81
94 160 72 91 65 165 69 97 60
95 113 125 124 113 118 122 130 108
96 180 112 76 74 185 109 82 69
97 146 107 88 68 151 104 94 63
98 208 143 143 138 213 140 149 133 99 150 60 98 105 155 57 104 100 100 145 101 109 60 150 98 115 55 101 166 107 110 101 171 104 116 96 102 212 150 148 144 217 147 154 139
103 182 76 87 75 187 73 93 70
104 125 102 120 111 130 99 126 106
105 160 102 60 62 165 99 66 57
106 210 148 146 143 215 145 152 138 107 134 121 115 88 139 118 121 83
108 122 90 77 96 127 87 83 91
109 194 134 133 124 199 131 139 119 110 143 113 94 105 148 110 100 100 111 157 84 110 94 162 81 116 89 112 182 91 63 106 187 88 69 101
113 189 99 93 78 194 96 99 73
114 144 78 102 97 149 75 108 92 115 184 109 112 65 189 106 118 60
116 161 67 77 81 166 64 83 76
117 190 132 131 120 195 129 137 115 118 150 94 123 112 155 91 129 107 119 172 123 104 73 177 120 110 68 120 186 108 103 51 191 105 109 46 121 165 108 83 88 170 105 89 83 122 164 111 118 109 169 108 124 104 123 172 93 90 108 177 90 96 103 124 132 128 128 117 137 125 134 112
Gambar 7.10 Menu Save As PRELIS
1.2 Analisis Deskripsi Data dan Normalitas Data
Setelah file MARKET.PSF terbuka selanjutnya untuk mengetahui deskripsi atau gambaran data
seperti normalitas data baik secara univariat maupun multivariat, histogram masing-masing
variabel, matrik korelasional, rerata (mean), dan simpangan baku antar variabel, dengan mudah
dapat dianalisis melalui menu PRELIS Data LISREL. Namun sebelum dianalisis, definisikan
terlebih dahulu jenis data yang akan dipakai, ini penting karena LISREL akan memperlakukan
variabel kategorikal yang terdistribusi secara normal dapat dianggap sebagai jenis data kontinyu.
Untuk itu ikuti langkah-langkah sebagai berikut:
Klik Data, pada editor PRELIS
Klik Define Variables
Pada kotak Define variables sudah berisi variabel X1 sd X8
Dengan menekan Ctrl (jangan dilepas) lalu klik X1 sd X8 terlihat berwarna biru
Lepas Ctrl, lalu klik Variable Type
Tampak beberapa pilihan tipe variabel, lalu klik Continous, klik OK
Setelah tipe variabel ditentukan langkah berikutnya menganalisis estimasi deskripsi
masing-masing variabel menggunakan menu statistik pada PRELIS LISREL. Langkah-langkahnya
sebagai berikut:
Klik menu Statistics
Gambar 7.11 Menu Output Option PRELIS
Klik kotak pada LISREL system data
Klik kotak di bawah Moment Matrix pilh Correlations,
klik kotak Save the transformed data to file,
lalu ketikkan nama File misalnya DESKRIP,
klik kotak pada Perform tests of multivariate normality, yang lainnya abaikan,
akhiri dengan klik OK
Hasil output LISREL dapat dilihat sebagai berikut:
DATE: 03/04/2012TIME: 19:09
LISREL 8.80 (STUDENT EDITION) BY
Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Chicago, IL 60646-1704, U.S.A.
Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-99 Use of this program is subject to the terms specified in the Universal Copyright Convention.
Website: www.ssicentral.com The following lines were read from file D:\MARKET.PR2: !PRELIS SYNTAX: Can be edited
SY=D:\MARKET.PSF
OU MA=KM RA=MARKET.PR2 ME= SD= DESKRIP Total Sample Size = 124
Univariate Summary Statistics for Continuous Variables
Test of Univariate Normality for Continuous Variables
Skewness Kurtosis Skewness and Kurtosis Variable Z-Score P-Value Z-Score P-Value Chi-Square P-Value X1 0.573 0.566 -0.564 0.573 0.647 0.724 X2 -0.052 0.958 0.017 0.986 0.003 0.998 X3 0.255 0.799 -1.714 0.087 3.002 0.223 X4 0.207 0.836 -0.029 0.977 0.044 0.978 X5 0.573 0.566 -0.564 0.573 0.647 0.724 X6 -0.052 0.958 0.017 0.986 0.003 0.998 X7 0.255 0.799 -1.714 0.087 3.002 0.223 X8 0.207 0.836 -0.029 0.977 0.044 0.978 Relative Multivariate Kurtosis = 2.538
Test of Multivariate Normality for Continuous Variables
Skewness Kurtosis Skewness and Kurtosis Value Z-Score P-Value Value Z-Score P-Value Chi-Square P-Value --- --- --- --- --- --- --- --- 63.623 28.419 0.000 124.317 13.037 0.000 977.577 0.000 Histograms for Continuous Variables
X1
Frequency Percentage Lower Class Limit 2 1.6 113.000 •• 8 6.5 124.700 ••••••••
16 12.9 136.400 •••••••••••••••• 12 9.7 148.100 ••••••••••••
23 18.5 159.800 ••••••••••••••••••••••• 27 21.8 171.500 ••••••••••••••••••••••••••• 15 12.1 183.200 •••••••••••••••
9 7.3 194.900 ••••••••• 8 6.5 206.600 •••••••• 4 3.2 218.300 ••••
X2
Frequency Percentage Lower Class Limit
7 5.6 57.000 ••••••• 6 4.8 67.900 •••••• 5 4.0 78.800 •••••
18 14.5 89.700 ••••••••••••••••••
28 22.6 100.600 •••••••••••••••••••••••••••• 23 18.5 111.500 ••••••••••••••••••••••• 15 12.1 122.400 •••••••••••••••
12 9.7 133.300 •••••••••••• 5 4.0 144.200 •••••
5 4.0 155.100 ••••• X3
Frequency Percentage Lower Class Limit
8 6.5 60.000 •••••••• 9 7.3 70.600 ••••••••• 16 12.9 81.200 •••••••••••••••• 17 13.7 91.800 ••••••••••••••••• 20 16.1 102.400 •••••••••••••••••••• 20 16.1 113.000 •••••••••••••••••••• 14 11.3 123.600 •••••••••••••• 9 7.3 134.200 ••••••••• 8 6.5 144.800 •••••••• 3 2.4 155.400 ••• X4
8 6.5 65.400 •••••••• 15 12.1 77.100 •••••••••••••••
25 20.2 88.800 ••••••••••••••••••••••••• 29 23.4 100.500 ••••••••••••••••••••••••••••• 12 9.7 112.200 ••••••••••••
9 7.3 123.900 ••••••••• 8 6.5 135.600 •••••••• 5 4.0 147.300 ••••• X5
Frequency Percentage Lower Class Limit 2 1.6 118.000 •• 8 6.5 129.700 ••••••••
16 12.9 141.400 •••••••••••••••• 12 9.7 153.100 ••••••••••••
23 18.5 164.800 ••••••••••••••••••••••• 27 21.8 176.500 ••••••••••••••••••••••••••• 15 12.1 188.200 •••••••••••••••
9 7.3 199.900 ••••••••• 8 6.5 211.600 •••••••• 4 3.2 223.300 •••• X6
Frequency Percentage Lower Class Limit
7 5.6 54.000 ••••••• 6 4.8 64.900 •••••• 5 4.0 75.800 •••••
18 14.5 86.700 ••••••••••••••••••
28 22.6 97.600 •••••••••••••••••••••••••••• 23 18.5 108.500 ••••••••••••••••••••••• 15 12.1 119.400 •••••••••••••••
12 9.7 130.300 •••••••••••• 5 4.0 141.200 •••••
5 4.0 152.100 ••••• X7
Frequency Percentage Lower Class Limit
8 6.5 66.000 •••••••• 9 7.3 76.600 ••••••••• 16 12.9 87.200 •••••••••••••••• 17 13.7 97.800 ••••••••••••••••• 20 16.1 108.400 •••••••••••••••••••• 20 16.1 119.000 •••••••••••••••••••• 14 11.3 129.600 •••••••••••••• 9 7.3 140.200 ••••••••• 8 6.5 150.800 •••••••• 3 2.4 161.400 ••• X8
Frequency Percentage Lower Class Limit 5 4.0 37.000 ••••• 8 6.5 48.700 •••••••• 8 6.5 60.400 •••••••• 15 12.1 72.100 •••••••••••••••
25 20.2 83.800 ••••••••••••••••••••••••• 29 23.4 95.500 ••••••••••••••••••••••••••••• 12 9.7 107.200 ••••••••••••
9 7.3 118.900 ••••••••• 8 6.5 130.600 •••••••• 5 4.0 142.300 ••••• Correlation Matrix
X1 X2 X3 X4 X5 X6 --- --- --- --- --- --- X1 1.000
X2 0.580 1.000
X3 0.556 0.668 1.000
X4 0.542 0.627 0.722 1.000
X5 0.861 0.456 0.369 0.387 1.000
X6 0.458 0.850 0.556 0.510 0.448 1.000 X7 0.444 0.562 0.879 0.619 0.357 0.588 X8 0.415 0.505 0.595 0.864 0.400 0.560 Correlation Matrix
X7 X8 --- --- X7 1.000
X8 0.588 1.000 Means
X1 X2 X3 X4 X5 X6 --- --- --- --- --- --- 171.274 111.234 108.790 100.597 176.274 108.234 Means
X7 X8 --- --- 114.790 95.597 Standard Deviations
X1 X2 X3 X4 X5 X6 --- --- --- --- --- --- 24.457 23.721 24.622 25.621 24.457 23.721 Standard Deviations
X7 X8 --- --- 24.622 25.621
The Problem used 9496 Bytes (= 0.0% of available workspace)
1.3 Diskusi Statistik Deskripsi dan Normalitas Data
1). Hasil uji normalitas univariat variabel X1, X2, X3, X4, X5, X6, X7 dan X8, diperoleh
Z
skewnessdan Z
kurtosisberada diantara -1.96 hingga +1,96. Sebagai contoh kita ambil variabel
X1 Z
skewness= 0,566 dan Z
kurtosis= -0.564 Dengan demikian nilai Z
skewnessdan Z
kurtosisuntuk
variabel X1
berada diantara -1,96 hingga +1,96 sehingga dapat disimpulkan bahwa data
variabel X1 cenderung berdistribusi normal. Demikian juga nilai P
skewnessmaupun P
kurtosisuntuk variabel berturut-
turut 0.566 dan 0.573 lebih besar dari α = 0,05 sehingga dapat
disimpulkan bahwa data variabel X1 cenderung berdistribusi normal.
2). Matrik koefisien korelasi antar variabel semuanya bernilai positif sehingga dapat dilanjutkan
sebagai data input untuk perhitungan koefisien pengaruh dan pengujian hipotesis pada
program SIMPLIS
3). Hasil estimasi ukuran pemusatan data masing-masing variabel seperti mean, simpangan
baku berikut histogramnya didisplaykan dengan cukup jelas.
2. Aplikasi Menu SIMPLIS Project
Untuk menguji contoh hipotesis penelitian di atas, buka menu SIMPLIS pada editor
LISREL dengan langkah sebagai berikut:
Klik File
Klik New
Klik SIMPLIS Project
Klik OK
Pada dialog box Save As pilih drive D:
Ketikkan Nama File (misal MARKET.SPJ)
Klik Save
Ketikkan program, mengikuti langkah-langkah seperti yang sudah dijelaskan pada bab 6
sebagai berikut:
Gambar 7.12 Pemrograman SIMPLIS Project
Untuk menjalankan program SIMPLIS Klik File kemudian Klik Run. Maka LISREL
akan mencetak output lengkap sesuai dengan request yang kita inginkan, seperti di bawah
ini:
DATE: 3/ 4/2012 TIME: 21:34
LISREL 8.80 (STUDENT EDITION) Karl G. Jöreskog & Dag Sörbom
This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Chicago, IL 60646-1704, U.S.A.
Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-99 Use of this program is subject to the terms specified in the Universal Copyright Convention.
Website: www.ssicentral.com
The following lines were read from file D:\MARKET.SPJ: studi marketing
OBSERVED VARIABLES: X1 X2 X3 X4 X5 X6 X7 X8 correlation matrix
1.000
0.580 1.000
0.556 0.668 1.000
0.542 0.627 0.722 1.000
0.458 0.850 0.556 0.510 0.448 1.000
0.444 0.562 0.879 0.619 0.357 0.588 1.000
0.415 0.505 0.595 0.864 0.400 0.560 0.588 1.000
Relationships X5 = X2 X4 X7 X6 = X3 X5 X7 X7 = X1 X3 X4 X8 = X1 X2 X4 X7 sample size 124
Options RS EF SC SS Nd=5 Path Diagram
End of problem Sample Size = 124
studi marketing Correlation Matrix to be Analyzed
X5 X6 X7 X8 X1 X2 --- --- --- --- --- --- X5 1.00000
X6 0.44800 1.00000
X7 0.35700 0.58800 1.00000
X8 0.40000 0.56000 0.58800 1.00000
X1 0.86100 0.45800 0.44400 0.41500 1.00000
X2 0.45600 0.85000 0.56200 0.50500 0.58000 1.00000 X3 0.36900 0.55600 0.87900 0.59500 0.55600 0.66800 X4 0.38700 0.51000 0.61900 0.86400 0.54200 0.62700 Correlation Matrix to be Analyzed
X3 X4 --- --- X3 1.00000
X4 0.72200 1.00000
STUDI MARKETING
Number of Iterations = 5
LISREL Estimates (Maximum Likelihood)
X5 = 0.099319*X7 + 0.32310*X2 + 0.12294*X4, Errorvar.= 0.76963 , R² = 0.23087 (0.10737) (0.10825) (0.11304) (0.099776) 0.92502 2.98481 1.08760 7.71362
X6 = 0.26605*X5 + 0.39844*X7 + 0.10759*X3, Errorvar.= 0.58670 , R² = 0.41450 (0.076341) (0.14735) (0.14941) (0.076060) 3.48507 2.70399 0.72015 7.71362
X7 = - 0.061781*X1 + 0.92384*X3 - 0.014525*X4, Errorvar.= 0.22437 , R² = 0.77563 (0.053875) (0.065437) (0.064721) (0.029087) -1.14674 14.11793 -0.22443 7.71362
X8 = 0.11733*X7 - 0.071024*X1 - 0.066031*X2 + 0.87127*X4, Errorvar.= 0.24105 , R² = 0.7589 (0.060201) (0.057677) (0.064355) (0.065102) (0.031250) 1.94892 -1.23142 -1.02604 13.38307 7.71362
Correlation Matrix of Independent Variables X1 X2 X3 X4 --- --- --- --- X1 1.00000
(0.12964) 7.71362
X2 0.58000 1.00000 (0.10597) (0.12964) 5.47310 7.71362
X3 0.55600 0.66800 1.00000 (0.10489) (0.11024) (0.12964) 5.30098 6.05944 7.71362
(0.10427) (0.10820) (0.11307) (0.12964) 5.19811 5.79489 6.38566 7.71362
Goodness of Fit Statistics Degrees of Freedom = 9
Minimum Fit Function Chi-Square = 349.29862 (P = 0.0)
Normal Theory Weighted Least Squares Chi-Square = 204.46422 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 195.46422
90 Percent Confidence Interval for NCP = (152.60125 ; 245.75765)
Minimum Fit Function Value = 2.83983
Population Discrepancy Function Value (F0) = 1.64256 90 Percent Confidence Interval for F0 = (1.28236 ; 2.06519) Root Mean Square Error of Approximation (RMSEA) = 0.42721 90 Percent Confidence Interval for RMSEA = (0.37747 ; 0.47903) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00000
Expected Cross-Validation Index (ECVI) = 2.17197 90 Percent Confidence Interval for ECVI = (1.81178 ; 2.59460) ECVI for Saturated Model = 0.60504
ECVI for Independence Model = 8.89990
Chi-Square for Independence Model with 28 Degrees of Freedom = 1043.08831 Independence AIC = 1059.08831
Model AIC = 258.46422 Saturated AIC = 72.00000 Independence CAIC = 1089.65056 Model CAIC = 361.61183 Saturated CAIC = 209.53014
Root Mean Square Residual (RMR) = 0.12525 Standardized RMR = 0.12517
Goodness of Fit Index (GFI) = 0.70643 Adjusted Goodness of Fit Index (AGFI) = -0.17430 Parsimony Goodness of Fit Index (PGFI) = 0.17661
Normed Fit Index (NFI) = 0.66513 Non-Normed Fit Index (NNFI) = -0.04297 Parsimony Normed Fit Index (PNFI) = 0.21379 Comparative Fit Index (CFI) = 0.66476 Incremental Fit Index (IFI) = 0.67092 Relative Fit Index (RFI) = -0.04182 Critical N (CN) = 8.62953
Studi Marketing
Fitted Covariance Matrix
X5 X6 X7 X8 X1 X2 --- --- --- --- --- --- X5 1.00065
X6 0.45195 1.00205
X7 0.36029 0.58888 1.00000
X8 0.32810 0.39109 0.58733 0.99984
X1 0.29813 0.31605 0.44400 0.41500 1.00000
X2 0.45701 0.42145 0.57218 0.50619 0.58000 1.00000 X3 0.39189 0.56209 0.87900 0.64859 0.55600 0.66800 X4 0.38700 0.42728 0.61900 0.86400 0.54200 0.62700 Fitted Covariance Matrix
X3 X4 --- --- X3 1.00000
X4 0.72200 1.00000 Fitted Residuals
X5 X6 X7 X8 X1 X2 --- --- --- --- --- --- X5 -0.00065
X6 -0.00395 -0.00205
X8 0.07190 0.16891 0.00067 0.00016
X1 0.56287 0.14195 0.00000 0.00000 - -
X2 -0.00101 0.42855 -0.01018 -0.00119 - - 0.00000 X3 -0.02289 -0.00609 0.00000 -0.05359 - - 0.00000 X4 0.00000 0.08272 0.00000 0.00000 - - 0.00000 Fitted Residuals
X3 X4 --- --- X3 0.00000
X4 0.00000 0.00000
Summary Statistics for Fitted Residuals Smallest Fitted Residual = -0.05359 Median Fitted Residual = 0.00000 Largest Fitted Residual = 0.56287 Stemleaf Plot
- 0|521100000000000000000000000000 0|78
1|47 2| 3| 4|3 5|6
Standardized Residuals
X5 X6 X7 X8 X1 X2 --- --- --- --- --- --- X5 -0.34296
X6 -0.71009 -0.71970
X7 -0.34297 -0.34297 - -
X8 1.80892 3.11830 0.34293 0.34317
X1 8.96932 2.36550 - - - - - -
X2 -0.34294 8.30929 -0.34297 -0.34296 - - - - X3 -0.70611 -0.70611 - - -3.00164 - - - - X4 - - 1.72837 - - - - - - - - Standardized Residuals
X3 X4 --- --- X3 - -
X4 - - - -
Summary Statistics for Standardized Residuals Smallest Standardized Residual = -3.00164 Median Standardized Residual = 0.00000 Largest Standardized Residual = 8.96932 studi marketing
Qplot of Standardized Residuals
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. . . . . . . x . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -3.5... -3.5 3.5
Standardized Residuals
The Modification Indices Suggest to Add the
Path to from Decrease in Chi-Square New Estimate X5 X6 65.6 -2.78 X6 X8 8.6 0.27 X7 X8 9.1 0.34 X8 X5 8.1 0.15 X8 X6 14.1 0.21 X5 X1 80.4 0.92 X6 X2 74.9 0.85 X8 X3 9.0 -0.34
X6 X5 65.7 -1.66 X8 X6 10.4 0.11 X1 X5 97.2 0.58 X1 X8 9.0 0.58 X2 X5 100.6 -1.08 X2 X6 85.6 0.43 X2 X1 71.2 -1.47 X2 X2 35.5 2.48 X3 X6 20.8 -0.24 X3 X7 17.5 0.26 X3 X8 10.1 -0.06 X4 X1 49.3 -1.64 X4 X3 10.6 0.07
studi marketing Standardized Solution
BETA
X5 X6 X7 X8 --- --- --- --- X5 - - - - 0.09929 - - X6 0.26587 - - 0.39803 - - X7 - - - - - - - - X8 - - - - 0.11734 - - GAMMA
X1 X2 X3 X4 --- --- --- --- X5 - - 0.32300 - - 0.12290 X6 - - - - 0.10748 - - X7 -0.06178 - - 0.92384 -0.01453 X8 -0.07103 -0.06604 - - 0.87134 Correlation Matrix of Y and X
X5 X6 X7 X8 X1 X2 --- --- --- --- --- --- X5 1.00000
X6 0.45134 1.00000
X7 0.36017 0.58827 1.00000
X8 0.32802 0.39072 0.58737 1.00000
X1 0.29803 0.31573 0.44400 0.41503 1.00000
X2 0.45686 0.42101 0.57218 0.50623 0.58000 1.00000 X3 0.39177 0.56151 0.87900 0.64864 0.55600 0.66800 X4 0.38687 0.42684 0.61900 0.86407 0.54200 0.62700 Correlation Matrix of Y and X
X3 X4 --- --- X3 1.00000
X4 0.72200 1.00000
PSI
Note: This matrix is diagonal.
Studi Marketing
Total and Indirect Effects
Total Effects of X on Y
X1 X2 X3 X4 --- --- --- --- X5 -0.00614 0.32310 0.09175 0.12149 (0.00852) (0.10825) (0.09941) (0.11386) -0.71998 2.98481 0.92304 1.06704
X6 -0.02625 0.08596 0.50010 0.02654 (0.02470) (0.03792) (0.08354) (0.04199) -1.06291 2.26701 5.98626 0.63201
X7 -0.06178 - - 0.92384 -0.01453 (0.05388) (0.06544) (0.06472) -1.14674 14.11793 -0.22443
X8 -0.07827 -0.06603 0.10839 0.86957 (0.05837) (0.06435) (0.05614) (0.06588) -1.34108 -1.02604 1.93061 13.19936
Indirect Effects of X on Y
X1 X2 X3 X4 --- --- --- --- X5 -0.00614 - - 0.09175 -0.00144 (0.00852) (0.09941) (0.00661) -0.71998 0.92304 -0.21810
X6 -0.02625 0.08596 0.39251 0.02654 (0.02470) (0.03792) (0.14136) (0.04199) -1.06291 2.26701 2.77675 0.63201
X7 - - - - - - - -
X8 -0.00725 - - 0.10839 -0.00170 (0.00733) (0.05614) (0.00764) -0.98835 1.93061 -0.22296 Total Effects of Y on Y
X5 X6 X7 X8 --- --- --- --- X5 - - - - 0.09932 - - (0.10737)
0.92502
X6 0.26605 - - 0.42487 - - (0.07634) (0.15002)
3.48507 2.83207
X7 - - - - - - - -
X8 - - - - 0.11733 - - (0.06020)
1.94892
Indirect Effects of Y on Y
X5 X6 X7 X8 --- --- --- --- X5 - - - - - - - -
X6 - - - - 0.02642 - - (0.02956)
0.89406
X7 - - - - - - - -
X8 - - - - - - - -
Standardized Total and Indirect Effects Standardized Total Effects of X on Y
X1 X2 X3 X4 --- --- --- --- X5 -0.00613 0.32300 0.09172 0.12145 X6 -0.02622 0.08587 0.49959 0.02651 X7 -0.06178 - - 0.92384 -0.01453 X8 -0.07828 -0.06604 0.10840 0.86964 Standardized Indirect Effects of X on Y X1 X2 X3 X4 --- --- --- --- X5 -0.00613 - - 0.09172 -0.00144 X6 -0.02622 0.08587 0.39211 0.02651 X7 - - - - - - - - X8 -0.00725 - - 0.10840 -0.00170 Standardized Total Effects of Y on Y
X5 X6 X7 X8 --- --- --- --- X5 - - - - 0.09929 - - X6 0.26587 - - 0.42443 - - X7 - - - - - - - - X8 - - - - 0.11734 - - Standardized Indirect Effects of Y on Y X5 X6 X7 X8 --- --- --- --- X5 - - - - - - - - X6 - - - - 0.02640 - - X7 - - - - - - - - X8 - - - - - - - -
UNTUK LEBIH JELASNYA, PEMBAHASAN DAN INTERPRETASI OUTPUT LISREL
DI ATAS DAPAT DILIHAT PADA BUKU APLIKASI LISREL UNTUK PENELITIAN
ANALISIS JALUR PENERBIT ANDI PUBLISHER YOGYAKARTA 2013
AUTHOR: DR. EDI RIADI
