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88 Lampiran I Kuesioner Penelitian KUESIONER PENELITIAN Dengan Hormat, Terima kasih atas kesediaan Saudara/Saudari untuk berpartisipasi untuk mengisi dan menjawab seluruh pertanyaan yang ada dalam kuesioner ini. Penelitian ini digunakan untuk menyusun skripsi yang berjudul “..PENGARUH CITRA MEREK DAN KUALITAS PRODUK TERHADAP LOYALITAS KONSUMEN MITSUBISHI PAJERO SPORT..”. Untuk itu diharapkan para responden dapat memberikan jawaban yang sebenar-benarnya demi membantu penelitian ini. Atas kesediaannya saya ucapkan terima kasih, semoga penelitian ini bermanfaat bagi kita semua. Hormat Saya, Rinto sudiono

Lampiran I Kuesioner Penelitian KUESIONER PENELITIAN Dengan

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Page 1: Lampiran I Kuesioner Penelitian KUESIONER PENELITIAN Dengan

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Lampiran I

Kuesioner Penelitian

KUESIONER PENELITIAN

Dengan Hormat,

Terima kasih atas kesediaan Saudara/Saudari untuk berpartisipasi untuk

mengisi dan menjawab seluruh pertanyaan yang ada dalam kuesioner ini. Penelitian

ini digunakan untuk menyusun skripsi yang berjudul “..PENGARUH CITRA

MEREK DAN KUALITAS PRODUK TERHADAP LOYALITAS KONSUMEN

MITSUBISHI PAJERO SPORT..”.

Untuk itu diharapkan para responden dapat memberikan jawaban yang

sebenar-benarnya demi membantu penelitian ini. Atas kesediaannya saya ucapkan

terima kasih, semoga penelitian ini bermanfaat bagi kita semua.

Hormat Saya,

Rinto sudiono

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KUESIONER PENELITIAN

I. IDENTITAS RESPONDEN

Responden diharapkan menjawab pertanyaan-pertanyaan berikut dengan mengisi

bagian yang kosong atau memberi tanda (X) pada jawaban yang tersedia.   

  Nama :

Jenis Kelamin : a. Laki – laki

b. Perempuan

Usia : ...... tahun

Jenis pekerjaan : a. Karyawan swasta

b. Pengusaha

` c. Profesional

b. Lainnya ..................................

*apabila mempunyai pekerjaan lainnya mohon diisi pada bagian “ kolom lainnya “

Terima kasih.

PETUNJUK PENGISIAN

Anda diminta untuk memilih salah satu dari beberapa alternatif jawaban yang

tersedia dengan cara memberikan tanda silang (X) . Dalam skala ini tidak ada

penilaian benar atau salah, jawaban yang paling baik adalah yang sesuai dengan diri

anda. Adapun jawaban yang tersedia yaitu :

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1 2 3 4

SS S TS STS

Keterangan:

SS : Sangat Setuju dengan pertanyaan

S : Setuju dengan pertanyaan

TS : Tidak Setuju dengan pertanyaan

STS : Sangat Tidak Setuju dengan pertanyaan

SELAMAT MENGERJAKAN

III . DAFTAR PERTANYAAN

No PERTANYAAN SS S TS STS

1. Merk Mitsubishi mempunyai reputasi yang baik

2. Mitsubishi merupakan merek yang sangat terkenal didunia otomotif

3. Mitsubishi sudah lama Mengikuti ajang balap Rally Dunia.

4. Mitsubishi sangat identik dengan mobil yang kencang larinya.

5. Merk Mitsubishi dikenal diseluruh dunia.

6. Mitsubishi pajero sport terkenal tangguh dimedan off road

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Sumber : Anca.E.Cretu and Roderick J.Brondie “The influence brand image and company reputation where manufactures market to small firms: A customer value perspective”.mei 2005

7. Saya akan merekomendasikan produk mitsubishi pajero sport ke teman saya

8. Saya akan merekomendasikan produk pajero sport ke saudara saya

9. Apabila saya akan mengganti mobil saya akan membeli pajero sport.

10. Saya merasa suka dengan produk mitsubishi pajero sport.

11. Mitsubishi Pajero sport mempunyai purna jual yang bagus (resale value) yang bagus .

12. Mitsubishi pajero sport merupakan produk yang bagus

13. Sales mitsubishi memberikan penjelasan yang baik mengenai informasi produk pajero sport.

14. Mitsubishi pajero sport menggunakan mesin diesel yang irit.

15. Mitsubishi pajero mudah dalam perawatan

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LAMPIRAN II

Data Final Skala pernyataan

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Lampiran 3. Uji validitas instrumen Citra Merek

Correlation Matrixa

CIM1 CIM2 CIM3 CIM4 CIM5 CIM6

Correlation CIM1 1.000 .262 .263 .342 .208 .273

CIM2 .262 1.000 .371 .366 .108 .250

CIM3 .263 .371 1.000 .310 .213 .297

CIM4 .342 .366 .310 1.000 .409 .218

CIM5 .208 .108 .213 .409 1.000 .299

CIM6 .273 .250 .297 .218 .299 1.000

Sig. (1-tailed) CIM1 .012 .011 .001 .037 .009

CIM2 .012 .001 .001 .178 .015

CIM3 .011 .001 .003 .033 .005

CIM4 .001 .001 .003 .000 .030

CIM5 .037 .178 .033 .000 .005

CIM6 .009 .015 .005 .030 .005

a. Determinant = .401

Inverse of Correlation Matrix

CIM1 CIM2 CIM3 CIM4 CIM5 CIM6

CIM1 1.220 -.120 -.125 -.275 -.044 -.193

CIM2 -.120 1.310 -.326 -.362 .150 -.164

CIM3 -.125 -.326 1.281 -.155 -.087 -.205

CIM4 -.275 -.362 -.155 1.463 -.480 .037

CIM5 -.044 .150 -.087 -.480 1.292 -.281

CIM6 -.193 -.164 -.205 .037 -.281 1.230

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KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .729

Bartlett's Test of Sphericity Approx. Chi-Square 65.058

Df 15

Sig. .000

Anti-image Matrices

CIM1 CIM2 CIM3 CIM4 CIM5 CIM6

Anti-image Covariance CIM1 .820 -.075 -.080 -.154 -.028 -.129

CIM2 -.075 .764 -.194 -.189 .089 -.102

CIM3 -.080 -.194 .780 -.083 -.053 -.130

CIM4 -.154 -.189 -.083 .683 -.254 .020

CIM5 -.028 .089 -.053 -.254 .774 -.177

CIM6 -.129 -.102 -.130 .020 -.177 .813

Anti-image Correlation CIM1 .810a -.095 -.100 -.206 -.035 -.157

CIM2 -.095 .708a -.252 -.262 .115 -.129

CIM3 -.100 -.252 .788a -.113 -.068 -.163

CIM4 -.206 -.262 -.113 .695a -.349 .027

CIM5 -.035 .115 -.068 -.349 .652a -.223

CIM6 -.157 -.129 -.163 .027 -.223 .753a

a. Measures of Sampling Adequacy(MSA)

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Communalities

Initial Extraction

CIM1 1.000 .379

CIM2 1.000 .391

CIM3 1.000 .427

CIM4 1.000 .515

CIM5 1.000 .331

CIM6 1.000 .363

Extraction Method: Principal

Component Analysis.

Total Variance Explained

Compo

nent

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.405 40.083 40.083 2.405 40.083 40.083

2 .947 15.776 55.859

3 .805 13.424 69.283

4 .753 12.549 81.833

5 .627 10.449 92.281

6 .463 7.719 100.000

Extraction Method: Principal Component Analysis.

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Component Matrixa

Component

1

CIM1 .615

CIM2 .626

CIM3 .653

CIM4 .717

CIM5 .575

CIM6 .602

Extraction Method:

Principal Component

Analysis.

a. 1 components

extracted.

Reproduced Correlations

CIM1 CIM2 CIM3 CIM4 CIM5 CIM6

Reproduced Correlation CIM1 .379a .385 .402 .442 .354 .371

CIM2 .385 .391a .409 .449 .360 .377

CIM3 .402 .409 .427a .469 .376 .393

CIM4 .442 .449 .469 .515a .413 .432

CIM5 .354 .360 .376 .413 .331a .346

CIM6 .371 .377 .393 .432 .346 .363a

Residualb CIM1 -.123 -.139 -.100 -.146 -.098

CIM2 -.123 -.037 -.083 -.252 -.127

CIM3 -.139 -.037 -.159 -.163 -.097

CIM4 -.100 -.083 -.159 -.003 -.214

CIM5 -.146 -.252 -.163 -.003 -.048

CIM6 -.098 -.127 -.097 -.214 -.048

Extraction Method: Principal Component Analysis.

a. Reproduced communalities

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Component Matrixa

Component

1

CIM1 .615

CIM2 .626

CIM3 .653

CIM4 .717

CIM5 .575

CIM6 .602

Extraction Method:

Principal Component

Analysis.

b. Residuals are computed between observed and reproduced correlations. There are 12 (80.0%) nonredundant

residuals with absolute values greater than 0.05.

Lampiran 4. Uji validitas instrumen Kualitas Produk

Correlation Matrixa

KUP1 KUP2 KUP3 KUP4 KUP5

Correlation KUP1 1.000 .520 .573 .425 .511

KUP2 .520 1.000 .659 .624 .421

KUP3 .573 .659 1.000 .680 .542

KUP4 .425 .624 .680 1.000 .539

KUP5 .511 .421 .542 .539 1.000

Sig. (1-tailed) KUP1 .000 .000 .000 .000

KUP2 .000 .000 .000 .000

KUP3 .000 .000 .000 .000

KUP4 .000 .000 .000 .000

KUP5 .000 .000 .000 .000

a. Determinant = .104

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Inverse of Correlation Matrix

KUP1 KUP2 KUP3 KUP4 KUP5

KUP1 1.720 -.434 -.559 .187 -.495

KUP2 -.434 2.078 -.708 -.687 .102

KUP3 -.559 -.708 2.569 -.889 -.329

KUP4 .187 -.687 -.889 2.242 -.533

KUP5 -.495 .102 -.329 -.533 1.676

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .825

Bartlett's Test of Sphericity Approx. Chi-Square 162.064

df 10

Sig. .000

Anti-image Matrices

KUP1 KUP2 KUP3 KUP4 KUP5

Anti-image Covariance KUP1 .582 -.122 -.126 .049 -.172

KUP2 -.122 .481 -.133 -.147 .029

KUP3 -.126 -.133 .389 -.154 -.076

KUP4 .049 -.147 -.154 .446 -.142

KUP5 -.172 .029 -.076 -.142 .597

Anti-image Correlation KUP1 .827a -.230 -.266 .095 -.292

KUP2 -.230 .835a -.306 -.318 .055

KUP3 -.266 -.306 .823a -.371 -.159

KUP4 .095 -.318 -.371 .804a -.275

KUP5 -.292 .055 -.159 -.275 .844a

a. Measures of Sampling Adequacy(MSA)

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Communalities

Initial Extraction

KUP1 1.000 .560

KUP2 1.000 .657

KUP3 1.000 .760

KUP4 1.000 .678

KUP5 1.000 .552

Extraction Method: Principal

Component Analysis.

Total Variance Explained

Compo

nent

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.207 64.148 64.148 3.207 64.148 64.148

2 .628 12.553 76.701

3 .553 11.066 87.767

4 .327 6.546 94.313

5 .284 5.687 100.000

Extraction Method: Principal Component Analysis.

Component Matrixa

Component

1

KUP1 .748

KUP2 .811

KUP3 .872

KUP4 .823

KUP5 .743

Extraction Method:

Principal Component

Analysis.

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Anti-image Matrices

KUP1 KUP2 KUP3 KUP4 KUP5

Anti-image Covariance KUP1 .582 -.122 -.126 .049 -.172

KUP2 -.122 .481 -.133 -.147 .029

KUP3 -.126 -.133 .389 -.154 -.076

KUP4 .049 -.147 -.154 .446 -.142

KUP5 -.172 .029 -.076 -.142 .597

Anti-image Correlation KUP1 .827a -.230 -.266 .095 -.292

KUP2 -.230 .835a -.306 -.318 .055

KUP3 -.266 -.306 .823a -.371 -.159

KUP4 .095 -.318 -.371 .804a -.275

KUP5 -.292 .055 -.159 -.275 .844a

a. 1 components

extracted.

Reproduced Correlations

KUP1 KUP2 KUP3 KUP4 KUP5

Reproduced Correlation KUP1 .560a .606 .652 .616 .556

KUP2 .606 .657a .707 .668 .602

KUP3 .652 .707 .760a .718 .648

KUP4 .616 .668 .718 .678a .612

KUP5 .556 .602 .648 .612 .552a

Residualb KUP1 -.087 -.079 -.191 -.044

KUP2 -.087 -.048 -.043 -.182

KUP3 -.079 -.048 -.038 -.106

KUP4 -.191 -.043 -.038 -.073

KUP5 -.044 -.182 -.106 -.073

Extraction Method: Principal Component Analysis.

a. Reproduced communalities

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Anti-image Matrices

KUP1 KUP2 KUP3 KUP4 KUP5

Anti-image Covariance KUP1 .582 -.122 -.126 .049 -.172

KUP2 -.122 .481 -.133 -.147 .029

KUP3 -.126 -.133 .389 -.154 -.076

KUP4 .049 -.147 -.154 .446 -.142

KUP5 -.172 .029 -.076 -.142 .597

Anti-image Correlation KUP1 .827a -.230 -.266 .095 -.292

KUP2 -.230 .835a -.306 -.318 .055

KUP3 -.266 -.306 .823a -.371 -.159

KUP4 .095 -.318 -.371 .804a -.275

KUP5 -.292 .055 -.159 -.275 .844a

b. Residuals are computed between observed and reproduced correlations. There are 6 (60.0%)

nonredundant residuals with absolute values greater than 0.05.

Lampiran 5. Uji validitas instrumen Loyalitas Konsumen

Correlation Matrixa

LOP1 LOP2 LOP3 LOP4

Correlation LOP1 1.000 .815 .604 .622

LOP2 .815 1.000 .625 .597

LOP3 .604 .625 1.000 .666

LOP4 .622 .597 .666 1.000

Sig. (1-tailed) LOP1 .000 .000 .000

LOP2 .000 .000 .000

LOP3 .000 .000 .000

LOP4 .000 .000 .000

a. Determinant = .094

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Inverse of Correlation Matrix

LOP1 LOP2 LOP3 LOP4

LOP1 3.279 -2.179 -.232 -.585

LOP2 -2.179 3.262 -.588 -.200

LOP3 -.232 -.588 2.115 -.914

LOP4 -.585 -.200 -.914 2.091

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .777

Bartlett's Test of Sphericity Approx. Chi-Square 170.161

df 6

Sig. .000

Anti-image Matrices

LOP1 LOP2 LOP3 LOP4

Anti-image Covariance LOP1 .305 -.204 -.033 -.085

LOP2 -.204 .307 -.085 -.029

LOP3 -.033 -.085 .473 -.207

LOP4 -.085 -.029 -.207 .478

Anti-image Correlation LOP1 .739a -.666 -.088 -.223

LOP2 -.666 .738a -.224 -.077

LOP3 -.088 -.224 .829a -.434

LOP4 -.223 -.077 -.434 .829a

a. Measures of Sampling Adequacy(MSA)

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Communalities

Initial Extraction

LOP1 1.000 .788

LOP2 1.000 .785

LOP3 1.000 .700

LOP4 1.000 .693

Extraction Method: Principal

Component Analysis.

Total Variance Explained

Compo

nent

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.966 74.157 74.157 2.966 74.157 74.157

2 .514 12.861 87.019

3 .338 8.439 95.458

4 .182 4.542 100.000

Extraction Method: Principal Component Analysis.

Component Matrixa

Component

1

LOP1 .888

LOP2 .886

LOP3 .836

LOP4 .833

Extraction Method:

Principal Component

Analysis.

a. 1 components

extracted.

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Reproduced Correlations

LOP1 LOP2 LOP3 LOP4

Reproduced Correlation LOP1 .788a .787 .742 .739

LOP2 .787 .785a .741 .738

LOP3 .742 .741 .700a .696

LOP4 .739 .738 .696 .693a

Residualb LOP1 .028 -.138 -.117

LOP2 .028 -.117 -.141

LOP3 -.138 -.117 -.031

LOP4 -.117 -.141 -.031

Extraction Method: Principal Component Analysis.

a. Reproduced communalities

b. Residuals are computed between observed and reproduced correlations. There are 4

(66.0%) nonredundant residuals with absolute values greater than 0.05.

Lampiran 6. Uji reliabilitas instrumen Citra Merek

Case Processing Summary

N %

Cases Valid 75 100.0

Excludeda 0 .0

Total 75 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's

Alpha N of Items

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Reliability Statistics

Cronbach's

Alpha N of Items

.698 6

Lampiran 7. Uji reliabilitas instrumen Kualitas Produk

Case Processing Summary

N %

Cases Valid 75 100.0

Excludeda 0 .0

Total 75 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's

Alpha N of Items

.859 5

Lampiran 8. Uji reliabilitas instrumen Loyalitas Konsumen

Case Processing Summary

N %

Cases Valid 75 100.0

Excludeda 0 .0

Total 75 100.0

a. Listwise deletion based on all variables in the

procedure.

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Reliability Statistics

Cronbach's

Alpha N of Items

.884 4

Lampiran 9. Output Analisis Structural Equation Modelling (SEM) DATE: 6/14/2014 TIME: 12:22 L I S R E L 8.51 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2001 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 E:\SKripsi Rinto\rinto data lisrel.LS8: raw data from file DATARINTO.psf latent variables: CIM KUP LOP relationships: CIM1 = CIM CIM2 = CIM CIM3 = CIM CIM4 = CIM !CIM5 = CIM !CIM6 = CIM KUP1 = KUP KUP2 = KUP KUP3 = KUP

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KUP4 = KUP KUP5 = KUP LOP1 = LOP LOP2 = LOP LOP3 = LOP LOP4 = LOP CIM = KUP LOP = CIM KUP options: SC path diagram end of problem Sample Size = 75 Covariance Matrix CIM1 CIM2 CIM3 CIM4 LOP1 LOP2 -------- -------- -------- -------- -------- -------- CIM1 0.19 CIM2 0.05 0.23 CIM3 0.06 0.09 0.25 CIM4 0.08 0.09 0.08 0.30 LOP1 0.02 0.06 0.03 0.05 1.07 LOP2 0.02 0.02 -0.02 0.02 0.87 1.06 LOP3 0.10 0.02 0.00 -0.01 0.59 0.61 LOP4 0.07 0.06 0.14 0.15 0.61 0.58 KUP1 - - 0.02 0.05 0.04 0.13 0.00 KUP2 0.04 -0.09 0.02 0.08 -0.02 0.01 KUP3 0.05 -0.03 0.02 0.09 0.08 0.03 KUP4 -0.03 -0.07 -0.08 0.08 -0.06 -0.13 KUP5 0.02 -0.06 0.03 0.08 0.16 0.14 Covariance Matrix LOP3 LOP4 KUP1 KUP2 KUP3 KUP4 -------- -------- -------- -------- -------- -------- LOP3 0.90 LOP4 0.60 0.90 KUP1 0.19 0.20 0.77 KUP2 0.13 0.20 0.44 0.95 KUP3 0.18 0.19 0.44 0.57 0.78 KUP4 0.09 0.07 0.34 0.55 0.54 0.82 KUP5 0.15 0.28 0.41 0.37 0.44 0.45 Covariance Matrix

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KUP5 -------- KUP5 0.83 Number of Iterations = 15 LISREL Estimates (Maximum Likelihood) Measurement Equations CIM1 = 0.22*CIM, Errorvar.= 0.14 , R² = 0.25 (0.028) 5.03 CIM2 = 0.28*CIM, Errorvar.= 0.15 , R² = 0.35 (0.10) (0.034) 2.83 4.28 CIM3 = 0.27*CIM, Errorvar.= 0.17 , R² = 0.30 (0.099) (0.037) 2.74 4.70 CIM4 = 0.34*CIM, Errorvar.= 0.18 , R² = 0.39 (0.12) (0.045) 2.85 4.00 LOP1 = 0.93*LOP, Errorvar.= 0.21 , R² = 0.80 (0.065) 3.29 LOP2 = 0.91*LOP, Errorvar.= 0.23 , R² = 0.79 (0.093) (0.065) 9.84 3.48 LOP3 = 0.68*LOP, Errorvar.= 0.43 , R² = 0.52 (0.094) (0.080) 7.27 5.39 LOP4 = 0.68*LOP, Errorvar.= 0.43 , R² = 0.52 (0.094) (0.081) 7.26 5.39 KUP1 = 0.57*KUP, Errorvar.= 0.44 , R² = 0.43

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(0.095) (0.080) 6.00 5.45 KUP2 = 0.74*KUP, Errorvar.= 0.40 , R² = 0.58 (0.10) (0.082) 7.35 4.89 KUP3 = 0.76*KUP, Errorvar.= 0.19 , R² = 0.75 (0.086) (0.055) 8.86 3.49 KUP4 = 0.71*KUP, Errorvar.= 0.32 , R² = 0.61 (0.093) (0.068) 7.58 4.74 KUP5 = 0.59*KUP, Errorvar.= 0.49 , R² = 0.42 (0.100) (0.088) 5.90 5.48 Structural Equations CIM = 0.072*KUP, Errorvar.= 0.99 , R² = 0.0051 (0.15) (0.55) 0.47 1.80 LOP = 0.14*CIM + 0.098*KUP, Errorvar.= 0.97 , R² = 0.032 (0.15) (0.13) (0.21) 0.93 0.76 4.68 Reduced Form Equations CIM = 0.072*KUP, Errorvar.= 0.99, R² = 0.0051 (0.15) 0.47 LOP = 0.11*KUP, Errorvar.= 0.99, R² = 0.012 (0.13) 0.84 Correlation Matrix of Independent Variables KUP -------- 1.00

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Covariance Matrix of Latent Variables CIM LOP KUP -------- -------- -------- CIM 1.00 LOP 0.15 1.00 KUP 0.07 0.11 1.00 Goodness of Fit Statistics Degrees of Freedom = 62 Minimum Fit Function Chi-Square = 124.99 (P = 0.00) Normal Theory Weighted Least Squares Chi-Square = 98.68 (P = 0.0021) Estimated Non-centrality Parameter (NCP) = 36.68 90 Percent Confidence Interval for NCP = (13.50 ; 67.77) Minimum Fit Function Value = 1.69 Population Discrepancy Function Value (F0) = 0.50 90 Percent Confidence Interval for F0 = (0.18 ; 0.92) Root Mean Square Error of Approximation (RMSEA) = 0.089 90 Percent Confidence Interval for RMSEA = (0.054 ; 0.12) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.035 Expected Cross-Validation Index (ECVI) = 2.12 90 Percent Confidence Interval for ECVI = (1.80 ; 2.54) ECVI for Saturated Model = 2.46 ECVI for Independence Model = 6.94 Chi-Square for Independence Model with 78 Degrees of Freedom = 487.67 Independence AIC = 513.67 Model AIC = 156.68 Saturated AIC = 182.00 Independence CAIC = 556.80 Model CAIC = 252.88 Saturated CAIC = 483.89 Normed Fit Index (NFI) = 0.74 Non-Normed Fit Index (NNFI) = 0.81 Parsimony Normed Fit Index (PNFI) = 0.59 Comparative Fit Index (CFI) = 0.85 Incremental Fit Index (IFI) = 0.85 Relative Fit Index (RFI) = 0.68 Critical N (CN) = 54.76 Root Mean Square Residual (RMR) = 0.067 Standardized RMR = 0.098

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Goodness of Fit Index (GFI) = 0.83 Adjusted Goodness of Fit Index (AGFI) = 0.75 Parsimony Goodness of Fit Index (PGFI) = 0.57 The Modification Indices Suggest to Add the Path to from Decrease in Chi-Square New Estimate LOP4 CIM 9.5 0.31 The Modification Indices Suggest to Add an Error Covariance Between and Decrease in Chi-Square New Estimate LOP2 LOP1 14.5 0.40 LOP4 LOP3 9.4 0.18 Standardized Solution LAMBDA-Y CIM LOP -------- -------- CIM1 0.22 - - CIM2 0.28 - - CIM3 0.27 - - CIM4 0.34 - - LOP1 - - 0.93 LOP2 - - 0.91 LOP3 - - 0.68 LOP4 - - 0.68 LAMBDA-X KUP -------- KUP1 0.57 KUP2 0.74 KUP3 0.76 KUP4 0.71 KUP5 0.59 BETA CIM LOP -------- -------- CIM - - - - LOP 0.14 - - GAMMA KUP --------

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CIM 0.07 LOP 0.10 Correlation Matrix of ETA and KSI CIM LOP KUP -------- -------- -------- CIM 1.00 LOP 0.15 1.00 KUP 0.07 0.11 1.00 PSI Note: This matrix is diagonal. CIM LOP -------- -------- 0.99 0.97 Regression Matrix ETA on KSI (Standardized) KUP -------- CIM 0.07 LOP 0.11 Completely Standardized Solution LAMBDA-Y CIM LOP -------- -------- CIM1 0.50 - - CIM2 0.60 - - CIM3 0.55 - - CIM4 0.62 - - LOP1 - - 0.89 LOP2 - - 0.89 LOP3 - - 0.72 LOP4 - - 0.72 LAMBDA-X KUP -------- KUP1 0.65 KUP2 0.76 KUP3 0.87 KUP4 0.78 KUP5 0.65

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BETA CIM LOP -------- -------- CIM - - - - LOP 0.14 - - GAMMA KUP -------- CIM 0.07 LOP 0.10 Correlation Matrix of ETA and KSI CIM LOP KUP -------- -------- -------- CIM 1.00 LOP 0.15 1.00 KUP 0.07 0.11 1.00 PSI Note: This matrix is diagonal. CIM LOP -------- -------- 0.99 0.97 THETA-EPS CIM1 CIM2 CIM3 CIM4 LOP1 LOP2 -------- -------- -------- -------- -------- -------- 0.75 0.65 0.70 0.61 0.20 0.21 THETA-EPS LOP3 LOP4 -------- -------- 0.48 0.48 THETA-DELTA KUP1 KUP2 KUP3 KUP4 KUP5 -------- -------- -------- -------- -------- 0.57 0.42 0.25 0.39 0.58 Regression Matrix ETA on KSI (Standardized) KUP

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-------- CIM 0.07 LOP 0.11 Time used: 0.062 Seconds