Identifikasi Faktor Penyebab Pengangguran di Kota Medan Kecamatan Medan Selayang dengan Menggunakan Metode Analisis Faktor

47

LAMPIRAN
UJI VALIDITAS I

Correlations
X1
X1

Pearson Correlation

X2
1

Sig. (2-tailed)
N
X2

X4

X7

.151

-.047

.111

.366

.011

.580

.139

.649

.277

1

Sig. (2-tailed)

.366
98

98

*

**

Pearson Correlation

.256

Sig. (2-tailed)

.011

.000

98

98

.389

98

98

98

98

**

-.025

-.025

.176

.000

.808

.808

98

98

98

.389

1

98

Pearson Correlation

.057

-.025

-.053

Sig. (2-tailed)

.580

.808

.605

X11

Y
*

-.018

.211

-.026

.258

.860

.037

.798

.010

.394

**

.000

98

98

98

98

.106

.206

*

-.076

.068

.083

.003

.298

.042

.460

.507

.000

98

98

98

98

98

98

98

**

-.044

.046

.297

**

.059

.127

-.017

.605

.526

.566

.214

.871

.002

.671

.652

.000

98

98

98

98

98

98

98

98

98

.090

.231

*

.151

.128

.211

*

-.111

.210

*

.377

.022

.138

.210

.037

.276

.038

98

98

98

98

98

-.025

.065

.090

1

.205

Sig. (2-tailed)

.139

.808

.526

.377

98

98

98

98
*

*

98
.225

*

98
.266

**

98

98

**

.083

.270

98
.280

**

.432

**

.065

1

.316

98
.475

-.053

98

N

X10
*

**

.151

Sig. (2-tailed)

X9

98

Pearson Correlation

Pearson Correlation

X8

.057

98

N
X6

X6

.256

98

N
X5

X5

.092

.092

N

X4
*

Pearson Correlation

N
X3

X3

.413

**

.000
98
.532

**

.043

.026

.008

.007

.415

.005

.000

98

98

98

98

98

98

98

98

*

1

.159

.141

.089

.079

.086

.118

.165

.385

.442

.397

.000

98

98

98

98

98

98

-.047

.176

.059

.231

.205

.649

.083

.566

.022

.043

98

98

98

98

98

98

Universitas Sumatera Utara

.443

**

48

X7

Pearson Correlation

.111

Sig. (2-tailed)

.277

N
X8

Pearson Correlation
Sig. (2-tailed)
N

X9

X11

.159

.003

.214

.138

.026

.118

98

98

-.017

.128

.860

.298

.871

98

98

*

*

.206

.042

1

*

.014

.182

.010

.215

.889

.072

.926

.034

.524

**

.000

98

98

98

98

98

98

98

**

.141

.014

1

-.060

-.169

.098

.210

.008

.165

.889

.560

.096

.335

.000

98

98

98

98

98

98

98

98

98

**

*

**

.089

.182

-.060

.008

**

.007

.385

.072

.560

.316

.002

.211

.037

.266

.270

98

1

.942

.295

.003

98
.347

.549

**

**

.000

98

98

98

98

98

98

98

98

98

98

98

98

-.026

-.076

-.044

-.111

.083

.079

.010

-.169

.008

1

.127

.149

.798

.460

.671

.276

.415

.442

.926

.096

.942

.211

.142

98

98

98

98

98

98

98

98

98

98

98

98

*

**

.086

.215

*

.098

**

.127

1

Pearson Correlation

.258

*

.068

.046

.210

Sig. (2-tailed)

.010

.507

.652

.038

.005

.397

.034

.335

.003

.211

98

98

98

98

98

98

98

98

98

98

98

98

**

**

**

**

**

**

**

**

**

.149

**

1

N
Y

*

98

.037

N

.225

.106

Sig. (2-tailed)

Sig. (2-tailed)

.151

98

.211

Pearson Correlation

.127

-.018

Pearson Correlation

N
X10

**

.297

Pearson Correlation
Sig. (2-tailed)
N

.394

.475

.432

.413

.280

.532

.443

.524

.347

.295

.549

.562

**

.000

.562

.000

.000

.000

.000

.000

.000

.000

.000

.000

.142

.000

98

98

98

98

98

98

98

98

98

98

98

*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

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49

UJI VALIDITAS II
Correlations
X1
X1

Pearson Correlation

X2
1

Sig. (2-tailed)
N
X2

Pearson Correlation

.092

Sig. (2-tailed)

.366

N
X3

X11

Y

.151

-.047

.111

-.018

.211

.366

.011

.580

.139

.649

.277

.860

.037

.010

.000

98

98

98

98

98

98

98

98

98

98

1

**

-.025

-.025

.176

**

.106

.206

*

.068

.000

.808

.808

.083

.003

.298

.042

.507

.000

98

98

98

98

98

98

98

98

98

**

.046

.002

.652

.389

.389

1

.000

.297

-.053

.065

.059

.127

-.017

.605

.526

.566

.214

.871

.316

*

.258

*

.494

.445

**

**

**

.000

98

98

98

98

98

98

98

98

-.025

-.053

1

.090

.231

*

.151

.128

.211

Sig. (2-tailed)

.580

.808

.605

.377

.022

.138

.210

.037

.038

.000

98

98

98

98

98

98

98

98

98

98

1

*

*

**

**

**

98

Pearson Correlation

.151

-.025

.065

.090

Sig. (2-tailed)

.139

.808

.526

.377

.205

.043

98

98

98

98

-.047

.176

.059

.231

.649

.083

.566

.022

.043

98

98

98

98

98

Pearson Correlation

.111

**

.127

.151

.225

*

.159

Sig. (2-tailed)

.277

.003

.214

.138

.026

.118

Pearson Correlation

N

.297

*

.225

.026

.266

.008

.270

*

.007

98

.403

98

Sig. (2-tailed)

X7

X9

.057

N
X6

X8

Pearson Correlation

N
X5

X7

.057

**

.011

X6

.256

98

Sig. (2-tailed)

X5

.092

*

.256

X4
*

98

Pearson Correlation

N
X4

98

X3

.210

.280

*

.005

98
.438

.523

**

**

.000

98

98

98

98

98

98

*

1

.159

.141

.089

.086

.118

.165

.385

.397

.000

98

98

98

98

98

.014

.182

.215

*

.889

.072

.034

.205

98

1

98
.433

.527

**

**

.000

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N
X8

Pearson Correlation
Sig. (2-tailed)

98

98

98

98

-.018

.106

-.017

.128

.860

.298

.871

98

98

*

*

N
X9

Pearson Correlation

.211

Sig. (2-tailed)

.037

N
X11

98

98

98

.014

1

-.060

.098

.210

.008

.165

.889

.560

.335

.000

98

98

98

98

98

98

98

98

**

*

**

.089

.182

-.060

1

**

.007

.385

.072

.560

.316

.002

.211

.037

98

98

98

.068

.046

.210

.010

.507

.652

98

98

**

**

Sig. (2-tailed)

.403

Sig. (2-tailed)

98

.141

*

.258

Pearson Correlation

.042

98

**

98

Pearson Correlation

N
Y

.206

98

.494

.266

.270

98

.295

.003

98

98

98

.215

*

.098

.038

.005

.397

.034

.335

.003

98

98

98

98

98

98

98

98

98

**

**

**

**

**

**

**

**

1

.438

.523

.433

.527

.382

1

.295

.554

98
.544

**

.000

.544

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

98

98

98

98

98

98

98

98

98

98

N

.000

.086

.445

98

**

**

.280

**

.554

**

98

*

98

98
.382

98

*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

CRONBACH ALPHA

Case Processing Summary
N
Cases

Valid
a

Excluded
Total

%
98

100.0

0

.0

98

100.0

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Case Processing Summary
N
Cases

Valid
a

Excluded
Total

%
98

100.0

0

.0

98

100.0

a. Listwise deletion based on all
variables in the procedure.

Reliability Statistics
Cronbach's
Alpha
.702

N of Items
11
ANALISIS FAKTOR
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity

Approx. Chi-Square
Df
Sig.

.601
112.628
45
.000

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Communalities
Initial

Extraction

X1

1.000

.597

X2

1.000

.723

X3

1.000

.672

X4

1.000

.608

X5

1.000

.618

X6

1.000

.542

X7

1.000

.424

X8

1.000

.739

X9

1.000

.507

X11

1.000

.545

Extraction Method: Principal
Component Analysis.

Total Variance Explained
Initial Eigenvalues

Compon
ent
1

Total
2.367

% of Variance
23.669

Extraction Sums of Squared Loadings

Cumulative %
23.669

Total
2.367

% of Variance
23.669

Cumulative %
23.669

Rotation Sums of Squared Loadings
Total
1.624

% of Variance
16.237

Cumulative %
16.237

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2

1.426

14.259

37.928

1.426

14.259

37.928

1.559

15.588

31.825

3

1.182

11.817

49.745

1.182

11.817

49.745

1.484

14.837

46.662

4

1.002

10.018

59.763

1.002

10.018

59.763

1.310

13.102

59.763

5

.872

8.722

68.486

6

.822

8.223

76.708

7

.732

7.318

84.027

8

.672

6.718

90.745

9

.519

5.192

95.936

10

.406

4.064

100.000

Extraction Method: Principal Component Analysis.

Component Matrix

a

Component
1

2

3

4

X1

.446

-.290

-.476

.296

X2

.490

-.404

.565

.018

X3

.468

-.608

.188

.220

X4

.427

.377

-.081

-.526

X5

.527

.407

-.117

.401

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X6

.405

.352

.475

-.169

X7

.559

-.043

.141

-.301

X8

.192

.557

.312

.543

X9

.649

-.158

-.223

-.106

X11

.561

.201

-.433

-.053

Extraction Method: Principal Component Analysis.
a. 4 components extracted.

Rotated Component Matrix

a

Component
1

2

3

4

X1

.737

.170

-.159

-.003

X2

-.053

.826

.184

.067

X3

.290

.757

-.114

-.052

X4

.149

-.156

.749

.003

X5

.432

-.014

.145

.640

X6

-.198

.222

.558

.377

X7

.179

.337

.527

-.014

X8

-.084

-.005

-.013

.856

X9

.565

.273

.334

-.043

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X11

.623

-.113

.357

.132

Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.

Component Transformation Matrix
Compo
nent

1

2

3

4

1

.612

.487

.548

.296

2

-.157

-.639

.398

.639

3

-.744

.577

.158

.297

4

.216

.144

-.718

.645

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

Universitas Sumatera Utara

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Anti-image Matrices
X1
Anti-image Covariance

Anti-image Correlation

X2

X3

X4

X5

X6

X7

X8

X9

X11

X1

.852

.011

-.175

-.014

-.060

.082

-.025

.028

-.049

-.171

X2

.011

.728

-.245

.073

.136

-.119

-.220

-.132

-.096

-.003

X3

-.175

-.245

.755

.046

-.007

-.010

.012

.047

-.154

.060

X4

-.014

.073

.046

.842

.087

-.155

-.117

-.123

-.166

-.113

X5

-.060

.136

-.007

.087

.745

-.138

-.165

-.236

-.185

-.092

X6

.082

-.119

-.010

-.155

-.138

.871

-.032

-.054

.008

-.013

X7

-.025

-.220

.012

-.117

-.165

-.032

.801

.120

.012

-.091

X8

.028

-.132

.047

-.123

-.236

-.054

.120

.852

.135

-.038

X9

-.049

-.096

-.154

-.166

-.185

.008

.012

.135

.732

-.130

X11

-.171

-.003

.060

-.113

-.092

-.013

-.091

-.038

-.130

.809

X1

.677

a

.014

-.218

-.017

-.075

.095

-.030

.033

-.062

-.206

a

-.331

.093

.184

-.149

-.289

-.168

-.132

-.003

a

.058

-.010

-.013

.016

.058

-.206

.077

a

.109

-.181

-.142

-.145

-.212

-.136

a

-.172

-.214

-.296

-.251

-.118

a

-.038

-.062

.010

-.015

X2

.014

.529

X3

-.218

-.331

X4

-.017

.093

.058

X5

-.075

.184

-.010

.109

.540

X6

.095

-.149

-.013

-.181

-.172

.610

.575

.668

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.629

a

X7

-.030

-.289

.016

-.142

-.214

-.038

.145

.016

-.113

X8

.033

-.168

.058

-.145

-.296

-.062

.145

a

.171

-.046

X9

-.062

-.132

-.206

-.212

-.251

.010

.016

.171

.660

a

-.169

X11

-.206

-.003

.077

-.136

-.118

-.015

-.113

-.046

-.169

.696

.729

a

a. Measures of Sampling Adequacy(MSA)

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DAFTAR JAWABAN RESPONDEN
NO

X1

X2

X3

X4

X5

X6

X7

X8

X9

X10

X11

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

3
4
3
3
4
4
4
4
4
4
4
5
3
1
4
4
3
3
5
4
4

2
5
3
3
4
3
3
3
4
5
2
3
3
1
2
4
4
4
4
4
5

2
3
2
2
4
2
2
4
4
4
2
3
2
1
2
3
3
2
3
3
4

5
2
2
2
3
4
4
2
2
3
5
4
4
1
3
3
2
2
2
3
2

4
3
2
3
3
5
3
4
4
2
4
4
3
3
5
5
3
3
4
4
5

3
2
4
3
3
3
5
2
4
3
4
3
3
3
2
5
2
3
3
3
3

2
5
2
4
3
5
4
3
5
2
5
5
5
1
5
3
4
4
5
5
4

2
4
2
4
3
4
5
4
5
3
3
4
5
5
1
5
3
3
4
4
3

5
3
2
4
4
4
4
4
3
4
4
4
4
1
5
5
3
2
3
4
5

5
3
4
5
4
4
3
4
5
5
5
3
2
5
5
4
4
4
5
4
4

4
4
2
2
1
3
5
4
3
4
3
5
2
1
5
2
3
3
4
4
3

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22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

4
4
4
5
3
4
4
4
4
4
4
5
5
3
4
4
3
2
5
4
5
5
5
2

5
2
4
4
5
4
3
2
1
3
3
4
4
2
5
4
3
5
5
3
5
4
4
5

5
3
4
3
4
5
2
3
4
2
4
4
3
2
4
4
5
3
5
4
4
4
3
5

2
5
4
2
3
2
1
2
4
4
3
4
4
2
3
3
4
3
2
2
3
2
5
2

3
4
4
4
5
4
5
4
4
3
4
5
4
4
5
4
2
4
2
5
5
4
5
5

4
5
4
4
4
3
5
3
3
4
3
4
2
4
4
3
4
5
5
3
5
3
3
5

4
5
5
3
5
4
4
4
3
3
4
4
4
4
5
4
4
5
4
4
4
4
5
5

5
2
4
2
4
5
4
4
2
4
4
4
2
3
3
3
2
2
4
4
4
2
4
4

4
4
5
5
4
5
2
2
3
2
4
4
4
4
4
4
4
5
4
4
5
2
5
5

5
5
5
5
5
5
5
4
5
3
4
2
4
4
5
2
3
4
4
5
5
4
5
5

3
4
3
4
4
5
3
3
4
4
4
2
4
5
5
3
4
3
5
3
4
2
3
5

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46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

4
4
5
4
4
4
4
4
4
3
4
5
4
5
4
5
5
4
3
4
4
2
4
4

5
4
5
4
4
5
4
2
2
3
4
5
2
2
4
3
4
3
2
4
5
5
5
4

2
3
5
3
4
3
4
2
4
3
5
3
3
2
4
4
4
4
3
4
5
2
5
3

2
3
3
2
3
5
4
2
4
4
5
3
5
2
3
3
2
5
2
2
2
4
2
3

2
4
2
4
3
5
4
5
3
5
5
5
5
5
4
5
3
4
3
2
4
2
4
4

5
3
2
3
3
5
4
2
4
5
5
4
3
4
4
4
2
3
2
2
2
3
4
4

3
5
5
5
3
5
4
2
4
3
5
2
4
5
3
3
3
2
3
3
4
5
5
5

1
5
3
3
2
5
5
4
4
5
4
5
5
5
5
5
4
5
2
4
2
2
2
2

5
5
5
5
4
5
4
4
5
4
5
5
5
5
5
5
3
5
5
4
4
4
5
4

4
3
2
5
5
5
4
4
3
4
4
5
3
5
4
5
5
4
5
4
3
5
5
5

1
4
1
4
2
5
4
4
3
2
4
5
5
5
4
4
3
3
3
3
3
3
2
5

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70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93

4
2
4
4
4
4
3
2
4
5
4
5
4
5
4
2
5
5
3
4
4
3
4
5

3
2
5
5
5
2
5
5
3
3
5
4
4
3
3
5
2
5
2
3
2
4
4
2

2
3
2
2
3
2
4
4
4
3
4
4
3
2
5
3
4
4
3
3
3
4
4
4

2
4
5
5
5
2
3
4
2
2
3
4
4
5
4
2
4
2
2
2
2
2
4
2

5
3
5
4
1
2
5
4
5
4
5
3
5
4
5
4
4
5
4
3
3
4
4
4

3
4
5
4
3
4
4
4
3
4
4
3
5
3
4
5
5
3
3
3
3
3
5
4

5
5
5
5
4
3
4
5
5
5
5
3
2
3
4
4
5
5
2
3
3
2
4
2

4
4
5
5
4
2
4
4
4
2
4
2
4
5
4
4
4
5
4
4
2
4
4
2

4
3
2
4
5
2
5
4
4
5
5
4
4
4
4
4
4
5
4
3
4
4
4
5

4
5
5
3
5
5
3
4
4
4
5
3
3
3
5
2
3
3
5
4
4
3
4
4

4
1
4
4
5
2
4
4
4
4
4
4
4
5
3
3
4
4
4
3
4
4
4
2

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94
95
96
97
98

5
5
5
5
3

4
5
4
3
2

5
4
3
4
2

4
4
2
2
5

4
3
3
5
4

5
4
4
3
3

Correlation Matrix
x1
Sig. (1-tailed)

x2

x1

x3
.180

x4

5
4
5
5
3

1
4
3
1
2

5
3
5
5
5

5
5
5
5
5

5
5
5
5
5

a

x5

x6

x7

x8

x9

x11

.005

.253

.076

.374

.114

.407

.016

.005

.000

.403

.495

.038

.001

.193

.018

.236

.477

.281

.275

.101

.312

.002

.326

.187

.017

.038

.100

.009

.011

.017

.011

.005

.003

.006

.064

.067

.138

.156

.415

.032

.016

.294

.206

x2

.180

x3

.005

.000

x4

.253

.403

.477

x5

.076

.495

.281

.187

x6

.374

.038

.275

.017

.017

x7

.114

.001

.101

.038

.011

.064

x8

.407

.193

.312

.100

.005

.067

.415

x9

.016

.018

.002

.009

.003

.138

.032

.294

x11

.005

.236

.326

.011

.006

.156

.016

.206

.002
.002

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64

Correlation Matrix
x1
Sig. (1-tailed)

x2

x1

x3
.180

x4

a

x5

x6

x7

x8

x9

x11

.005

.253

.076

.374

.114

.407

.016

.005

.000

.403

.495

.038

.001

.193

.018

.236

.477

.281

.275

.101

.312

.002

.326

.187

.017

.038

.100

.009

.011

.017

.011

.005

.003

.006

.064

.067

.138

.156

.415

.032

.016

.294

.206

x2

.180

x3

.005

.000

x4

.253

.403

.477

x5

.076

.495

.281

.187

x6

.374

.038

.275

.017

.017

x7

.114

.001

.101

.038

.011

.064

x8

.407

.193

.312

.100

.005

.067

.415

x9

.016

.018

.002

.009

.003

.138

.032

.294

x11

.005

.236

.326

.011

.006

.156

.016

.206

.002
.002

a. Determinant = .297

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65

PERHITUNGAN KMO DAN MSA

Untuk menghitung KMO dan MSA maka diperlukan matriks korelasi sederhana dan matriks korelasi parsial yang semua entrinya telah
dikuadratkan. Berikut ini akan disajikan matriks korelasi sederhana dan matriks korelasi parsial yang semua entrinya telah dikuadratkan.

MATRIKS KORELASI SEDERHANA�rij �
X1

X2

X3

X4

X5

X6

X7

X8

X9

X11

X1

1.000

X2

0.180
0.005
0.253
0.076
0.374
0.114
0.407
0.016
0.005

0.180
1.000
0.000
0.403
0.495
0.038
0.001
0.193
0.018
0.236

0.005
0.000
1.000
0.477
0.281
0.275
0.101
0.312
0.002
0.326

0.253
0.403
0.477
1.000
0.187
0.017
0.038
0.100
0.009
0.011

0.076
0.495
0.281
0.187
1.000
0.017
0.011
0.005
0.003
0.006

0.374
0.038
0.275
0.017
0.017
1.000
0.064
0.067
0.138
0.156

0.114
0.001
0.101
0.038
0.011
0.064
1.000
0.415
0.032
0.016

0.407
0.193
0.312
0.100
0.005
0.067
0.415
1.000
0.294
0.206

0.016
0.018
0.002
0.009
0.003
0.138
0.032
0.294
1.000
0.002

0.005
0.236
0.326
0.011
0.006
0.156
0.016
0.206
0.002
1.000

X3
X4
Σ=

X5
X6
X7
X8
X9
X11

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66

MATRIKS KORELASI PARSIAL

X1
X1
X2
X3
X4
X5
A = (aij) =

X6
X7
X8
X9
X11

X2

0.018
0.018
-0.272
-0.020
-0.095
0.110
-0.037
0.039
-0.079
-0.248

-.446
0.119
0.250
-0.187
-0.378
-0.214
-0.181
-0.004

X3

X4

-0.272 -0.020
-0.446 0.119
0.072
0.072
-0.013 0.138
-0.016 -0.211
0.020 -0.173
0.073 -0.171
-0.278 -0.270
0.099 -0.165

X5

X6

X7

X8

X9

X11

-0.095
0.250
-0.013
0.138

0.110
-0.187
-0.016
-0.211
-0.213

-0.037
-0.378
0.020
-0.173
-0.276
-0.045

0.039
-0.214
0.073
-0.171
-0.372
-0.073
0.175

-0.079
-0.181
-0.278
-0.270
-0.339
0.013
0.021
0.216

-0.248
-0.004
0.099
-0.165
-0.152
-0.018
-0.141
-0.056
-0.220

-0.213
-0.276
-0.372
-0.339
-0.152

-0.045
-0.073
0.013
-0.018

.0175
0.021
-0.141

0.216
-0.056

-0.220

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67

Kuadrat Matriks Korelasi Sederhana
X1

Σ=

(���2 )

=

X1
X2
X3
X4
X5
X6
X7
X8
X9
X11

X2

X3

X4

X5

X6

X7

X8

X9

X11

Jumlah

0.0324

0.000025
0.000000

0.064009
0.162409
0.227529

0.005776
0.245025
0.078961
0.034969

0.139876
0.001444
0.075625
0.000289

0.01300
0.00000
0.01020
0.00144

0.165649
0.037249
0.097344
0.01

0.000256
0.000324
0.000004
0.000081

0.000025
0.055696
0.106276
0.000121

1.421016
1.534547
1.595969
1.500848

0.000289

0.00012
0.00410

0.000025
0.004489
0.172225

0.000009
0.019044
0.001024
0.086436

0.000036
0.024336
0.000256
0.042436
0.000004

1.365214
1.269496
1.202368
1.615859
1.107182
1.229195

0.03240
0.00003
0.06401

0.000000
0.162409

0.227529

0.00578
0.13988
0.01300
0.16565
0.00026
0.00003

0.245025
0.001444
0.000001
0.037249
0.000324
0.055696

0.078961
0.075625
0.010201
0.097344
0.000004
0.106276

0.034969
0.000289
0.001444
0.01
0.000081
0.000121

0.000289
0.000121
0.000025
0.000009
0.000036
Jumlah

0.004096
0.004489
0.019044
0.024336

0.17223
0.00102
0.00026

0.086436
0.042436

0.000004

13.84169

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68

Kuadrat Matriks Korelasi Parsial
X1

D=

2
(���
)

=

X1
X2
X3
X4
X5
X6
X7
X8
X9
X11

X2

X3

0.000324
0.000324

X4

X5

0.073984

0.0004

0.198916

0.014161
0.005184

0.073984

0.198916

0.0004

0.014161

0.005184

0.009025

0.0625

0.000169

0.019044

X6

X7

X8

X9

X11

Jumlah

0.009025

0.0121

0.00137

0.001521

0.006241

0.061504

0.0625

0.034969

0.14288

0.045796

0.032761

0.000016

0.000169

0.000256

0.00040

0.005329

0.077284

0.009801

0.019044

0.044521

0.02993

0.029241

0.0729

0.027225

0.16647
0.53232
0.37132
0.24261

0.045369

0.07618

0.138384

0.114921

0.023104

0.00203

0.005329

0.000169

0.000324

0.030625

0.000441

0.019881

0.046656

0.003136

0.0121

0.034969

0.000256

0.044521

0.045369

0.001369

0.142884

0.0004

0.029929

0.076176

0.002025

0.001521

0.045796

0.005329

0.029241

0.138384

0.005329

0.00031

0.006241

0.032761

0.077284

0.0729

0.114921

0.000169

0.00044

0.046656

0.061504

0.000016

0.009801

0.027225

0.023104

0.000324

0.01988

0.003136

Jumlah

0.0484
0.0484

0.48870
0.14507
0.30373
0.27570
0.39977
0.19339
3.11908

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69

1. KMO= ∑�

�=1

�

�

�

∑�=1 ∑� ≠1 ���2
�

�

2
∑� ≠1 ���2 +∑�=1 ∑� ≠1 � ��

13.84169

KMO = 13.84169 +3.11908 = 0,601

�

�

∑�=1 ∑� ≠1 ���2

2. MSA = ∑�

�
2
� 2 +∑�=1 � ��
�=1 ��

1.421016

���1 =1.421016 +0.16647 = 0,677
1.534547

���2 =1.534547 +0.53232 = 0,529
1.595969

���3 =1.595969+0.37132 = 0,610
1.500848

���4 =1.500848 +0.24261 = 0,575
1.365214

���5 =1.365214 +0.48870 = 0,540
1.269496

���6 =1.269496 +0.14507 = 0,668
1.202368

���7 =1.202368 +0.30373 = 0,629
1.615859

���8 =1.615859 +0.27570 = 0,696
1.107182

���9 =1.107182 +0.39977 = 0,660
1.229195

���11 =1.229195 +0.19339 = 0,729

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70

UJI BARLETT PENDEKATAN STATISTIK CHI AQUARE
Untuk menguji apakah matriks korelasi sederhana bukan merupakan suatumatriks idensitas, maka digunakan uji Barlett dengan
pendekatan statistik chi square. Berikut ini langkah-langkah pengujiannya:
1. Hipotesis
Ho : Matriks korelasi sederhana merupakan matriks idensitas
H1 : Matriks korelasi sederhana bukan merupakan matriks idensitas
2. Statistik Uji
(2� + 5)
� 2 = �(� − 1) −
� ��|∑|
6
3. Taraf nyata α dan nilaiχ2 dari tabel diperoleh:
α = 5% = 0,05
p(p−1)
10(10−1)
dengan df =
=
= 45
2
2
2
χ tabel = 61,66
4. Kriteria pengujian:
H0 ditolak apabila χ2 hitung ≥ χ2 tabel
H0 diterima apabila χ2 hitung < χ2 tabel
5. Perhitungan � 2 :
Det (R) = 0,297
(2(10) + 5)
� ��|0,297|
� 2 = − �(98 − 1) −
6
= −[97 − 4,167](−1,214)
= −(92,83)(−1,214)
=112,695
6. Kesimpulan:
χ2 hitung = 112,695>χ2 tabel = 61,66, maka Ho ditolak. Dengan kata lain Matriks sederhana bukan merupaka matriks idensitas.

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