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Table 3 Rotated component matrixa

From: Factors affecting the value revitalization of Qajar religious schools in Tehran

Expert No

Component

1

2

3

4

5

6

7

8

Expert 18

.843

.246

.121

− .194

.001

.030

− .039

.230

Expert 5

.822

− .075

− .287

.137

.092

.020

.095

− .093

Expert 9

.759

.152

− .152

.029

− .055

.345

.011

.175

Expert 22

.601

.410

.212

.179

− .057

− .275

.114

.016

Expert 15

.519

− .013

.259

− .438

− .382

.104

− .158

− .150

Expert 2

− .061

.873

.159

− .019

− .012

.113

.046

− .052

Expert 25

.297

.772

.059

− .016

.142

− .233

− .040

.142

Expert 19

.284

.767

.076

.250

− .089

.180

.208

− .013

Expert 8

− .208

− .016

.697

.037

− .101

.081

.076

− .051

Expert 21

− .138

.358

.690

.255

.335

− .095

− .033

.012

Expert 3

.473

.053

.628

.146

− .121

− .139

.109

− .166

Expert 10

.409

.295

.587

.030

.329

− .231

.033

.246

Expert 17

.323

.197

.400

.234

.265

.322

− .146

.167

Expert 7

.063

.003

.175

.811

− .260

.095

.099

.014

Expert 4

− .104

.148

.218

.692

.202

.259

.216

.066

Expert 6

.275

.056

− .020

.636

.196

.038

− .561

.060

Expert 20

.127

.058

− .083

− .021

.825

.082

.201

− .091

Expert 14

.190

.248

− .203

.147

.561

− .097

.319

− .190

Expert 24

− .215

.158

.380

.276

.536

− .148

.237

.331

Expert 13

.036

.248

− .021

.261

− .190

.754

.097

.000

Expert 16

− .093

.185

.042

− .015

− .235

.701

.166

− .103

Expert 1

.204

.044

.069

.171

.229

− .177

.778

− .154

Expert 11

− .055

.225

.121

.123

.058

.210

.528

.437

Expert 12

.134

− .081

− .025

.111

.091

.031

− .046

.841

Expert 23

− .096

− .328

.150

.263

.273

− .134

.138

.433

  1. Italic values indicate a high correlation coefficient among the experts in each factor
  2. Extraction Method: Principal Component Analysis
  3. aRotation Method: Varimax with Kaiser Normalization