Skip to main content

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