请高手帮忙看因子分析结果，一共形成了6个公共因子，每个公共因子分别解释了哪些指标，实在看不太懂，急急急！！


Component Matrix(a)
  Component      
1 2 3 4 5 6
VAR00001 .605 -.246 -.028 -.286 -.149 -.511
VAR00002 .592 -.184 .380 -.243 .217 .453
VAR00003 .895 .020 .182 -.226 .054 -.122
VAR00004 .870 .321 .024 .116 .053 .213
VAR00005 .337 .740 -.082 .325 .128 .050
VAR00006 .687 .622 -.029 .295 .069 .135
VAR00007 .023 -.350 -.466 .302 .523 .265
VAR00008 .740 .584 .013 .236 .001 .052
VAR00009 .725 .577 -.013 .274 -.015 -.024
VAR00010 .517 .779 .101 .190 -.024 -.077
VAR00011 .600 -.623 .037 .067 .420 -.069
VAR00012 .956 .013 .028 -.010 .046 -.091
VAR00013 -.277 .735 .265 -.018 -.068 .019
VAR00014 .506 -.530 .352 .301 -.240 -.024
VAR00015 -.276 .594 .078 -.342 .293 -.270
VAR00016 .808 .461 -.133 -.001 .223 -.134
VAR00017 .932 .211 -.043 -.068 .204 -.122
VAR00018 .585 -.484 .392 .037 .085 -.146
VAR00019 .472 -.698 -.043 .236 .068 -.082
VAR00020 .951 -.008 -.027 -.132 .156 .053
VAR00021 .960 .050 .056 -.029 .132 .004
VAR00022 .516 -.458 .431 .329 -.117 .170
VAR00023 .483 -.498 .269 -.066 .453 -.035
VAR00024 .262 .211 .624 .052 -.195 .264
VAR00025 .922 -.106 -.142 -.247 -.071 .073
VAR00026 -.074 .222 .470 -.640 -.083 .135
VAR00027 .858 .180 .132 .245 -.060 -.062
VAR00028 .861 .029 .176 -.182 .145 -.174
VAR00029 .959 -.015 -.126 -.153 -.076 .017
VAR00030 .848 -.091 -.337 .020 -.305 -.012
VAR00031 .900 -.098 -.244 -.164 -.166 .109
VAR00032 .935 .052 -.180 -.226 -.057 .075
VAR00033 .832 -.056 -.226 -.078 -.259 -.003
VAR00034 .931 -.001 -.146 -.082 -.188 .011
VAR00035 .943 -.069 -.023 -.123 -.057 .020
VAR00036 .529 -.478 -.318 -.099 -.256 .282
VAR00037 .352 -.305 .252 .575 -.169 -.328
Extraction Method: Principal Component Analysis.
a 6 components extracted.


Total Variance Explained
Component Extraction Sums of Squared Loadings   Rotation Sums of Squared Loadings   
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 18.872 51.006 51.006 12.155 32.852 32.852
2 6.027 16.289 67.295 7.808 21.102 53.953
3 2.192 5.924 73.219 4.209 11.375 65.329
4 2.097 5.669 78.887 4.080 11.028 76.356
5 1.465 3.959 82.846 2.388 6.455 82.811
6 1.140 3.082 85.928 1.153 3.117 85.928
Extraction Method: Principal Component Analysis.


Rotated Component Matrix(a)
  Component      
1 2 3 4 5 6
VAR00001 .646 -.063 .175 .237 .098 .526
VAR00002 .394 .098 .145 .612 .252 -.465
VAR00003 .689 .350 .146 .466 .224 .132
VAR00004 .569 .703 .128 .214 .041 -.197
VAR00005 .027 .866 -.155 -.122 -.041 -.032
VAR00006 .328 .921 .018 .015 .001 -.116
VAR00007 -.030 -.057 -.019 .248 -.794 -.280
VAR00008 .399 .881 .059 .026 .075 -.032
VAR00009 .384 .882 .082 -.004 .043 .044
VAR00010 .188 .905 -.076 -.079 .227 .099
VAR00011 .376 -.079 .374 .721 -.357 .039
VAR00012 .713 .456 .243 .374 .013 .101
VAR00013 -.380 .418 -.324 -.262 .446 -.007
VAR00014 .288 -.041 .815 .242 .061 -.010
VAR00015 -.290 .216 -.650 .055 .270 .287
VAR00016 .537 .742 -.140 .251 -.049 .159
VAR00017 .667 .588 .017 .407 -.018 .139
VAR00018 .326 -.028 .548 .576 .094 .114
VAR00019 .356 -.187 .583 .367 -.373 .055
VAR00020 .754 .400 .107 .458 -.021 -.039
VAR00021 .687 .495 .188 .440 .019 .005
VAR00022 .217 .059 .774 .335 .078 -.209
VAR00023 .227 -.075 .273 .789 -.102 .000
VAR00024 -.002 .316 .301 .113 .576 -.279
VAR00025 .901 .213 .115 .282 .011 -.046
VAR00026 .024 -.133 -.323 .162 .739 -.123
VAR00027 .510 .638 .371 .198 .064 .065
VAR00028 .617 .375 .126 .510 .165 .180
VAR00029 .874 .340 .149 .252 .008 .009
VAR00030 .877 .253 .270 -.057 -.157 .044
VAR00031 .919 .221 .151 .140 -.062 -.077
VAR00032 .892 .345 .034 .227 .014 -.042
VAR00033 .843 .245 .213 .028 -.039 .033
VAR00034 .864 .348 .213 .131 .009 .016
VAR00035 .814 .317 .223 .314 .046 -.003
VAR00036 .721 -.223 .278 .013 -.222 -.265
VAR00037 .031 .181 .781 .088 -.103 .292
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 10 iterations.