我想知道几个问题能不能用来measure一个variable。 factor analysis的结果是 -------------------------------------------------------------------------- Factor Eigenvalue Difference Proportion Cumulative -------------+------------------------------------------------------------ Factor1 4.17977 3.34728 0.9516 0.9516 Factor2 0.83249 0.80832 0.1895 1.1411 Factor3 0.02416 0.03348 0.0055 1.1466 Factor4 -0.00932 0.04828 -0.0021 1.1445 Factor5 -0.05760 0.00238 -0.0131 1.1314 Factor6 -0.05998 0.05057 -0.0137 1.1177 Factor7 -0.11055 0.01519 -0.0252 1.0925 Factor8 -0.12574 0.00724 -0.0286 1.0639 Factor9 -0.13297 0.01477 -0.0303 1.0336 Factor10 -0.14775 . -0.0336 1.0000 -------------------------------------------------------------------------- LR test: independent vs. saturated: chi2(45) = 9.2e+04 Prob>chi2 = 0.0000 Factor loadings (pattern matrix) and unique variances ----------------------------------------------------------- Variable Factor1 Factor2 Factor3 Uniqueness -------------+------------------------------+-------------- a8d 0.5476 0.3270 0.0322 0.5921 a8e 0.5838 0.3287 0.0358 0.5499 a8f 0.4762 0.2980 0.0260 0.6838 a8g 0.6444 0.3554 -0.0331 0.4574 a7a 0.6822 -0.1993 -0.0726 0.4897 a7b 0.7161 -0.2563 -0.0392 0.4200 a7c 0.7326 -0.3071 -0.0175 0.3687 a7d 0.7363 -0.2978 0.0532 0.3663 a7e 0.6294 -0.2284 0.0873 0.5440 a8b 0.6664 0.2486 -0.0500 0.4917 ----------------------------------------------------------- rotate以后是 Factor analysis/correlation Number of obs = 22451 Method: principal factors Retained factors = 3 Rotation: orthogonal varimax (Horst off) Number of params = 27 -------------------------------------------------------------------------- Factor | Variance Difference Proportion Cumulative -------------+------------------------------------------------------------ Factor1 | 2.90073 0.78948 0.6604 0.6604 Factor2 | 2.11125 2.08682 0.4806 1.1410 Factor3 | 0.02443 . 0.0056 1.1466 -------------------------------------------------------------------------- LR test: independent vs. saturated: chi2(45) = 9.2e+04 Prob>chi2 = 0.0000 Rotated factor loadings (pattern matrix) and unique variances ----------------------------------------------------------- Variable | Factor1 Factor2 Factor3 | Uniqueness -------------+------------------------------+-------------- a8d | 0.2283 0.5952 0.0391 | 0.5921 a8e | 0.2557 0.6188 0.0430 | 0.5499 a8f | 0.1901 0.5283 0.0321 | 0.6838 a8g | 0.2868 0.6780 -0.0253 | 0.4574 a7a | 0.6594 0.2659 -0.0699 | 0.4897 a7b | 0.7213 0.2416 -0.0369 | 0.4200 a7c | 0.7657 0.2117 -0.0156 | 0.3687 a7d | 0.7629 0.2205 0.0553 | 0.3663 a7e | 0.6360 0.2084 0.0894 | 0.5440 a8b | 0.3701 0.6079 -0.0430 | 0.4917 ----------------------------------------------------------- Factor rotation matrix ----------------------------------------- | Factor1 Factor2 Factor3 -------------+--------------------------- Factor1 | 0.7861 0.6181 0.0068 Factor2 | -0.6182 0.7860 0.0098 Factor3 | 0.0007 -0.0119 0.9999 ----------------------------------------- 所以,我到底是要用rotate之前的结果还是rotate之后的啊?那这几个能不能放在一块儿用来measure一个variable 啊? 可以的话,接下来是不是直接用predict的命令就可以了? |


雷达卡



京公网安备 11010802022788号







