I am working with a large survey data (N=220,000). The survey asks how often people use reading and writing skills at work by asking 12 different questions. These are the 12 categorical variables G_Q* in the data enclosed below (with value levels). Sample weights are in the variable WT.
I want to make *one* index for the use of literacy skills at work using these 12 variables. The best method I could find online is making a principal components index using polychoric correlations. I do this using the following code. I also provide the output for completeness. While the code runs without error, producing a polychoric correlation matrix and executing the principal component analysis, I get a warning: "convergence not achieved". When I don't use sample weights [pw=WT], I no longer get this warning message.
Note: running the code on the data extract I provide below does not produce any warning. But the same code does produce a warning on the full data.
- I am new to PCA. What does it mean to not achieve convergence? What can I do to achieve convergence? What does this have to do with sampling weights?
- If convergence is not achieved, is the resulting PCA invalid?
- PCA with polychoric correlations is just one method I found online. Is there a better way to create *one* index from a high dimensional set of categorical variables, like in my example?
Thanks.
Code:
polychoricpca G_Q01A G_Q01B G_Q01C G_Q01D G_Q01E G_Q01F G_Q01G G_Q01H G_Q02A G_Q02B G_Q02C G_Q02D [pw=WT], score(pca) nscore(1)
convergence not achieved
Polychoric correlation matrix
G_Q01A G_Q01B G_Q01C G_Q01D G_Q01E
G_Q01A 1
G_Q01B .55846902 1
G_Q01C .42631142 .67224243 1
G_Q01D .45070461 .65548802 .76658735 1
G_Q01E .34250992 .45084042 .56178017 .59155061 1
G_Q01F .59469005 .56052551 .49759875 .55588562 .53441044
G_Q01G .27126738 .52399025 .42723118 .38754332 .23691087
G_Q01H .47336072 .54266279 .45239904 .48713368 .37947737
G_Q02A .45680246 .88212333 .60576374 .61159295 .43619415
G_Q02B .25495252 .51072115 .53469256 .55277812 .48291761
G_Q02C .42820831 .55988434 .42479452 .46075696 .38452038
G_Q02D .47989936 .58308406 .39359131 .40248829 .29558057
G_Q01F G_Q01G G_Q01H G_Q02A G_Q02B
G_Q01F 1
G_Q01G .30743004 1
G_Q01H .53347603 .34539894 1
G_Q02A .50425125 .50169205 .50907761 1
G_Q02B .34467389 .29900805 .36191432 .55356011 1
G_Q02C .47497015 .28889701 .45570917 .60108939 .40060757
G_Q02D .49022314 .38242366 .45735662 .57974156 .28189509
G_Q02C G_Q02D
G_Q02C 1
G_Q02D .63773768 1
Principal component analysis
k | Eigenvalues | Proportion explained | Cum. explained
----+---------------+------------------------+------------------
1 | 6.330187 | 0.527516 | 0.527516
2 | 1.104438 | 0.092037 | 0.619552
3 | 0.925240 | 0.077103 | 0.696655
4 | 0.728330 | 0.060694 | 0.757350
5 | 0.548358 | 0.045697 | 0.803046
6 | 0.535607 | 0.044634 | 0.847680
7 | 0.451277 | 0.037606 | 0.885286
8 | 0.386905 | 0.032242 | 0.917528
9 | 0.337059 | 0.028088 | 0.945617
10 | 0.326661 | 0.027222 | 0.972838
11 | 0.225979 | 0.018832 | 0.991670
12 | 0.099960 | 0.008330 | 1.000000
Scoring coefficients
Variable | Coeff. 1 | Coeff. 2 | Coeff. 3
------------------------------------------------------
G_Q01A
1 | -0.342112 | -0.413705 | -0.468062
2 | -0.129872 | -0.157050 | -0.177685
3 | -0.038684 | -0.046780 | -0.052926
4 | 0.058973 | 0.071314 | 0.080684
5 | 0.293034 | 0.354357 | 0.400915
G_Q01B
1 | -0.386800 | -0.060226 | 0.245954
2 | -0.130805 | -0.020367 | 0.083175
3 | -0.080628 | -0.012554 | 0.051269
4 | -0.011845 | -0.001844 | 0.007532
5 | 0.300556 | 0.046798 | -0.191114
G_Q01C
1 | -0.307872 | 0.323132 | 0.032328
2 | -0.045241 | 0.047483 | 0.004751
3 | 0.035355 | -0.037108 | -0.003712
4 | 0.155904 | -0.163632 | -0.016371
5 | 0.423643 | -0.444641 | -0.044485
G_Q01D
1 | -0.266657 | 0.261569 | -0.060142
2 | 0.041008 | -0.040225 | 0.009249
3 | 0.158926 | -0.155893 | 0.035844
4 | 0.322979 | -0.316816 | 0.072845
5 | 0.597503 | -0.586102 | 0.134761
G_Q01E
1 | -0.165424 | 0.251300 | -0.229450
2 | 0.124093 | -0.188514 | 0.172123
3 | 0.220295 | -0.334657 | 0.305558
4 | 0.310384 | -0.471513 | 0.430515
5 | 0.486489 | -0.739040 | 0.674781
G_Q01F
1 | -0.322755 | -0.130053 | -0.467717
2 | -0.052821 | -0.021284 | -0.076545
3 | 0.078669 | 0.031699 | 0.114003
4 | 0.217752 | 0.087742 | 0.315553
5 | 0.461859 | 0.186104 | 0.669297
G_Q01G
1 | -0.173600 | -0.042452 | 0.471671
2 | 0.033866 | 0.008282 | -0.092013
3 | 0.090740 | 0.022190 | -0.246540
4 | 0.163753 | 0.040045 | -0.444917
5 | 0.330233 | 0.080756 | -0.897243
G_Q01H
1 | -0.213112 | -0.131207 | -0.122083
2 | 0.051536 | 0.031729 | 0.029523
3 | 0.130155 | 0.080133 | 0.074560
4 | 0.221036 | 0.136085 | 0.126622
5 | 0.424630 | 0.261432 | 0.243252
G_Q02A
1 | -0.336591 | -0.041337 | 0.300412
2 | -0.074822 | -0.009189 | 0.066779
3 | -0.026465 | -0.003250 | 0.023620
4 | 0.044145 | 0.005422 | -0.039400
5 | 0.331005 | 0.040651 | -0.295426
G_Q02B
1 | -0.061691 | 0.104820 | 0.032386
2 | 0.340202 | -0.578038 | -0.178597
3 | 0.441758 | -0.750591 | -0.231912
4 | 0.529965 | -0.900465 | -0.278218
5 | 0.675229 | -1.147282 | -0.354478
G_Q02C
1 | -0.233182 | -0.234828 | -0.005294
2 | 0.027955 | 0.028152 | 0.000635
3 | 0.114893 | 0.115704 | 0.002608
4 | 0.218999 | 0.220545 | 0.004972
5 | 0.430918 | 0.433960 | 0.009783
G_Q02D
1 | -0.292278 | -0.501917 | 0.087116
2 | -0.066430 | -0.114078 | 0.019800
3 | 0.013132 | 0.022552 | -0.003914
4 | 0.103927 | 0.178470 | -0.030976
5 | 0.328963 | 0.564915 | -0.098050Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(G_Q01A G_Q01B G_Q01C G_Q01D G_Q01E G_Q01F G_Q01G G_Q01H G_Q02A G_Q02B G_Q02C G_Q02D WT) 5 5 4 4 3 5 5 5 5 1 2 5 670.3777 5 5 2 2 1 4 1 4 5 1 5 5 1474.5376 5 2 5 3 1 3 5 1 5 1 1 1 1480.1084 4 5 5 4 2 2 5 4 4 2 3 3 1064.5608 1 1 1 1 1 1 1 1 1 1 1 1 1356.1427 5 5 5 5 4 4 2 5 5 1 4 5 697.5229 3 2 1 2 1 1 1 2 3 1 1 1 862.6973 4 4 5 4 1 3 5 4 3 1 1 2 1248.006 3 5 4 3 2 2 4 4 5 1 3 3 824.368 1 1 1 1 1 1 1 1 1 1 1 1 1874.0227 5 4 5 4 1 4 4 4 4 1 2 3 488.0146 5 5 2 2 1 2 1 1 4 1 2 2 1322.504 5 5 1 1 1 2 1 3 3 1 5 5 1304.4955 1 2 1 2 3 2 4 1 3 1 5 4 534.6276 5 5 1 4 1 5 1 5 5 1 1 2 544.7978 5 1 1 1 1 4 1 4 1 1 1 4 1097.4651 1 1 1 1 5 1 1 1 1 1 1 4 1341.5585 5 5 3 1 1 4 1 2 5 1 4 1 747.2416 5 5 1 4 5 4 1 1 5 1 5 1 1994.5953 5 5 1 1 1 2 1 1 5 1 1 5 755.1249 1 1 1 1 1 1 1 1 1 1 1 1 1637.1313 5 4 1 1 1 4 1 5 4 1 3 5 1093.3983 2 5 5 2 1 2 4 1 4 1 1 2 862.6757 5 5 5 4 2 4 4 5 5 2 5 5 915.0981 5 5 5 5 4 5 5 4 5 1 4 4 860.2231 5 5 4 3 2 4 2 3 1 1 5 2 1127.2556 3 5 5 4 4 3 4 3 5 3 3 4 778.3862 5 5 2 1 1 1 5 3 5 1 1 4 658.481 4 5 5 3 2 2 3 4 5 1 2 5 703.714 1 1 1 1 1 1 1 4 1 1 2 2 1635.737 2 1 1 1 1 1 3 1 1 1 1 5 1245.5356 5 5 5 5 1 5 5 5 5 1 5 3 1189.4763 5 5 5 3 3 2 5 5 5 1 4 4 958.0001 5 4 5 5 3 1 1 5 5 1 4 5 1252.147 4 1 1 1 1 3 1 1 1 1 1 1 949.5825 2 5 4 4 4 2 4 4 4 2 5 4 986.7715 2 5 2 3 1 2 3 2 5 1 3 2 893.688 5 5 1 1 1 3 1 1 5 1 1 5 1621.4678 5 5 4 1 1 4 3 5 5 2 2 3 923.4249 5 5 1 1 1 1 1 1 1 1 5 1 1343.4316 4 5 3 4 2 3 1 4 5 1 3 2 1049.4156 5 5 1 1 1 1 1 5 1 1 5 5 1378.893 5 1 1 1 1 1 1 1 2 1 1 1 849.3949 1 1 1 1 3 1 5 1 1 1 1 4 1450.1848 4 1 1 1 1 2 1 1 1 1 1 1 1017.3097 1 1 1 1 1 1 1 1 1 1 1 1 1030.2325 5 5 1 1 1 3 1 5 3 1 3 3 1320.2722 4 5 3 2 1 2 3 1 2 1 1 2 1113.2659 5 5 2 2 2 3 1 5 5 1 5 5 1013.8658 4 4 3 1 1 1 3 3 4 1 1 2 1138.4254 5 5 1 1 1 2 1 2 5 1 5 5 1163.7865 5 2 1 2 1 2 1 5 1 1 1 5 1060.8427 5 1 2 3 2 4 1 2 2 1 1 1 1166.6366 3 5 2 2 1 3 5 4 5 1 3 3 1333.4493 2 5 3 1 1 2 4 4 5 1 1 2 1495.5415 2 5 3 5 4 2 1 3 5 1 2 2 845.4944 4 5 5 5 4 4 2 4 5 4 5 5 795.5335 1 1 2 1 1 2 1 4 1 1 1 1 710.6758 5 5 4 1 1 3 1 4 3 1 1 3 1570.0457 1 1 1 1 1 1 1 1 1 1 1 2 1350.4603 1 5 2 1 1 2 1 4 5 1 5 4 1245.5356 4 1 1 1 1 4 1 1 1 1 1 1 985.5627 3 5 5 4 2 2 3 2 2 1 1 2 1248.2083 4 5 5 4 2 3 4 4 5 2 5 5 1863.6233 3 5 3 3 1 5 5 1 3 1 1 3 1091.4998 2 5 5 5 5 5 4 5 5 2 5 5 754.7754 2 4 4 4 4 3 3 2 4 2 3 2 958.0001 4 5 4 3 1 3 4 3 4 1 2 3 1302.5463 4 5 3 2 1 5 1 1 5 1 3 2 1113.2659 5 4 1 1 1 2 1 4 3 1 2 5 1003.9622 3 2 1 2 1 3 4 4 2 1 1 2 1222.442 1 5 5 4 5 5 5 2 5 1 2 5 1201.3804 5 5 1 3 1 5 1 3 5 1 5 4 1118.7402 5 5 1 3 2 5 1 5 3 1 5 5 1166.6366 3 4 4 3 3 2 3 3 5 1 3 3 1038.4982 2 2 4 1 1 2 1 1 1 1 1 2 1736.6432 5 5 5 2 2 3 5 1 5 2 1 2 1416.138 3 5 5 5 5 3 1 2 5 2 5 2 1141.8723 5 5 4 4 2 5 1 5 5 1 3 3 540.10284 2 5 4 4 1 2 5 2 5 1 1 5 865.8524 1 5 4 2 1 1 5 5 5 1 4 2 312.7036 4 4 4 3 1 5 2 5 4 1 4 4 1102.532 5 5 5 5 2 1 4 1 5 3 3 3 898.0923 1 5 3 3 1 1 1 1 5 1 1 5 830.7625 5 5 4 3 2 3 5 2 5 1 2 2 1154.9568 3 1 5 1 1 1 1 5 1 1 1 1 1509.3463 5 1 1 1 1 1 4 1 1 1 2 4 1412.1627 3 5 4 3 2 2 5 3 5 1 1 2 933.4961 1 1 1 1 1 1 1 1 1 1 1 1 1275.919 5 5 4 2 2 2 1 1 1 1 1 1 1679.76 5 2 1 1 1 1 1 5 5 1 1 2 975.3658 5 5 1 3 3 1 5 5 2 1 4 3 710.1245 5 5 5 4 1 5 1 5 5 2 4 4 1142.1385 5 5 5 5 4 4 5 5 5 4 4 2 1559.4113 5 5 5 4 2 3 3 4 5 3 4 4 711.0385 4 5 5 5 5 5 3 4 5 5 5 3 1570.0457 4 2 4 4 1 2 1 2 1 1 1 2 1427.972 5 5 5 4 2 2 5 3 5 2 3 4 1112.0211 1 1 1 1 1 1 1 1 1 1 1 1 1202.7153 3 4 2 2 2 3 1 2 5 1 4 2 633.3771 2 5 3 3 2 2 5 2 5 3 3 2 1765.0685 5 5 5 4 1 3 5 3 5 5 5 3 1154.407 5 5 4 3 1 5 3 4 5 3 4 2 1096.2896 4 5 1 1 1 4 4 5 1 1 4 4 1086.3383 4 4 5 3 2 4 3 2 3 2 5 2 1248.2083 4 2 1 1 1 3 2 2 3 1 1 2 997.721 3 3 3 3 2 3 1 2 3 1 5 5 1172.2534 5 4 1 1 1 4 1 1 5 1 1 1 905.6619 5 5 3 2 1 4 1 5 5 1 1 3 706.9456 5 1 5 1 1 5 1 1 1 1 1 1 926.6711 1 1 1 1 1 1 1 1 5 1 1 1 820.6309 5 5 1 1 1 4 5 1 4 1 1 5 1016.6126 5 5 2 2 1 1 1 1 5 1 1 1 1026.8401 4 4 2 1 1 4 1 4 1 1 1 5 869.4208 5 5 5 3 2 3 5 4 5 2 3 4 696.1805 1 5 3 4 1 1 4 1 3 1 1 3 1171.6075 5 4 2 3 2 5 3 5 3 1 4 4 1390.6957 5 2 5 2 1 4 5 2 2 1 1 4 1278.557 4 4 3 3 2 2 1 1 2 1 4 4 1799.5986 5 5 4 4 2 2 1 1 2 1 5 5 1043.791 2 1 3 2 1 1 4 1 1 1 1 1 927.9819 1 1 1 1 1 1 3 3 2 1 2 1 1026.8401 5 5 3 4 2 5 5 4 5 1 5 5 561.1263 5 5 4 1 1 3 1 5 5 1 5 5 1349.911 3 4 5 4 1 3 4 3 4 1 1 3 799.8239 5 5 5 4 1 1 1 1 4 1 1 5 1054.6245 1 1 1 1 1 1 1 1 1 1 1 1 1033.1786 4 5 5 4 3 3 4 2 5 2 2 5 1329.9496 2 2 4 4 1 1 1 2 2 1 2 2 1655.1737 1 1 1 1 1 5 1 1 1 1 1 1 1509.3463 2 4 3 3 1 1 5 1 5 1 2 3 1105.469 4 1 1 1 1 1 1 4 1 1 1 1 1033.8688 5 5 3 3 1 5 5 3 5 3 3 4 753.2714 5 5 5 5 1 5 3 5 5 2 5 5 1652.5264 4 5 5 5 3 1 1 4 5 5 5 5 705.0697 5 5 5 4 1 2 5 5 5 1 5 2 951.4235 2 3 3 4 5 2 1 2 5 1 5 3 1157.4608 3 5 5 4 2 3 4 3 5 2 3 5 657.3076 2 5 5 3 2 2 5 3 5 2 3 2 614.6135 2 2 1 2 1 1 1 1 1 1 1 1 654.9514 5 5 4 3 3 4 5 5 5 1 4 4 1282.5227 5 5 5 4 2 2 4 2 5 2 4 3 1396.2556 3 5 1 1 1 1 3 2 5 2 2 1 705.0764 1 1 1 1 1 3 3 1 1 1 2 1 1898.487 3 1 1 1 1 1 1 5 1 1 1 5 1556.9008 3 3 4 4 1 4 3 4 4 1 1 2 1232.802 2 5 2 2 1 2 5 4 5 1 2 4 1778.042 4 5 5 4 3 3 3 3 5 3 2 3 846.9681 2 1 1 1 1 2 1 2 2 1 2 2 862.6973 5 4 4 4 3 5 4 5 5 1 5 4 896.2487 5 5 5 4 2 5 5 4 5 1 4 2 1740.0518 5 3 1 1 1 2 1 1 5 1 1 5 1271.54 5 4 3 3 2 1 5 2 3 1 3 5 969.6646 4 5 4 2 1 2 5 4 5 1 4 4 1208.1257 5 5 1 1 1 1 1 1 5 1 1 5 1424.29 5 5 5 4 1 5 5 4 5 1 4 4 757.2543 1 3 3 4 1 2 3 1 1 1 1 5 1994.5953 5 5 1 1 1 1 1 5 4 1 5 1 1120.931 5 1 1 1 1 2 1 1 1 1 1 2 1578.2784 2 3 1 1 1 1 3 2 3 1 2 3 1675.4478 1 4 4 4 5 2 1 1 5 2 2 4 553.89606 5 5 2 3 1 3 5 2 5 1 1 5 956.9728 3 5 4 3 1 2 1 1 5 1 3 4 1222.8916 5 1 3 1 1 1 1 1 5 1 1 5 1032.231 5 5 5 4 1 3 5 5 5 2 1 3 850.9202 2 5 4 4 3 4 4 3 2 1 2 3 1020.3174 5 5 4 3 1 5 1 4 1 1 5 5 1077.5919 2 4 4 4 4 3 4 3 4 1 3 2 1302.1807 3 4 5 4 1 3 4 4 4 1 1 3 642.8826 5 5 4 4 1 4 1 2 4 1 1 3 1053.9055 5 5 4 2 1 3 1 2 4 1 1 5 1374.9136 4 5 4 3 2 4 1 4 5 1 4 3 1556.535 1 4 4 3 4 3 4 3 5 2 2 2 997.8046 1 1 1 1 1 1 1 1 1 1 1 1 1765.4777 5 5 2 2 2 2 1 5 5 1 1 2 905.0423 5 4 4 4 2 5 5 5 5 1 5 5 1776.084 5 5 5 3 1 3 3 1 5 1 1 5 1094.1616 2 5 5 4 3 3 2 2 5 4 1 2 1103.8457 2 5 5 4 2 4 4 2 5 2 2 2 722.2988 5 5 4 1 1 2 5 2 5 2 3 4 1566.508 5 5 5 2 1 4 5 5 5 1 5 5 1304.4955 4 5 2 2 1 5 1 4 5 1 1 3 739.7717 2 1 1 1 1 2 1 4 1 1 1 1 806.0541 5 5 1 1 1 3 1 5 1 1 1 1 597.4919 1 1 1 1 2 2 1 2 4 1 1 4 857.576 5 5 5 4 3 5 1 2 5 2 3 3 767.5212 1 1 1 1 1 5 1 1 1 1 1 1 830.862 5 1 1 4 3 5 1 3 1 1 1 2 935.6891 1 1 1 3 1 1 1 1 1 1 1 1 1290.099 1 1 1 2 1 1 1 5 1 1 1 5 1946.581 3 1 1 2 1 4 3 4 2 1 1 3 1004.8798 1 1 1 1 1 1 1 1 1 1 1 1 799.8239 4 5 5 4 3 4 2 4 5 2 3 3 1141.8723 3 5 3 2 2 2 3 5 5 1 3 4 540.10284 1 5 5 2 5 5 3 1 5 1 1 2 1509.3463 1 5 1 1 1 2 5 2 5 1 1 5 905.3553 5 5 4 4 1 3 2 1 5 1 1 4 753.2714 1 5 1 4 1 4 1 1 4 1 1 2 690.6699 2 5 2 1 1 2 5 3 5 1 4 5 1425.7312 5 1 5 4 2 4 4 4 1 1 4 2 1052.9564 end label values G_Q01A G_Q01A label def G_Q01A 1 "Never", modify label def G_Q01A 2 "Less than once a month", modify label def G_Q01A 3 "Less than once a week but at least once a month", modify label def G_Q01A 4 "At least once a week but not every day", modify label def G_Q01A 5 "Every day", modify label values G_Q01B G_Q01B label def G_Q01B 1 "Never", modify label def G_Q01B 2 "Less than once a month", modify label def G_Q01B 3 "Less than once a week but at least once a month", modify label def G_Q01B 4 "At least once a week but not every day", modify label def G_Q01B 5 "Every day", modify label values G_Q01C G_Q01C label def G_Q01C 1 "Never", modify label def G_Q01C 2 "Less than once a month", modify label def G_Q01C 3 "Less than once a week but at least once a month", modify label def G_Q01C 4 "At least once a week but not every day", modify label def G_Q01C 5 "Every day", modify label values G_Q01D G_Q01D label def G_Q01D 1 "Never", modify label def G_Q01D 2 "Less than once a month", modify label def G_Q01D 3 "Less than once a week but at least once a month", modify label def G_Q01D 4 "At least once a week but not every day", modify label def G_Q01D 5 "Every day", modify label values G_Q01E G_Q01E label def G_Q01E 1 "Never", modify label def G_Q01E 2 "Less than once a month", modify label def G_Q01E 3 "Less than once a week but at least once a month", modify label def G_Q01E 4 "At least once a week but not every day", modify label def G_Q01E 5 "Every day", modify label values G_Q01F G_Q01F label def G_Q01F 1 "Never", modify label def G_Q01F 2 "Less than once a month", modify label def G_Q01F 3 "Less than once a week but at least once a month", modify label def G_Q01F 4 "At least once a week but not every day", modify label def G_Q01F 5 "Every day", modify label values G_Q01G G_Q01G label def G_Q01G 1 "Never", modify label def G_Q01G 2 "Less than once a month", modify label def G_Q01G 3 "Less than once a week but at least once a month", modify label def G_Q01G 4 "At least once a week but not every day", modify label def G_Q01G 5 "Every day", modify label values G_Q01H G_Q01H label def G_Q01H 1 "Never", modify label def G_Q01H 2 "Less than once a month", modify label def G_Q01H 3 "Less than once a week but at least once a month", modify label def G_Q01H 4 "At least once a week but not every day", modify label def G_Q01H 5 "Every day", modify label values G_Q02A G_Q02A label def G_Q02A 1 "Never", modify label def G_Q02A 2 "Less than once a month", modify label def G_Q02A 3 "Less than once a week but at least once a month", modify label def G_Q02A 4 "At least once a week but not every day", modify label def G_Q02A 5 "Every day", modify label values G_Q02B G_Q02B label def G_Q02B 1 "Never", modify label def G_Q02B 2 "Less than once a month", modify label def G_Q02B 3 "Less than once a week but at least once a month", modify label def G_Q02B 4 "At least once a week but not every day", modify label def G_Q02B 5 "Every day", modify label values G_Q02C G_Q02C label def G_Q02C 1 "Never", modify label def G_Q02C 2 "Less than once a month", modify label def G_Q02C 3 "Less than once a week but at least once a month", modify label def G_Q02C 4 "At least once a week but not every day", modify label def G_Q02C 5 "Every day", modify label values G_Q02D G_Q02D label def G_Q02D 1 "Never", modify label def G_Q02D 2 "Less than once a month", modify label def G_Q02D 3 "Less than once a week but at least once a month", modify label def G_Q02D 4 "At least once a week but not every day", modify label def G_Q02D 5 "Every day", modify
0 Response to convergence warning in PCA using polychoric correlation
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