Current Versus Past DW-NOMINATE Scores
Updated 18 February 2013
House: 1 to 112 vs. 1 to 111 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_111
Source | SS df MS Number of obs = 36634
-------------+------------------------------ F( 1, 36632) = .
Model | 5194.46959 1 5194.46959 Prob > F = 0.0000
Residual | 6.33217396 36632 .000172859 R-squared = 0.9988
-------------+------------------------------ Adj R-squared = 0.9988
Total | 5200.80176 36633 .141970403 Root MSE = .01315
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_111 | .9929292 .0001811 5481.82 0.000 .9925742 .9932842
_cons | .0006376 .0000688 9.27 0.000 .0005029 .0007724
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_111
Source | SS df MS Number of obs = 36634
-------------+------------------------------ F( 1, 36632) = .
Model | 8787.64248 1 8787.64248 Prob > F = 0.0000
Residual | 32.6611903 36632 .000891603 R-squared = 0.9963
-------------+------------------------------ Adj R-squared = 0.9963
Total | 8820.30367 36633 .240774812 Root MSE = .02986
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_111 | .9894514 .0003152 3139.43 0.000 .9888336 .9900691
_cons | .0051238 .0001562 32.79 0.000 .0048175 .00543
------------------------------------------------------------------------------
House: 1 to 112 vs. 1 to 110 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_110
Source | SS df MS Number of obs = 36189
-------------+------------------------------ F( 1, 36187) = .
Model | 5054.92928 1 5054.92928 Prob > F = 0.0000
Residual | 25.7583967 36187 .000711814 R-squared = 0.9949
-------------+------------------------------ Adj R-squared = 0.9949
Total | 5080.68767 36188 .140397029 Root MSE = .02668
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_110 | .9692825 .0003637 2664.86 0.000 .9685696 .9699955
_cons | .0001694 .0001404 1.21 0.228 -.0001058 .0004445
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_110
Source | SS df MS Number of obs = 36189
-------------+------------------------------ F( 1, 36187) = .
Model | 8642.22256 1 8642.22256 Prob > F = 0.0000
Residual | 101.878944 36187 .002815346 R-squared = 0.9883
-------------+------------------------------ Adj R-squared = 0.9883
Total | 8744.1015 36188 .241629864 Root MSE = .05306
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_110 | .9748233 .0005564 1752.05 0.000 .9737328 .9759138
_cons | .0094704 .0002792 33.92 0.000 .0089232 .0100177
------------------------------------------------------------------------------
House: 1 to 112 vs. 1 to 109 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_109
Source | SS df MS Number of obs = 35742
-------------+------------------------------ F( 1, 35740) = .
Model | 4918.76818 1 4918.76818 Prob > F = 0.0000
Residual | 41.2759505 35740 .001154895 R-squared = 0.9917
-------------+------------------------------ Adj R-squared = 0.9917
Total | 4960.04413 35741 .13877743 Root MSE = .03398
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_109 | .9530132 .0004618 2063.75 0.000 .9521081 .9539184
_cons | -.0027246 .00018 -15.14 0.000 -.0030774 -.0023719
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_109
Source | SS df MS Number of obs = 35742
-------------+------------------------------ F( 1, 35740) = .
Model | 8555.96064 1 8555.96064 Prob > F = 0.0000
Residual | 120.493685 35740 .003371396 R-squared = 0.9861
-------------+------------------------------ Adj R-squared = 0.9861
Total | 8676.45433 35741 .242759137 Root MSE = .05806
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_109 | .9590656 .000602 1593.05 0.000 .9578856 .9602456
_cons | .0116577 .0003073 37.93 0.000 .0110552 .0122601
------------------------------------------------------------------------------
House: 1 to 112 vs. 1 to 108 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_108
Source | SS df MS Number of obs = 35303
-------------+------------------------------ F( 1, 35301) = .
Model | 4784.30371 1 4784.30371 Prob > F = 0.0000
Residual | 55.2873005 35301 .001566168 R-squared = 0.9886
-------------+------------------------------ Adj R-squared = 0.9886
Total | 4839.59101 35302 .137091128 Root MSE = .03957
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_108 | .9361791 .0005356 1747.79 0.000 .9351292 .9372289
_cons | -.003585 .0002109 -17.00 0.000 -.0039983 -.0031717
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_108
Source | SS df MS Number of obs = 35303
-------------+------------------------------ F( 1, 35301) = .
Model | 8475.90298 1 8475.90298 Prob > F = 0.0000
Residual | 139.751527 35301 .003958855 R-squared = 0.9838
-------------+------------------------------ Adj R-squared = 0.9838
Total | 8615.6545 35302 .244055705 Root MSE = .06292
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_108 | .9417496 .0006436 1463.22 0.000 .9404881 .9430111
_cons | .0134106 .0003351 40.02 0.000 .0127539 .0140674
------------------------------------------------------------------------------
House: 1 to 112 vs. 1 to 107 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_107
Source | SS df MS Number of obs = 34862
-------------+------------------------------ F( 1, 34860) = .
Model | 4649.08448 1 4649.08448 Prob > F = 0.0000
Residual | 77.6970568 34860 .002228831 R-squared = 0.9836
-------------+------------------------------ Adj R-squared = 0.9836
Total | 4726.78154 34861 .135589385 Root MSE = .04721
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_107 | .9034402 .0006255 1444.26 0.000 .9022141 .9046663
_cons | -.0030716 .0002531 -12.14 0.000 -.0035677 -.0025756
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_107
Source | SS df MS Number of obs = 34862
-------------+------------------------------ F( 1, 34860) = .
Model | 8310.64262 1 8310.64262 Prob > F = 0.0000
Residual | 246.434508 34860 .007069263 R-squared = 0.9712
-------------+------------------------------ Adj R-squared = 0.9712
Total | 8557.07713 34861 .245462756 Root MSE = .08408
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_107 | .9324342 .00086 1084.25 0.000 .9307486 .9341198
_cons | .0144587 .0004505 32.09 0.000 .0135756 .0153417
------------------------------------------------------------------------------
House: 1 to 112 vs. 1 to 106 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_106
Source | SS df MS Number of obs = 34420
-------------+------------------------------ F( 1, 34418) = .
Model | 4504.71107 1 4504.71107 Prob > F = 0.0000
Residual | 116.033662 34418 .003371308 R-squared = 0.9749
-------------+------------------------------ Adj R-squared = 0.9749
Total | 4620.74473 34419 .134249825 Root MSE = .05806
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_106 | .8832626 .0007641 1155.94 0.000 .8817649 .8847602
_cons | -.0026324 .0003132 -8.40 0.000 -.0032462 -.0020185
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_106
Source | SS df MS Number of obs = 34420
-------------+------------------------------ F( 1, 34418) = .
Model | 8109.43423 1 8109.43423 Prob > F = 0.0000
Residual | 386.3253 34418 .011224513 R-squared = 0.9545
-------------+------------------------------ Adj R-squared = 0.9545
Total | 8495.75953 34419 .246833421 Root MSE = .10595
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_106 | .8876834 .0010444 849.99 0.000 .8856364 .8897303
_cons | .0191597 .0005712 33.54 0.000 .0180401 .0202793
------------------------------------------------------------------------------
House: 1 to 112 vs. 1 to 105 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_105
Source | SS df MS Number of obs = 33980
-------------+------------------------------ F( 1, 33978) = .
Model | 4233.5406 1 4233.5406 Prob > F = 0.0000
Residual | 289.039542 33978 .008506667 R-squared = 0.9361
-------------+------------------------------ Adj R-squared = 0.9361
Total | 4522.58014 33979 .133099271 Root MSE = .09223
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_105 | .9569306 .0013565 705.46 0.000 .9542719 .9595893
_cons | -.0040236 .0005008 -8.03 0.000 -.0050051 -.0030421
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_105
Source | SS df MS Number of obs = 33980
-------------+------------------------------ F( 1, 33978) = .
Model | 7372.78108 1 7372.78108 Prob > F = 0.0000
Residual | 1058.96867 33978 .031166304 R-squared = 0.8744
-------------+------------------------------ Adj R-squared = 0.8744
Total | 8431.74975 33979 .248145906 Root MSE = .17654
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_105 | .8886463 .0018271 486.38 0.000 .8850652 .8922274
_cons | .0240209 .0009578 25.08 0.000 .0221436 .0258982
------------------------------------------------------------------------------
Senate: 1 to 112 vs. 1 to 111 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_111
Source | SS df MS Number of obs = 8856
-------------+------------------------------ F( 1, 8854) = .
Model | 1284.07349 1 1284.07349 Prob > F = 0.0000
Residual | 7.05270605 8854 .000796556 R-squared = 0.9945
-------------+------------------------------ Adj R-squared = 0.9945
Total | 1291.1262 8855 .145807589 Root MSE = .02822
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_111 | .9834476 .0007746 1269.66 0.000 .9819292 .9849659
_cons | -.0052175 .0003001 -17.38 0.000 -.0058058 -.0046291
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_111
Source | SS df MS Number of obs = 8856
-------------+------------------------------ F( 1, 8854) = .
Model | 2357.04069 1 2357.04069 Prob > F = 0.0000
Residual | 13.4864048 8854 .001523199 R-squared = 0.9943
-------------+------------------------------ Adj R-squared = 0.9943
Total | 2370.5271 8855 .267704923 Root MSE = .03903
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_111 | .9842843 .0007913 1243.96 0.000 .9827333 .9858353
_cons | -.0050517 .0004152 -12.17 0.000 -.0058655 -.0042378
------------------------------------------------------------------------------
Senate: 1 to 112 vs. 1 to 110 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_110
Source | SS df MS Number of obs = 8748
-------------+------------------------------ F( 1, 8746) = .
Model | 1245.99654 1 1245.99654 Prob > F = 0.0000
Residual | 25.7815099 8746 .002947806 R-squared = 0.9797
-------------+------------------------------ Adj R-squared = 0.9797
Total | 1271.77805 8747 .145395913 Root MSE = .05429
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_110 | .9547608 .0014685 650.14 0.000 .9518821 .9576395
_cons | -.0096051 .0005807 -16.54 0.000 -.0107434 -.0084668
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_110
Source | SS df MS Number of obs = 8748
-------------+------------------------------ F( 1, 8746) = .
Model | 2289.27271 1 2289.27271 Prob > F = 0.0000
Residual | 63.0526463 8746 .007209312 R-squared = 0.9732
-------------+------------------------------ Adj R-squared = 0.9732
Total | 2352.32536 8747 .268929388 Root MSE = .08491
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_110 | .9662736 .0017147 563.51 0.000 .9629123 .9696349
_cons | -.0060424 .0009087 -6.65 0.000 -.0078237 -.004261
------------------------------------------------------------------------------
Senate: 1 to 112 vs. 1 to 109 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_109
Source | SS df MS Number of obs = 8645
-------------+------------------------------ F( 1, 8643) = .
Model | 1217.6162 1 1217.6162 Prob > F = 0.0000
Residual | 35.7306241 8643 .004134053 R-squared = 0.9715
-------------+------------------------------ Adj R-squared = 0.9715
Total | 1253.34682 8644 .144996162 Root MSE = .0643
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_109 | .939643 .0017314 542.71 0.000 .9362491 .943037
_cons | -.0135068 .0006916 -19.53 0.000 -.0148626 -.0121511
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_109
Source | SS df MS Number of obs = 8645
-------------+------------------------------ F( 1, 8643) = .
Model | 2249.33562 1 2249.33562 Prob > F = 0.0000
Residual | 84.5160532 8643 .009778555 R-squared = 0.9638
-------------+------------------------------ Adj R-squared = 0.9638
Total | 2333.85167 8644 .269996723 Root MSE = .09889
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_109 | .9365398 .0019527 479.61 0.000 .9327121 .9403676
_cons | -.0074685 .0010645 -7.02 0.000 -.0095551 -.0053818
------------------------------------------------------------------------------
Senate: 1 to 112 vs. 1 to 108 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_108
Source | SS df MS Number of obs = 8543
-------------+------------------------------ F( 1, 8541) = .
Model | 1180.30466 1 1180.30466 Prob > F = 0.0000
Residual | 55.2650066 8541 .006470555 R-squared = 0.9553
-------------+------------------------------ Adj R-squared = 0.9553
Total | 1235.56967 8542 .144646414 Root MSE = .08044
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_108 | .9202874 .0021548 427.10 0.000 .9160635 .9245112
_cons | -.0156863 .0008704 -18.02 0.000 -.0173924 -.0139801
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_108
Source | SS df MS Number of obs = 8543
-------------+------------------------------ F( 1, 8541) = .
Model | 2191.93393 1 2191.93393 Prob > F = 0.0000
Residual | 124.479079 8541 .014574298 R-squared = 0.9463
-------------+------------------------------ Adj R-squared = 0.9463
Total | 2316.41301 8542 .271179233 Root MSE = .12072
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_108 | .9009382 .0023231 387.81 0.000 .8963842 .9054921
_cons | -.0068739 .0013073 -5.26 0.000 -.0094365 -.0043113
------------------------------------------------------------------------------
Senate: 1 to 112 vs. 1 to 107 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_107
Source | SS df MS Number of obs = 8442
-------------+------------------------------ F( 1, 8440) = .
Model | 1146.46366 1 1146.46366 Prob > F = 0.0000
Residual | 73.7076378 8440 .008733132 R-squared = 0.9396
-------------+------------------------------ Adj R-squared = 0.9396
Total | 1220.17129 8441 .144552931 Root MSE = .09345
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_107 | .8999348 .0024838 362.32 0.000 .895066 .9048037
_cons | -.0174788 .0010172 -17.18 0.000 -.0194727 -.0154849
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_107
Source | SS df MS Number of obs = 8442
-------------+------------------------------ F( 1, 8440) = .
Model | 2126.2418 1 2126.2418 Prob > F = 0.0000
Residual | 170.532433 8440 .020205265 R-squared = 0.9258
-------------+------------------------------ Adj R-squared = 0.9257
Total | 2296.77423 8441 .272097409 Root MSE = .14215
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_107 | .8709172 .0026847 324.39 0.000 .8656544 .8761799
_cons | -.0043815 .0015487 -2.83 0.005 -.0074174 -.0013456
------------------------------------------------------------------------------
Senate: 1 to 112 vs. 1 to 106 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_106
Source | SS df MS Number of obs = 8340
-------------+------------------------------ F( 1, 8338) = .
Model | 1120.36394 1 1120.36394 Prob > F = 0.0000
Residual | 83.2586986 8338 .009985452 R-squared = 0.9308
-------------+------------------------------ Adj R-squared = 0.9308
Total | 1203.62264 8339 .144336568 Root MSE = .09993
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_106 | .9011134 .0026902 334.96 0.000 .8958399 .9063868
_cons | -.0159484 .0010943 -14.57 0.000 -.0180936 -.0138032
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_106
Source | SS df MS Number of obs = 8340
-------------+------------------------------ F( 1, 8338) =89945.62
Model | 2083.72927 1 2083.72927 Prob > F = 0.0000
Residual | 193.162658 8338 .023166546 R-squared = 0.9152
-------------+------------------------------ Adj R-squared = 0.9152
Total | 2276.89193 8339 .273041363 Root MSE = .15221
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_106 | .8275599 .0027594 299.91 0.000 .8221509 .832969
_cons | -.0021191 .0016687 -1.27 0.204 -.0053903 .001152
------------------------------------------------------------------------------
Senate: 1 to 112 vs. 1 to 105 DW-NOMINATE Scalings
Dimension 1 vs. Dimension 1
. regress dwnom1_112 dwnom1_105
Source | SS df MS Number of obs = 8237
-------------+------------------------------ F( 1, 8235) =62827.25
Model | 1049.58878 1 1049.58878 Prob > F = 0.0000
Residual | 137.573486 8235 .016705949 R-squared = 0.8841
-------------+------------------------------ Adj R-squared = 0.8841
Total | 1187.16226 8236 .144143063 Root MSE = .12925
------------------------------------------------------------------------------
dwnom1_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom1_105 | .9075034 .0036205 250.65 0.000 .9004063 .9146006
_cons | -.0201841 .0014242 -14.17 0.000 -.0229759 -.0173924
------------------------------------------------------------------------------
Dimension 2 vs. Dimension 2
. regress dwnom2_112 dwnom2_105
Source | SS df MS Number of obs = 8237
-------------+------------------------------ F( 1, 8235) =47304.53
Model | 1922.90464 1 1922.90464 Prob > F = 0.0000
Residual | 334.748481 8235 .040649482 R-squared = 0.8517
-------------+------------------------------ Adj R-squared = 0.8517
Total | 2257.65312 8236 .274120098 Root MSE = .20162
------------------------------------------------------------------------------
dwnom2_112 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dwnom2_105 | .8310725 .0038211 217.50 0.000 .8235822 .8385628
_cons | -.0058528 .0022234 -2.63 0.008 -.0102113 -.0014942
------------------------------------------------------------------------------
House Correlation Matrix All DW-NOMINATE Scalings
. pwcorr dwnom1_112 dwnom2_112 dwnom1_111 dwnom2_111 dwnom1_110 dwnom2_110 dwnom1_109 dwnom2_109 dwnom1_108 dwnom2_108 dwnom1_107 dwnom2_107 dwnom1_106 dwnom2_106 dwnom1_105 dwnom2_105, sig
| dwnom1~2 dwnom2~2 dwnom1~1 dwnom2~1 dwnom1~0 dwnom2~0 dwnom1~9 dwnom2~9 dwnom1~8 dwnom2~8 dwnom1~7 dwnom2~7 dwnom1~6 dwnom2~6 dwnom1~5 dwnom2~5
-------------+------------------------------------------------------------------------------------------------------------
dwnom1_112 | 1.0000
|
|
dwnom2_112 | -0.0640 1.0000
| 0.0000
|
dwnom1_111 | 0.9994 -0.0677 1.0000
| 0.0000 0.0000
|
dwnom2_111 | -0.0780 0.9981 -0.0784 1.0000
| 0.0000 0.0000 0.0000
|
dwnom1_110 | 0.9975 -0.0700 0.9987 -0.0789 1.0000
| 0.0000 0.0000 0.0000 0.0000
|
dwnom2_110 | -0.0836 0.9942 -0.0836 0.9966 -0.0831 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom1_109 | 0.9958 -0.0759 0.9975 -0.0836 0.9992 -0.0867 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom2_109 | -0.0838 0.9930 -0.0838 0.9951 -0.0836 0.9983 -0.0865 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom1_108 | 0.9943 -0.0807 0.9961 -0.0874 0.9983 -0.0893 0.9995 -0.0886 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom2_108 | -0.0798 0.9919 -0.0800 0.9942 -0.0801 0.9976 -0.0830 0.9984 -0.0850 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom1_107 | 0.9917 -0.0912 0.9935 -0.0972 0.9959 -0.0984 0.9970 -0.0973 0.9973 -0.0948 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom2_107 | -0.0615 0.9855 -0.0619 0.9878 -0.0621 0.9924 -0.0652 0.9937 -0.0673 0.9942 -0.0754 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom1_106 | 0.9874 -0.0984 0.9890 -0.1039 0.9916 -0.1045 0.9926 -0.1034 0.9928 -0.1014 0.9983 -0.0810 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom2_106 | -0.0423 0.9770 -0.0429 0.9792 -0.0433 0.9843 -0.0464 0.9864 -0.0485 0.9876 -0.0563 0.9962 -0.0615 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom1_105 | 0.9675 -0.0989 0.9693 -0.1036 0.9727 -0.1037 0.9744 -0.1024 0.9753 -0.1005 0.9843 -0.0798 0.9895 -0.0602 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom2_105 | 0.0009 0.9351 0.0005 0.9377 0.0006 0.9449 -0.0022 0.9474 -0.0041 0.9494 -0.0097 0.9649 -0.0127 0.9734 -0.0097 1.0000
| 0.8694 0.0000 0.9219 0.0000 0.9063 0.0000 0.6814 0.0000 0.4545 0.0000 0.0736 0.0000 0.0190 0.0000 0.0736
|
Senate Correlation Matrix All DW-NOMINATE Scalings
. pwcorr dwnom1_112 dwnom2_112 dwnom1_111 dwnom2_111 dwnom1_110 dwnom2_110 dwnom1_109 dwnom2_109 dwnom1_108 dwnom2_108 dwnom1_107 dwnom2_107 dwnom1_106 dwnom2_106 dwnom1_105 dwnom2_105, sig
| dwnom1~2 dwnom2~2 dwnom1~1 dwnom2~1 dwnom1~0 dwnom2~0 dwnom1~9 dwnom2~9 dwnom1~8 dwnom2~8 dwnom1~7 dwnom2~7 dwnom1~6 dwnom2~6 dwnom1~5 dwnom2~5
-------------+------------------------------------------------------------------------------------------------------------
dwnom1_112 | 1.0000
|
|
dwnom2_112 | -0.0234 1.0000
| 0.0268
|
dwnom1_111 | 0.9973 -0.0258 1.0000
| 0.0000 0.0154
|
dwnom2_111 | -0.0294 0.9972 -0.0340 1.0000
| 0.0056 0.0000 0.0014
|
dwnom1_110 | 0.9898 -0.0168 0.9962 -0.0263 1.0000
| 0.0000 0.1154 0.0000 0.0141
|
dwnom2_110 | -0.0376 0.9865 -0.0422 0.9931 -0.0372 1.0000
| 0.0004 0.0000 0.0001 0.0000 0.0005
|
dwnom1_109 | 0.9856 -0.0147 0.9938 -0.0251 0.9991 -0.0365 1.0000
| 0.0000 0.1717 0.0000 0.0196 0.0000 0.0007
|
dwnom2_109 | -0.0412 0.9817 -0.0462 0.9898 -0.0414 0.9986 -0.0423 1.0000
| 0.0001 0.0000 0.0000 0.0000 0.0001 0.0000 0.0001
|
dwnom1_108 | 0.9774 -0.0127 0.9878 -0.0238 0.9956 -0.0353 0.9981 -0.0413 1.0000
| 0.0000 0.2396 0.0000 0.0280 0.0000 0.0011 0.0000 0.0001
|
dwnom2_108 | -0.0444 0.9728 -0.0495 0.9825 -0.0450 0.9942 -0.0461 0.9975 -0.0460 1.0000
| 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
dwnom1_107 | 0.9693 -0.0033 0.9816 -0.0155 0.9914 -0.0279 0.9944 -0.0348 0.9966 -0.0407 1.0000
| 0.0000 0.7619 0.0000 0.1534 0.0000 0.0102 0.0000 0.0014 0.0000 0.0002
|
dwnom2_107 | -0.0397 0.9622 -0.0432 0.9725 -0.0376 0.9871 -0.0378 0.9910 -0.0363 0.9943 -0.0320 1.0000
| 0.0003 0.0000 0.0001 0.0000 0.0006 0.0000 0.0005 0.0000 0.0009 0.0000 0.0033
|
dwnom1_106 | 0.9648 0.0093 0.9770 -0.0038 0.9866 -0.0170 0.9891 -0.0245 0.9899 -0.0317 0.9971 -0.0229 1.0000
| 0.0000 0.3946 0.0000 0.7299 0.0000 0.1214 0.0000 0.0252 0.0000 0.0038 0.0000 0.0366
|
dwnom2_106 | -0.0406 0.9566 -0.0430 0.9667 -0.0369 0.9806 -0.0364 0.9845 -0.0344 0.9879 -0.0301 0.9962 -0.0231 1.0000
| 0.0002 0.0000 0.0001 0.0000 0.0008 0.0000 0.0009 0.0000 0.0017 0.0000 0.0060 0.0000 0.0349
|
dwnom1_105 | 0.9403 0.0008 0.9542 -0.0135 0.9654 -0.0266 0.9686 -0.0347 0.9705 -0.0428 0.9836 -0.0331 0.9898 -0.0336 1.0000
| 0.0000 0.9385 0.0000 0.2220 0.0000 0.0159 0.0000 0.0017 0.0000 0.0001 0.0000 0.0026 0.0000 0.0023
|
dwnom2_105 | -0.0220 0.9229 -0.0226 0.9335 -0.0158 0.9499 -0.0144 0.9534 -0.0112 0.9563 -0.0058 0.9725 0.0016 0.9764 -0.0085 1.0000
| 0.0458 0.0000 0.0403 0.0000 0.1528 0.0000 0.1913 0.0000 0.3116 0.0000 0.5993 0.0000 0.8830 0.0000 0.4388
|