Current Versus Past DW-NOMINATE Scores

Updated 9 January 2011


The STATA and Excel files below combine current legislator scores (HL01111E21_PRES.DAT for the House; SL01111E21.DAT for the Senate) with the past six releases of the DW-NOMINATE legislator scores (HL01105D.SRT, HL01106C.DAT, HL01107A1.DAT, HL01108A1_PRES.DAT, HL01109A21_PRES.DAT, and HL01110D21_PRES_NEW.DAT for the House; SL01105C.DAT, SL01106D.DAT, SL01107A1.DAT, SL01108A1.DAT, SL01109B21.DAT, and SL01110C21_NEW.DAT the Senate). The DW-NOMINATE scalings for Congresses 1 - 105 were done in late 1998 using an early version of DW-NOMINATE. Because of computer limitations, this early version (1996-98) had a clumsy design that necessitated running the legislator, roll call, and utility function parameters in separate computer programs. Each program read the results from the previous one -- DW-NOMINATE was in fact a battery of programs. Also, given that only 100 - 200mhz machines were available at the time this early version was developed meant that there had to be some tradeoffs between precision and computer time.

In 2000 we developed a much improved version of DW-NOMINATE that does not have the limitations of the original battery of programs. DW-NOMINATE is now a stand-alone program like our original D-NOMINATE Program and it runs very efficiently on current high-speed PCs. The past six releases are from this version.

Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata File, 36,634 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 36,634 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (EVIEWS File, 36,634 lines)

Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata File, 8,856 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 8,856 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (EVIEWS File, 8,856 lines)

Below are the results of regressing the current dimensions on the corresponding dimensions of the previous releases. The r-squares for the current House with the 1 to 110 House release are .998 for the first dimension and .993 for the second. The corresponding r-squares for the Senate are .992 and .986, respectively. The regression tables give the mapping of the 1 - 110 into the current release for the House and Senate.

The r-squares for the current House with the 1 to 105 House scaling released in late 1998 are .940 for the first dimension and .879 for the second. The corresponding r-squares for the Senate are .911 and .871, respectively. These r-squares are lower for the reasons given above. The regression tables give the mapping of the 1 - 105 into the current release.

As noted on the DW-NOMINATE Scores Page, when a new Congress is added to the dataset this will slightly change the scores for more recent members because their scores are estimated using their entire voting history. This will also slightly change the overall means of the dimensions. In addition, the past few Congresses are nearly unidimensional with correct classifications of 90 percent or better. Consequently, the overall fit of the DW-NOMINATE estimation has increased as recent Congresses have been added to the dataset. Consequently, the r-squares of the 1 to 111 coordinates with previous releases decline slightly with the decline being greater the earlier the release.



House: 1 to 111 vs. 1 to 110 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_110

      Source |       SS       df       MS              Number of obs =   36189
-------------+------------------------------           F(  1, 36187) =       .
       Model |  5139.76402     1  5139.76402           Prob > F      =  0.0000
    Residual |  12.9195221 36187  .000357021           R-squared     =  0.9975
-------------+------------------------------           Adj R-squared =  0.9975
       Total |  5152.68355 36188  .142386524           Root MSE      =   .0189

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_110 |   .9773822   .0002576  3794.24   0.000     .9768773    .9778871
       _cons |   -.000183   .0000994    -1.84   0.066    -.0003778    .0000119
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_110

      Source |       SS       df       MS              Number of obs =   36189
-------------+------------------------------           F(  1, 36187) =       .
       Model |  8838.08936     1  8838.08936           Prob > F      =  0.0000
    Residual |   60.138226 36187  .001661874           R-squared     =  0.9932
-------------+------------------------------           Adj R-squared =  0.9932
       Total |  8898.22759 36188  .245888902           Root MSE      =  .04077

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_110 |   .9858081   .0004275  2306.11   0.000     .9849702    .9866459
       _cons |   .0045833   .0002145    21.37   0.000     .0041628    .0050037
------------------------------------------------------------------------------

House: 1 to 111 vs. 1 to 109 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_109

      Source |       SS       df       MS              Number of obs =   35742
-------------+------------------------------           F(  1, 35740) =       .
       Model |  5011.44014     1  5011.44014           Prob > F      =  0.0000
    Residual |  25.6306063 35740  .000717141           R-squared     =  0.9949
-------------+------------------------------           Adj R-squared =  0.9949
       Total |  5037.07075 35741  .140932563           Root MSE      =  .02678

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_109 |    .961949   .0003639  2643.50   0.000     .9612357    .9626622
       _cons |  -.0027838   .0001418   -19.63   0.000    -.0030617   -.0025058
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_109

      Source |       SS       df       MS              Number of obs =   35742
-------------+------------------------------           F(  1, 35740) =       .
       Model |   8744.6575     1   8744.6575           Prob > F      =  0.0000
    Residual |  85.5082449 35740  .002392508           R-squared     =  0.9903
-------------+------------------------------           Adj R-squared =  0.9903
       Total |  8830.16575 35741   .24705984           Root MSE      =  .04891

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_109 |   .9695837   .0005072  1911.81   0.000     .9685897    .9705778
       _cons |   .0070545   .0002589    27.25   0.000      .006547     .007562
------------------------------------------------------------------------------

House: 1 to 111 vs. 1 to 108 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_108

      Source |       SS       df       MS              Number of obs =   35303
-------------+------------------------------           F(  1, 35301) =       .
       Model |  4883.89265     1  4883.89265           Prob > F      =  0.0000
    Residual |   38.529007 35301  .001091442           R-squared     =  0.9922
-------------+------------------------------           Adj R-squared =  0.9922
       Total |  4922.42166 35302  .139437473           Root MSE      =  .03304

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_108 |   .9458725   .0004471  2115.35   0.000     .9449961     .946749
       _cons |  -.0033282    .000176   -18.91   0.000    -.0036732   -.0029832
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_108

      Source |       SS       df       MS              Number of obs =   35303
-------------+------------------------------           F(  1, 35301) =       .
       Model |  8666.78699     1  8666.78699           Prob > F      =  0.0000
    Residual |  100.946465 35301  .002859592           R-squared     =  0.9885
-------------+------------------------------           Adj R-squared =  0.9885
       Total |  8767.73346 35302  .248363647           Root MSE      =  .05348

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_108 |   .9522951    .000547  1740.91   0.000     .9512229    .9533672
       _cons |   .0092101   .0002848    32.34   0.000     .0086519    .0097682
------------------------------------------------------------------------------

House: 1 to 111 vs. 1 to 107 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_107

      Source |       SS       df       MS              Number of obs =   34862
-------------+------------------------------           F(  1, 34860) =       .
       Model |  4751.66849     1  4751.66849           Prob > F      =  0.0000
    Residual |  62.6566683 34860   .00179738           R-squared     =  0.9870
-------------+------------------------------           Adj R-squared =  0.9870
       Total |  4814.32516 34861  .138100604           Root MSE      =   .0424

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_107 |   .9133532   .0005617  1625.93   0.000     .9122522    .9144543
       _cons |  -.0025098   .0002273   -11.04   0.000    -.0029553   -.0020644
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_107

      Source |       SS       df       MS              Number of obs =   34862
-------------+------------------------------           F(  1, 34860) =       .
       Model |  8496.07599     1  8496.07599           Prob > F      =  0.0000
    Residual |  211.039047 34860  .006053903           R-squared     =  0.9758
-------------+------------------------------           Adj R-squared =  0.9758
       Total |  8707.11504 34861  .249766646           Root MSE      =  .07781

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_107 |   .9427794   .0007958  1184.65   0.000     .9412195    .9443392
       _cons |   .0106725   .0004169    25.60   0.000     .0098553    .0114897
------------------------------------------------------------------------------

House: 1 to 111 vs. 1 to 106 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_106

      Source |       SS       df       MS              Number of obs =   34420
-------------+------------------------------           F(  1, 34418) =       .
       Model |  4609.43404     1  4609.43404           Prob > F      =  0.0000
    Residual |  103.338114 34418  .003002444           R-squared     =  0.9781
-------------+------------------------------           Adj R-squared =  0.9781
       Total |  4712.77215 34419  .136923564           Root MSE      =  .05479

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_106 |   .8934704   .0007211  1239.04   0.000      .892057    .8948837
       _cons |  -.0017796   .0002956    -6.02   0.000    -.0023589   -.0012003
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_106

      Source |       SS       df       MS              Number of obs =   34420
-------------+------------------------------           F(  1, 34418) =       .
       Model |  8286.46906     1  8286.46906           Prob > F      =  0.0000
    Residual |  356.012401 34418  .010343785           R-squared     =  0.9588
-------------+------------------------------           Adj R-squared =  0.9588
       Total |  8642.48146 34419  .251096239           Root MSE      =   .1017

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_106 |   .8973204   .0010025   895.05   0.000     .8953554    .8992855
       _cons |   .0158494   .0005483    28.90   0.000     .0147747    .0169242
------------------------------------------------------------------------------

House:  1 to 111 vs. 1 to 105 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_105

      Source |       SS       df       MS              Number of obs =   33980
-------------+------------------------------           F(  1, 33978) =       .
       Model |  4338.91386     1  4338.91386           Prob > F      =  0.0000
    Residual |   279.42958 33978  .008223838           R-squared     =  0.9395
-------------+------------------------------           Adj R-squared =  0.9395
       Total |  4618.34344 33979   .13591758           Root MSE      =  .09069

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_105 |   .9687665   .0013337   726.36   0.000     .9661523    .9713806
       _cons |  -.0029271   .0004924    -5.94   0.000    -.0038921    -.001962
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_105

      Source |       SS       df       MS              Number of obs =   33980
-------------+------------------------------           F(  1, 33978) =       .
       Model |  7539.38214     1  7539.38214           Prob > F      =  0.0000
    Residual |  1034.83439 33978  .030456013           R-squared     =  0.8793
-------------+------------------------------           Adj R-squared =  0.8793
       Total |  8574.21654 33979  .252338696           Root MSE      =  .17452

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_105 |   .8986305   .0018061   497.54   0.000     .8950904    .9021706
       _cons |   .0211797   .0009468    22.37   0.000      .019324    .0230355
------------------------------------------------------------------------------

Senate:  1 to 111 vs. 1 to 110 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_110

      Source |       SS       df       MS              Number of obs =    8748
-------------+------------------------------           F(  1,  8746) =       .
       Model |  1296.76332     1  1296.76332           Prob > F      =  0.0000
    Residual |  9.99226514  8746  .001142495           R-squared     =  0.9924
-------------+------------------------------           Adj R-squared =  0.9924
       Total |  1306.75558  8747  .149394716           Root MSE      =   .0338

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_110 |    .974017   .0009142  1065.38   0.000     .9722249    .9758091
       _cons |  -.0043025   .0003615   -11.90   0.000    -.0050111   -.0035938
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_110

      Source |       SS       df       MS              Number of obs =    8748
-------------+------------------------------           F(  1,  8746) =       .
       Model |  2381.29599     1  2381.29599           Prob > F      =  0.0000
    Residual |  33.0012675  8746  .003773298           R-squared     =  0.9863
-------------+------------------------------           Adj R-squared =  0.9863
       Total |  2414.29726  8747   .27601432           Root MSE      =  .06143

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_110 |   .9855032   .0012405   794.41   0.000     .9830714    .9879349
       _cons |  -.0009352   .0006574    -1.42   0.155     -.002224    .0003535
------------------------------------------------------------------------------


Senate:  1 to 111 vs. 1 to 109 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_109

      Source |       SS       df       MS              Number of obs =    8645
-------------+------------------------------           F(  1,  8643) =       .
       Model |  1270.82414     1  1270.82414           Prob > F      =  0.0000
    Residual |  15.9926146  8643  .001850355           R-squared     =  0.9876
-------------+------------------------------           Adj R-squared =  0.9876
       Total |  1286.81675  8644  .148868204           Root MSE      =  .04302

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_109 |    .959954   .0011583   828.73   0.000     .9576833    .9622246
       _cons |  -.0081467   .0004627   -17.61   0.000    -.0090537   -.0072397
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_109

      Source |       SS       df       MS              Number of obs =    8645
-------------+------------------------------           F(  1,  8643) =       .
       Model |  2347.78165     1  2347.78165           Prob > F      =  0.0000
    Residual |  48.6922865  8643  .005633725           R-squared     =  0.9797
-------------+------------------------------           Adj R-squared =  0.9797
       Total |  2396.47393  8644  .277241316           Root MSE      =  .07506

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_109 |    .956815   .0014822   645.55   0.000     .9539096    .9597204
       _cons |  -.0024568    .000808    -3.04   0.002    -.0040407    -.000873
------------------------------------------------------------------------------

Senate:  1 to 111 vs. 1 to 108 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_108

      Source |       SS       df       MS              Number of obs =    8543
-------------+------------------------------           F(  1,  8541) =       .
       Model |  1237.00528     1  1237.00528           Prob > F      =  0.0000
    Residual |   30.659354  8541  .003589668           R-squared     =  0.9758
-------------+------------------------------           Adj R-squared =  0.9758
       Total |  1267.66464  8542  .148403727           Root MSE      =  .05991

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_108 |   .9421329   .0016049   587.03   0.000     .9389869     .945279
       _cons |  -.0102338   .0006483   -15.79   0.000    -.0115046    -.008963
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_108

      Source |       SS       df       MS              Number of obs =    8543
-------------+------------------------------           F(  1,  8541) =       .
       Model |   2296.8023     1   2296.8023           Prob > F      =  0.0000
    Residual |  82.6393788  8541  .009675609           R-squared     =  0.9653
-------------+------------------------------           Adj R-squared =  0.9653
       Total |  2379.44168  8542  .278557912           Root MSE      =  .09836

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_108 |   .9222381   .0018929   487.22   0.000     .9185276    .9259486
       _cons |  -.0019096   .0010652    -1.79   0.073    -.0039976    .0001784
------------------------------------------------------------------------------

Senate:  1 to 111 vs. 1 to 107 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_107

      Source |       SS       df       MS              Number of obs =    8442
-------------+------------------------------           F(  1,  8440) =       .
       Model |  1205.36866     1  1205.36866           Prob > F      =  0.0000
    Residual |  45.6457603  8440  .005408265           R-squared     =  0.9635
-------------+------------------------------           Adj R-squared =  0.9635
       Total |  1251.01442  8441  .148206897           Root MSE      =  .07354

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_107 |   .9227645   .0019546   472.10   0.000      .918933     .926596
       _cons |  -.0119334   .0008004   -14.91   0.000    -.0135025   -.0103643
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_107

      Source |       SS       df       MS              Number of obs =    8442
-------------+------------------------------           F(  1,  8440) =       .
       Model |  2232.12869     1  2232.12869           Prob > F      =  0.0000
    Residual |  128.160586  8440  .015184904           R-squared     =  0.9457
-------------+------------------------------           Adj R-squared =  0.9457
       Total |  2360.28927  8441  .279621997           Root MSE      =  .12323

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_107 |   .8923396   .0023274   383.40   0.000     .8877772    .8969019
       _cons |   .0006276   .0013426     0.47   0.640    -.0020043    .0032594
------------------------------------------------------------------------------

Senate:  1 to 111 vs. 1 to 106 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_106

      Source |       SS       df       MS              Number of obs =    8340
-------------+------------------------------           F(  1,  8338) =       .
       Model |  1177.33192     1  1177.33192           Prob > F      =  0.0000
    Residual |  56.0331436  8338  .006720214           R-squared     =  0.9546
-------------+------------------------------           Adj R-squared =  0.9546
       Total |  1233.36506  8339  .147903233           Root MSE      =  .08198

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_106 |   .9237391   .0022069   418.56   0.000     .9194129    .9280653
       _cons |  -.0102118   .0008978   -11.37   0.000    -.0119717    -.008452
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_106

      Source |       SS       df       MS              Number of obs =    8340
-------------+------------------------------           F(  1,  8338) =       .
       Model |  2187.01664     1  2187.01664           Prob > F      =  0.0000
    Residual |  153.453716  8338   .01840414           R-squared     =  0.9344
-------------+------------------------------           Adj R-squared =  0.9344
       Total |  2340.47036  8339   .28066559           Root MSE      =  .13566

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_106 |   .8478223   .0024594   344.72   0.000     .8430012    .8526434
       _cons |   .0029075   .0014874     1.95   0.051    -8.10e-06    .0058231
------------------------------------------------------------------------------

Senate:  1 to 111 vs. 1 to 105 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_111 dwnom1_105

      Source |       SS       df       MS              Number of obs =    8237
-------------+------------------------------           F(  1,  8235) =83795.50
       Model |  1107.23149     1  1107.23149           Prob > F      =  0.0000
    Residual |  108.813136  8235  .013213496           R-squared     =  0.9105
-------------+------------------------------           Adj R-squared =  0.9105
       Total |  1216.04463  8236  .147649906           Root MSE      =  .11495

------------------------------------------------------------------------------
  dwnom1_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_105 |   .9320901   .0032199   289.47   0.000     .9257782     .938402
       _cons |  -.0143823   .0012666   -11.36   0.000    -.0168651   -.0118995
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_111 dwnom2_105

      Source |       SS       df       MS              Number of obs =    8237
-------------+------------------------------           F(  1,  8235) =55823.39
       Model |  2022.94366     1  2022.94366           Prob > F      =  0.0000
    Residual |  298.422243  8235  .036238281           R-squared     =  0.8714
-------------+------------------------------           Adj R-squared =  0.8714
       Total |   2321.3659  8236  .281855986           Root MSE      =  .19036

------------------------------------------------------------------------------
  dwnom2_111 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_105 |   .8524167   .0036078   236.27   0.000     .8453444    .8594889
       _cons |  -.0009523   .0020993    -0.45   0.650    -.0050675     .003163
------------------------------------------------------------------------------
House Correlation Matrix All DW-NOMINATE Scalings

. pwcorr 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

             | dwnom1~1 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_111 |   1.0000 
             |
             |
  dwnom2_111 |  -0.0784   1.0000 
             |   0.0000
             |
  dwnom1_110 |   0.9987  -0.0789   1.0000 
             |   0.0000   0.0000
             |
  dwnom2_110 |  -0.0836   0.9966  -0.0831   1.0000 
             |   0.0000   0.0000   0.0000
             |
  dwnom1_109 |   0.9975  -0.0836   0.9992  -0.0867   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_109 |  -0.0838   0.9951  -0.0836   0.9983  -0.0865   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_108 |   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
             |
  dwnom2_108 |  -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
             |
  dwnom1_107 |   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
             |
  dwnom2_107 |  -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
             |
  dwnom1_106 |   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
             |
  dwnom2_106 |  -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
             |
  dwnom1_105 |   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
             |
  dwnom2_105 |   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  
             |   0.9219   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_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

             | dwnom1~1 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_111 |   1.0000 
             |
             |
  dwnom2_111 |  -0.0340   1.0000 
             |   0.0014
             |
  dwnom1_110 |   0.9962  -0.0263   1.0000 
             |   0.0000   0.0141
             |
  dwnom2_110 |  -0.0422   0.9931  -0.0372   1.0000 
             |   0.0001   0.0000   0.0005
             |
  dwnom1_109 |   0.9938  -0.0251   0.9991  -0.0365   1.0000 
             |   0.0000   0.0196   0.0000   0.0007
             |
  dwnom2_109 |  -0.0462   0.9898  -0.0414   0.9986  -0.0423   1.0000 
             |   0.0000   0.0000   0.0001   0.0000   0.0001
             |
  dwnom1_108 |   0.9878  -0.0238   0.9956  -0.0353   0.9981  -0.0413   1.0000 
             |   0.0000   0.0280   0.0000   0.0011   0.0000   0.0001
             |
  dwnom2_108 |  -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
             |
  dwnom1_107 |   0.9816  -0.0155   0.9914  -0.0279   0.9944  -0.0348   0.9966  -0.0407   1.0000 
             |   0.0000   0.1534   0.0000   0.0102   0.0000   0.0014   0.0000   0.0002
             |
  dwnom2_107 |  -0.0432   0.9725  -0.0376   0.9871  -0.0378   0.9910  -0.0363   0.9943  -0.0320   1.0000 
             |   0.0001   0.0000   0.0006   0.0000   0.0005   0.0000   0.0009   0.0000   0.0033
             |
  dwnom1_106 |   0.9770  -0.0038   0.9866  -0.0170   0.9891  -0.0245   0.9899  -0.0317   0.9971  -0.0229   1.0000 
             |   0.0000   0.7299   0.0000   0.1214   0.0000   0.0252   0.0000   0.0038   0.0000   0.0366
             |
  dwnom2_106 |  -0.0430   0.9667  -0.0369   0.9806  -0.0364   0.9845  -0.0344   0.9879  -0.0301   0.9962  -0.0231   1.0000 
             |   0.0001   0.0000   0.0008   0.0000   0.0009   0.0000   0.0017   0.0000   0.0060   0.0000   0.0349
             |
  dwnom1_105 |   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   0.2220   0.0000   0.0159   0.0000   0.0017   0.0000   0.0001   0.0000   0.0026   0.0000   0.0023
             |
  dwnom2_105 |  -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 
             |   0.0403   0.0000   0.1528   0.0000   0.1913   0.0000   0.3116   0.0000   0.5993   0.0000   0.8830   0.0000   0.4388
             |

Site Links

VOTEVIEW Blog
NOMINATE Data, Roll Call Data, and Software
Course Web Pages: UGA (2010 - )
Course Web Pages: UC San Diego (2004 - 2010)
University of San Diego Law School (2005)
Course Web Pages: University of Houston (2000 - 2005)
Course Web Pages: Carnegie-Mellon University (1997 - 2000)
Spatial Models of Parliamentary Voting
Recent Working Papers
Analyses of Recent Politics
About This Website
K7MOA Log Books: 1960 - 2011
Bio of Keith T. Poole
Related Links