Current Versus Past DW-NOMINATE Scores

Updated 26 May 2015



The STATA, Eviews and Excel files below combine current legislator scores (HL01113D21_PRES.DAT for the House; SL01113D21_PRES.DAT for the Senate) with the past eight releases of the DW-NOMINATE legislator scores (HL01105D.SRT, HL01106C.DAT, HL01107A1.DAT, HL01108A1_PRES.DAT, HL01109A21_PRES.DAT, HL01110D21_PRES_NEW.DAT, HL01111E21_PRES.DAT, and HL01112D21_PRES.DAT for the House; SL01105C.DAT, SL01106D.DAT, SL01107A1.DAT, SL01108A1.DAT, SL01109B21.DAT, SL01110C21_NEW.DAT, SL01111E21.DAT, and SL01112D21.DAT for 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 seven releases are from this version.

Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 13 File, 37,521 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 12 File, 37,521 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 37,521 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (EVIEWS File, 37,521 lines)

Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 13 File, 9,063 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 12 File, 9,063 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 9,063 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (EVIEWS File, 9,063 lines)

Below are the results of regressing the current dimensions on the corresponding dimensions of the previous releases. The r-squares for the current 1 to 113 House with the 1 to 112 House release are .998 for the first dimension and .991 for the second. The corresponding r-squares for the Senate are .994 and .990, respectively. The regression tables give the mapping of the 1 - 112 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 .929 for the first dimension and .865 for the second. The corresponding r-squares for the Senate are .855 and .822, 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. Finally, 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 113 vs. 1 to 112 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_112

      Source |       SS       df       MS              Number of obs =   37077
-------------+------------------------------           F(  1, 37075) =       .
       Model |  5169.02708     1  5169.02708           Prob > F      =  0.0000
    Residual |  11.6393954 37075  .000313942           R-squared     =  0.9978
-------------+------------------------------           Adj R-squared =  0.9978
       Total |  5180.66648 37076  .139730998           Root MSE      =  .01772

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_112 |   .9829331   .0002422  4057.70   0.000     .9824583    .9834079
       _cons |  -.0013952   .0000921   -15.14   0.000    -.0015758   -.0012146
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_112

      Source |       SS       df       MS              Number of obs =   37077
-------------+------------------------------           F(  1, 37075) =       .
       Model |  8587.38328     1  8587.38328           Prob > F      =  0.0000
    Residual |  76.9756672 37075  .002076215           R-squared     =  0.9911
-------------+------------------------------           Adj R-squared =  0.9911
       Total |  8664.35895 37076  .233691848           Root MSE      =  .04557

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_112 |   .9835681   .0004836  2033.73   0.000     .9826202    .9845161
       _cons |    .010514   .0002372    44.33   0.000     .0100491    .0109788
------------------------------------------------------------------------------

House: 1 to 113 vs. 1 to 111 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_111

      Source |       SS       df       MS              Number of obs =   36634
-------------+------------------------------           F(  1, 36632) =       .
       Model |  4997.92072     1  4997.92072           Prob > F      =  0.0000
    Residual |  24.3035172 36632   .00066345           R-squared     =  0.9952
-------------+------------------------------           Adj R-squared =  0.9952
       Total |  5022.22424 36633  .137095631           Root MSE      =  .02576

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_111 |   .9739628   .0003549  2744.67   0.000     .9732673    .9746583
       _cons |  -.0010557   .0001347    -7.84   0.000    -.0013198   -.0007917
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_111

      Source |       SS       df       MS              Number of obs =   36634
-------------+------------------------------           F(  1, 36632) =       .
       Model |  8460.71188     1  8460.71188           Prob > F      =  0.0000
    Residual |  148.198988 36632  .004045616           R-squared     =  0.9828
-------------+------------------------------           Adj R-squared =  0.9828
       Total |  8608.91086 36633  .235004255           Root MSE      =  .06361

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_111 |   .9708714   .0006714  1446.14   0.000     .9695556    .9721873
       _cons |   .0154127   .0003328    46.31   0.000     .0147603     .016065
------------------------------------------------------------------------------

House: 1 to 113 vs. 1 to 110 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_110

      Source |       SS       df       MS              Number of obs =   36189
-------------+------------------------------           F(  1, 36187) =       .
       Model |  4848.84281     1  4848.84281           Prob > F      =  0.0000
    Residual |  49.5044524 36187  .001368018           R-squared     =  0.9899
-------------+------------------------------           Adj R-squared =  0.9899
       Total |  4898.34726 36188  .135358331           Root MSE      =  .03699

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_110 |   .9493184   .0005042  1882.67   0.000     .9483301    .9503067
       _cons |  -.0017018   .0001946    -8.74   0.000    -.0020833   -.0013204
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_110

      Source |       SS       df       MS              Number of obs =   36189
-------------+------------------------------           F(  1, 36187) =       .
       Model |   8295.3667     1   8295.3667           Prob > F      =  0.0000
    Residual |  238.762484 36187  .006598018           R-squared     =  0.9720
-------------+------------------------------           Adj R-squared =  0.9720
       Total |  8534.12918 36188    .2358276           Root MSE      =  .08123

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_110 |   .9550607   .0008518  1121.27   0.000     .9533912    .9567302
       _cons |   .0193568   .0004274    45.29   0.000     .0185191    .0201945
------------------------------------------------------------------------------


House: 1 to 113 vs. 1 to 109 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_109

      Source |       SS       df       MS              Number of obs =   35742
-------------+------------------------------           F(  1, 35740) =       .
       Model |  4707.70549     1  4707.70549           Prob > F      =  0.0000
    Residual |  67.0482072 35740  .001875999           R-squared     =  0.9860
-------------+------------------------------           Adj R-squared =  0.9860
       Total |   4774.7537 35741  .133593176           Root MSE      =  .04331

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_109 |   .9323423   .0005886  1584.12   0.000     .9311887    .9334959
       _cons |    -.00476   .0002294   -20.75   0.000    -.0052096   -.0043104
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_109

      Source |       SS       df       MS              Number of obs =   35742
-------------+------------------------------           F(  1, 35740) =       .
       Model |  8219.84534     1  8219.84534           Prob > F      =  0.0000
    Residual |  246.509137 35740   .00689729           R-squared     =  0.9709
-------------+------------------------------           Adj R-squared =  0.9709
       Total |  8466.35447 35741  .236880738           Root MSE      =  .08305

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_109 |   .9400387   .0008611  1091.67   0.000     .9383509    .9417265
       _cons |   .0209769   .0004396    47.72   0.000     .0201152    .0218385
------------------------------------------------------------------------------

House: 1 to 113 vs. 1 to 108 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_108

      Source |       SS       df       MS              Number of obs =   35303
-------------+------------------------------           F(  1, 35301) =       .
       Model |  4570.51797     1  4570.51797           Prob > F      =  0.0000
    Residual |  81.5430401 35301  .002309936           R-squared     =  0.9825
-------------+------------------------------           Adj R-squared =  0.9825
       Total |  4652.06101 35302  .131778965           Root MSE      =  .04806

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_108 |   .9150235   .0006505  1406.64   0.000     .9137485    .9162986
       _cons |  -.0058533   .0002561   -22.86   0.000    -.0063552   -.0053513
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_108

      Source |       SS       df       MS              Number of obs =   35303
-------------+------------------------------           F(  1, 35301) =       .
       Model |  8145.94141     1  8145.94141           Prob > F      =  0.0000
    Residual |  259.550663 35301  .007352502           R-squared     =  0.9691
-------------+------------------------------           Adj R-squared =  0.9691
       Total |  8405.49208 35302  .238102433           Root MSE      =  .08575

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_108 |   .9232368   .0008771  1052.58   0.000     .9215176     .924956
       _cons |   .0219556   .0004566    48.08   0.000     .0210606    .0228506
------------------------------------------------------------------------------

House: 1 to 113 vs. 1 to 107 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_107

      Source |       SS       df       MS              Number of obs =   34862
-------------+------------------------------           F(  1, 34860) =       .
       Model |  4434.57372     1  4434.57372           Prob > F      =  0.0000
    Residual |   102.82705 34860  .002949715           R-squared     =  0.9773
-------------+------------------------------           Adj R-squared =  0.9773
       Total |  4537.40077 34861  .130156931           Root MSE      =  .05431

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_107 |   .8823515   .0007196  1226.13   0.000      .880941     .883762
       _cons |  -.0056134   .0002911   -19.28   0.000    -.0061841   -.0050428
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_107

      Source |       SS       df       MS              Number of obs =   34862
-------------+------------------------------           F(  1, 34860) =       .
       Model |  8000.30839     1  8000.30839           Prob > F      =  0.0000
    Residual |  346.587451 34860  .009942268           R-squared     =  0.9585
-------------+------------------------------           Adj R-squared =  0.9585
       Total |  8346.89584 34861  .239433632           Root MSE      =  .09971

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_107 |   .9148592   .0010199   897.04   0.000     .9128602    .9168581
       _cons |   .0221275   .0005343    41.41   0.000     .0210803    .0231748
------------------------------------------------------------------------------

House: 1 to 113 vs. 1 to 106 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_106

      Source |       SS       df       MS              Number of obs =   34420
-------------+------------------------------           F(  1, 34418) =       .
       Model |  4290.80523     1  4290.80523           Prob > F      =  0.0000
    Residual |  139.531743 34418  .004054034           R-squared     =  0.9685
-------------+------------------------------           Adj R-squared =  0.9685
       Total |  4430.33697 34419  .128717771           Root MSE      =  .06367

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_106 |   .8620367   .0008379  1028.79   0.000     .8603943     .863679
       _cons |  -.0054574   .0003434   -15.89   0.000    -.0061306   -.0047842
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_106

      Source |       SS       df       MS              Number of obs =   34420
-------------+------------------------------           F(  1, 34418) =       .
       Model |  7822.98089     1  7822.98089           Prob > F      =  0.0000
    Residual |  463.692674 34418  .013472389           R-squared     =  0.9440
-------------+------------------------------           Adj R-squared =  0.9440
       Total |  8286.67357 34419  .240758696           Root MSE      =  .11607

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_106 |   .8718644   .0011442   762.02   0.000     .8696218     .874107
       _cons |   .0258426   .0006258    41.30   0.000      .024616    .0270692
------------------------------------------------------------------------------

House:  1 to 113 vs. 1 to 105 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_105

      Source |       SS       df       MS              Number of obs =   33980
-------------+------------------------------           F(  1, 33978) =       .
       Model |  4023.09364     1  4023.09364           Prob > F      =  0.0000
    Residual |  308.780491 33978  .009087659           R-squared     =  0.9287
-------------+------------------------------           Adj R-squared =  0.9287
       Total |  4331.87413 33979  .127486805           Root MSE      =  .09533

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_105 |   .9328432    .001402   665.36   0.000     .9300952    .9355912
       _cons |  -.0070752   .0005176   -13.67   0.000    -.0080897   -.0060607
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_105

      Source |       SS       df       MS              Number of obs =   33980
-------------+------------------------------           F(  1, 33978) =       .
       Model |  7110.84621     1  7110.84621           Prob > F      =  0.0000
    Residual |  1114.43871 33978  .032798832           R-squared     =  0.8645
-------------+------------------------------           Adj R-squared =  0.8645
       Total |  8225.28492 33979  .242069658           Root MSE      =   .1811

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_105 |   .8727179   .0018743   465.62   0.000     .8690442    .8763917
       _cons |   .0296881   .0009826    30.22   0.000     .0277622    .0316139
------------------------------------------------------------------------------


Senate:  1 to 113 vs. 1 to 112 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_112

      Source |       SS       df       MS              Number of obs =    8958
-------------+------------------------------           F(  1,  8956) =       .
       Model |  1278.05146     1  1278.05146           Prob > F      =  0.0000
    Residual |  7.64674359  8956  .000853812           R-squared     =  0.9941
-------------+------------------------------           Adj R-squared =  0.9941
       Total |   1285.6982  8957  .143541163           Root MSE      =  .02922

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_112 |   .9867752   .0008065  1223.47   0.000     .9851942    .9883562
       _cons |  -.0035728   .0003091   -11.56   0.000    -.0041788   -.0029669
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_112

      Source |       SS       df       MS              Number of obs =    8958
-------------+------------------------------           F(  1,  8956) =       .
       Model |  2268.33905     1  2268.33905           Prob > F      =  0.0000
    Residual |  22.6633966  8956  .002530527           R-squared     =  0.9901
-------------+------------------------------           Adj R-squared =  0.9901
       Total |  2291.00245  8957  .255777878           Root MSE      =   .0503

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_112 |   .9752736   .0010301   946.78   0.000     .9732544    .9772928
       _cons |  -.0132106   .0005323   -24.82   0.000     -.014254   -.0121672
------------------------------------------------------------------------------

Senate:  1 to 113 vs. 1 to 111 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_111

      Source |       SS       df       MS              Number of obs =    8856
-------------+------------------------------           F(  1,  8854) =       .
       Model |  1239.75754     1  1239.75754           Prob > F      =  0.0000
    Residual |  22.9381596  8854  .002590711           R-squared     =  0.9818
-------------+------------------------------           Adj R-squared =  0.9818
       Total |   1262.6957  8855  .142596917           Root MSE      =   .0509

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_111 |   .9663282   .0013969   691.77   0.000     .9635899    .9690665
       _cons |   -.009075   .0005413   -16.77   0.000     -.010136    -.008014
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_111

      Source |       SS       df       MS              Number of obs =    8856
-------------+------------------------------           F(  1,  8854) =       .
       Model |  2226.11889     1  2226.11889           Prob > F      =  0.0000
    Residual |  50.4710363  8854  .005700366           R-squared     =  0.9778
-------------+------------------------------           Adj R-squared =  0.9778
       Total |  2276.58993  8855  .257096547           Root MSE      =   .0755

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_111 |   .9565577   .0015307   624.92   0.000     .9535572    .9595583
       _cons |   -.018028   .0008031   -22.45   0.000    -.0196024   -.0164537
------------------------------------------------------------------------------


Senate:  1 to 113 vs. 1 to 110 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_110

      Source |       SS       df       MS              Number of obs =    8748
-------------+------------------------------           F(  1,  8746) =       .
       Model |  1193.81294     1  1193.81294           Prob > F      =  0.0000
    Residual |  48.2041367  8746  .005511564           R-squared     =  0.9612
-------------+------------------------------           Adj R-squared =  0.9612
       Total |  1242.01708  8747  .141993493           Root MSE      =  .07424

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_110 |   .9345538    .002008   465.40   0.000     .9306176    .9384901
       _cons |  -.0137322    .000794   -17.29   0.000    -.0152887   -.0121758
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_110

      Source |       SS       df       MS              Number of obs =    8748
-------------+------------------------------           F(  1,  8746) =       .
       Model |  2145.73455     1  2145.73455           Prob > F      =  0.0000
    Residual |  113.562971  8746  .012984561           R-squared     =  0.9497
-------------+------------------------------           Adj R-squared =  0.9497
       Total |  2259.29752  8747  .258293989           Root MSE      =  .11395

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_110 |   .9354904   .0023013   406.51   0.000     .9309794    .9400014
       _cons |  -.0191431   .0012196   -15.70   0.000    -.0215337   -.0167525
------------------------------------------------------------------------------

Senate:  1 to 113 vs. 1 to 109 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_109

      Source |       SS       df       MS              Number of obs =    8645
-------------+------------------------------           F(  1,  8643) =       .
       Model |  1160.77771     1  1160.77771           Prob > F      =  0.0000
    Residual |  61.4627811  8643  .007111279           R-squared     =  0.9497
-------------+------------------------------           Adj R-squared =  0.9497
       Total |  1222.24049  8644  .141397558           Root MSE      =  .08433

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_109 |   .9174496   .0022708   404.02   0.000     .9129983    .9219009
       _cons |  -.0178532   .0009071   -19.68   0.000    -.0196313    -.016075
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_109

      Source |       SS       df       MS              Number of obs =    8645
-------------+------------------------------           F(  1,  8643) =       .
       Model |  2099.61757     1  2099.61757           Prob > F      =  0.0000
    Residual |  141.707519  8643   .01639564           R-squared     =  0.9368
-------------+------------------------------           Adj R-squared =  0.9368
       Total |  2241.32509  8644  .259292583           Root MSE      =  .12805

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_109 |   .9048346   .0025285   357.85   0.000     .8998782    .9097911
       _cons |  -.0206364   .0013784   -14.97   0.000    -.0233383   -.0179344
------------------------------------------------------------------------------

Senate:  1 to 113 vs. 1 to 108 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_108

      Source |       SS       df       MS              Number of obs =    8543
-------------+------------------------------           F(  1,  8541) =       .
       Model |  1117.79341     1  1117.79341           Prob > F      =  0.0000
    Residual |   85.592758  8541  .010021398           R-squared     =  0.9289
-------------+------------------------------           Adj R-squared =  0.9289
       Total |  1203.38617  8542  .140878737           Root MSE      =  .10011

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_108 |   .8955857   .0026816   333.98   0.000     .8903292    .9008423
       _cons |  -.0203238   .0010832   -18.76   0.000    -.0224471   -.0182005
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_108

      Source |       SS       df       MS              Number of obs =    8543
-------------+------------------------------           F(  1,  8541) =93089.23
       Model |  2036.99078     1  2036.99078           Prob > F      =  0.0000
    Residual |  186.895291  8541  .021882132           R-squared     =  0.9160
-------------+------------------------------           Adj R-squared =  0.9160
       Total |  2223.88607  8542  .260347234           Root MSE      =  .14793

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_108 |   .8685119   .0028466   305.11   0.000     .8629319    .8740919
       _cons |  -.0201398   .0016019   -12.57   0.000    -.0232799   -.0169998
------------------------------------------------------------------------------

Senate:  1 to 113 vs. 1 to 107 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_107

      Source |       SS       df       MS              Number of obs =    8442
-------------+------------------------------           F(  1,  8440) =86200.70
       Model |  1081.72364     1  1081.72364           Prob > F      =  0.0000
    Residual |  105.912685  8440  .012548896           R-squared     =  0.9108
-------------+------------------------------           Adj R-squared =  0.9108
       Total |  1187.63632  8441  .140698534           Root MSE      =  .11202

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_107 |   .8741563   .0029774   293.60   0.000     .8683199    .8799927
       _cons |  -.0223433   .0012193   -18.32   0.000    -.0247334   -.0199532
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_107

      Source |       SS       df       MS              Number of obs =    8442
-------------+------------------------------           F(  1,  8440) =72633.23
       Model |  1974.45293     1  1974.45293           Prob > F      =  0.0000
    Residual |  229.431953  8440  .027183881           R-squared     =  0.8959
-------------+------------------------------           Adj R-squared =  0.8959
       Total |  2203.88488  8441  .261092866           Root MSE      =  .16488

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_107 |    .839255   .0031141   269.51   0.000     .8331507    .8453593
       _cons |   -.017781   .0017964    -9.90   0.000    -.0213024   -.0142597
------------------------------------------------------------------------------

Senate:  1 to 113 vs. 1 to 106 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1

. regress dwnom1_113 dwnom1_106

      Source |       SS       df       MS              Number of obs =    8340
-------------+------------------------------           F(  1,  8338) =77219.80
       Model |  1056.92026     1  1056.92026           Prob > F      =  0.0000
    Residual |    114.1236  8338  .013687167           R-squared     =  0.9025
-------------+------------------------------           Adj R-squared =  0.9025
       Total |  1171.04386  8339  .140429771           Root MSE      =  .11699

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_106 |   .8752275   .0031496   277.88   0.000     .8690535    .8814016
       _cons |  -.0211169   .0012812   -16.48   0.000    -.0236284   -.0186053
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_106

      Source |       SS       df       MS              Number of obs =    8340
-------------+------------------------------           F(  1,  8338) =65224.43
       Model |  1936.04076     1  1936.04076           Prob > F      =  0.0000
    Residual |  247.494797  8338  .029682753           R-squared     =  0.8867
-------------+------------------------------           Adj R-squared =  0.8866
       Total |  2183.53556  8339  .261846212           Root MSE      =  .17229

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_106 |   .7976935   .0031234   255.39   0.000     .7915708    .8038162
       _cons |  -.0156205   .0018889    -8.27   0.000    -.0193232   -.0119178
------------------------------------------------------------------------------

Senate:  1 to 113 vs. 1 to 105 DW-NOMINATE Scalings

Dimension 1 vs. Dimension 1


. regress dwnom1_113 dwnom1_105

      Source |       SS       df       MS              Number of obs =    8237
-------------+------------------------------           F(  1,  8235) =48476.17
       Model |  986.919987     1  986.919987           Prob > F      =  0.0000
    Residual |   167.65529  8235   .02035887           R-squared     =  0.8548
-------------+------------------------------           Adj R-squared =  0.8548
       Total |  1154.57528  8236  .140186411           Root MSE      =  .14268

------------------------------------------------------------------------------
  dwnom1_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom1_105 |   .8799939   .0039968   220.17   0.000     .8721591    .8878287
       _cons |  -.0254872   .0015722   -16.21   0.000     -.028569   -.0224053
------------------------------------------------------------------------------

Dimension 2 vs. Dimension 2

. regress dwnom2_113 dwnom2_105

      Source |       SS       df       MS              Number of obs =    8237
-------------+------------------------------           F(  1,  8235) =38087.20
       Model |  1779.07855     1  1779.07855           Prob > F      =  0.0000
    Residual |  384.662303  8235  .046710662           R-squared     =  0.8222
-------------+------------------------------           Adj R-squared =  0.8222
       Total |  2163.74085  8236  .262717442           Root MSE      =  .21613

------------------------------------------------------------------------------
  dwnom2_113 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  dwnom2_105 |    .799388   .0040961   195.16   0.000     .7913586    .8074173
       _cons |  -.0192329   .0023835    -8.07   0.000    -.0239051   -.0145607
------------------------------------------------------------------------------

House Correlation Matrix All DW-NOMINATE Scalings

. pwcorr dwnom1_113 dwnom2_113 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

-------------+------------------------------------------------------------------------------------------------------------
             | dwnom1~3 dwnom2~3 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_113 |   1.0000 
             |
  dwnom2_113 |  -0.0313   1.0000 
             |   0.0000
             |
  dwnom1_112 |   0.9989  -0.0351   1.0000 
             |   0.0000   0.0000
             |
  dwnom2_112 |  -0.0634   0.9955  -0.0640   1.0000 
             |   0.0000   0.0000   0.0000
             |
  dwnom1_111 |   0.9976  -0.0401   0.9994  -0.0677   1.0000 
             |   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_111 |  -0.0779   0.9914  -0.0780   0.9981  -0.0784   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_110 |   0.9949  -0.0448   0.9975  -0.0700   0.9987  -0.0789   1.0000 
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom2_110 |  -0.0837   0.9859  -0.0836   0.9942  -0.0836   0.9966  -0.0831   1.0000  
             |   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_109 |   0.9930  -0.0529   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   0.0000   0.0000
             |
  dwnom2_109 |  -0.0842   0.9853  -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   0.0000   0.0000
             |
  dwnom1_108 |   0.9912  -0.0600   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   0.0000   0.0000
             |
  dwnom2_108 |  -0.0797   0.9844  -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   0.0000   0.0000
             |
  dwnom1_107 |   0.9886  -0.0723   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   0.0000   0.0000
             |
  dwnom2_107 |  -0.0614   0.9790  -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   0.0000   0.0000
             |
  dwnom1_106 |   0.9841  -0.0813   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   0.0000   0.0000
             |
  dwnom2_106 |  -0.0421   0.9716  -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   0.0000   0.0000
             |
  dwnom1_105 |   0.9637  -0.0838   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   0.0000   0.0000
             |
  dwnom2_105 |   0.0007   0.9298   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   
             |   0.8921   0.0000   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_113 dwnom2_113 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

-------------+------------------------------------------------------------------------------------------------------------             |
             | dwnom1~3 dwnom2~3 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_113 |   1.0000 
             |
             |
  dwnom2_113 |  -0.0215   1.0000 
             |   0.0404
             |
  dwnom1_112 |   0.9970  -0.0186   1.0000 
             |   0.0000   0.0784
             |
  dwnom2_112 |  -0.0237   0.9950  -0.0234   1.0000 
             |   0.0249   0.0000   0.0268
             |
  dwnom1_111 |   0.9909  -0.0188   0.9973  -0.0258   1.0000 
             |   0.0000   0.0765   0.0000   0.0154
             |
  dwnom2_111 |  -0.0292   0.9889  -0.0294   0.9972  -0.0340   1.0000 
             |   0.0060   0.0000   0.0056   0.0000   0.0014
             |
  dwnom1_110 |   0.9804  -0.0089   0.9898  -0.0168   0.9962  -0.0263   1.0000 
             |   0.0000   0.4066   0.0000   0.1154   0.0000   0.0141
             |
  dwnom2_110 |  -0.0369   0.9745  -0.0376   0.9865  -0.0422   0.9931  -0.0372   1.0000 
             |   0.0006   0.0000   0.0004   0.0000   0.0001   0.0000   0.0005
             |
  dwnom1_109 |   0.9745  -0.0060   0.9856  -0.0147   0.9938  -0.0251   0.9991  -0.0365   1.0000 
             |   0.0000   0.5792   0.0000   0.1717   0.0000   0.0196   0.0000   0.0007
             |
  dwnom2_109 |  -0.0403   0.9679  -0.0412   0.9817  -0.0462   0.9898  -0.0414   0.9986  -0.0423   1.0000  
             |   0.0002   0.0000   0.0001   0.0000   0.0000   0.0000   0.0001   0.0000   0.0001
             |
  dwnom1_108 |   0.9638  -0.0031   0.9774  -0.0127   0.9878  -0.0238   0.9956  -0.0353   0.9981  -0.0413   1.0000  
             |   0.0000   0.7770   0.0000   0.2396   0.0000   0.0280   0.0000   0.0011   0.0000   0.0001
             |
  dwnom2_108 |  -0.0435   0.9571  -0.0444   0.9728  -0.0495   0.9825  -0.0450   0.9942  -0.0461   0.9975  -0.0460   1.0000  
             |   0.0001   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000   0.0000
             |
  dwnom1_107 |   0.9544   0.0083   0.9693  -0.0033   0.9816  -0.0155   0.9914  -0.0279   0.9944  -0.0348   0.9966  -0.0407   1.0000  
             |   0.0000   0.4474   0.0000   0.7619   0.0000   0.1534   0.0000   0.0102   0.0000   0.0014   0.0000   0.0002
             |
  dwnom2_107 |  -0.0409   0.9465  -0.0397   0.9622  -0.0432   0.9725  -0.0376   0.9871  -0.0378   0.9910  -0.0363   0.9943  -0.0320   1.0000  
             |   0.0002   0.0000   0.0003   0.0000   0.0001   0.0000   0.0006   0.0000   0.0005   0.0000   0.0009   0.0000   0.0033
             |
  dwnom1_106 |   0.9500   0.0221   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   0.0432   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 |  -0.0432   0.9416  -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   
             |   0.0001   0.0000   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 |   0.9245   0.0155   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   0.1599   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 |  -0.0264   0.9068  -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   
             |   0.0164   0.0000   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



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