Current Versus Past DW-NOMINATE Scores

Updated 18 February 2013


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

Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata File, 37,077 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 9 File, 37,077 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 37,077 lines)
Legislator Estimates Current and Past Releases of DW-NOMINATE Scores (EVIEWS File, 37,077 lines)

Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata File, 8,958 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Stata 9 File, 8,958 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (Excel File, 8,958 lines)
Senator Estimates Current and Past Releases of DW-NOMINATE Scores (EVIEWS File, 8,958 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 111 House release are .999 for the first dimension and .996 for the second. The corresponding r-squares for the Senate are .995 and .994, respectively. The regression tables give the mapping of the 1 - 111 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 .936 for the first dimension and .874 for the second. The corresponding r-squares for the Senate are .884 and .852, 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 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
             |

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