(Joint House and Senate Scaling)

Updated 12 December 2016 [Minor update 20 December 2016] (This is the Final Release for Congresses 1 - 114.)

Beginning 1 January 2017 Jeff Lewis (UCLA) is in charge of the NOMINATE project (now nearly 35 years old). At some point voteview.com will be assigned to a UCLA server. This server, K7MOA.COM, will be a repository for all estimations done by Poole and Rosenthal, and Poole separately (OC, BasicSpace), from 1991 to date. Health permitting, Poole will maintain this repository until 2020. Keith Poole and Howard Rosenthal would like to thank the countless researchers who have sent us corrections to our data base. Keith Poole will be happy to answer questions on the data and programs of the project.

This is the thirty-first release of

In order to easily update the Common Space DW-NOMINATE scores when new roll calls are cast in Congress we had to write a new DW-NOMINATE program that required as input only the roll call matrix from Congresses 1 to 114 and the previous Legislator and Roll Call output files for Congresses 1 - 113 from the former program. Jeff Lewis wrote a batch file that combines PERL and Python scripts to combine all the roll call vote matrices together and then run the program. When we have everything completed these scores will be posted at the new voteview website at UCLA and the links below will go there with updated numbers of roll calls and legislators.

The New DW-NOMINATE program uses LBFGS to simultaneously estimate the roll call paraments and to simultaneously estimate the legislator parameters. Beta and the 2nd dimension weight are estimated using the Brent local minimization algorithm (Brent, Richard. 2002.

As of 12 December 2016 there were a total of 104,635 roll calls of which 93,727 were scalable. The number of unique legislators is 12,046 (this counts two new members, Evans (D-PA), and Comer (R-KY), and one former member, Hanabusa (D-HI), all three were elected in Special Elections on 8 November 2016) producing a total of 17,492,427 choices. The second dimension weight is 0.4153 and Beta is 7.6912. The correct classification is 87.42 percent with an APRE of 0.6294 and a geometric mean probability of 0.7568.

In order to calculate distances from these Common Space DW-NOMINATE scores you must multiply the second dimension by the weight parameter. To calculate the choice probabilities you must apply both the second dimension weight and the Beta parameter.

Please note that these files contain scores for most Presidents. For Presidents prior to Eisenhower these are based on roll calls corresponding to Presidential requests. These roll calls were compiled by an NSF project headed by Elaine Swift ( Study No. 3371, Database of Congressional Historical Statistics, 1789-1989). Many of these scores are based upon a small number of roll calls

Please note that at the end of each Congress we will post a final set of coordinates with bootstrapped standard errors on our Common Space DW-NOMINATE download page.

The format of the legislator files is:

Legislator Estimates 11. Congress Number 2. ICPSR ID Number: 5 digit code assigned by the ICPSR as corrected by Howard Rosenthal and myself. 3. State Code: 2 digit ICPSR State Code. 4. Congressional District Number (0 if Senate or President) 5. State Name 6. Party Code: 100 = Dem., 200 = Repub. (See PARTY3.DAT) 7. Occupancy: ICPSR Occupancy Code -- 0=only occupant; 1=1st occupant; 2=2nd occupant; etc. 8. Last Means of Attaining Office: ICPSR Attain-Office Code -- 1=general election; 2=special election; 3=elected by state legislature; 5=appointed 9. Name 10. 1st Dimension Coordinate 11. 2nd Dimension Coordinate 12. Log-Likelihood 13. Number of Votes 14. Number of Classification Errors 15. Geometric Mean Probability The format of the roll call files is: 1. Congress Number 2. Roll Call Number 3. Log-Likelihood 4. Spread on 1st Dimension -- if the roll call was not scaled, there 5. Midpoint on 1st Dimension -- are 0.000's in all four fields 6. Spread on 2nd Dimension -- 7. Midpoint on 2nd Dimension --

Legislator Estimates 1

Legislator Estimates 1

Legislator Estimates 1

Legislator Estimates 1

Roll Call Estimates 1

Roll Call Estimates 1

Roll Call Estimates 1

Roll Call Estimates 1

Roll Call Estimates 1

Below is STATA output showing regressions of these new coordinates onto the old coordinates for Congresses 1 - 113. All the r-squares are greater than 0.95 so that the new program is producing essentially the same coordinates as the old program.

. regress dwnom1new dwnom1 Source | SS df MS Number of obs = 46,506 -------------+---------------------------------- F(1, 46504) > 99999.00 Model | 6337.23422 1 6337.23422 Prob > F = 0.0000 Residual | 15.4389598 46,504 .000331992 R-squared = 0.9976 -------------+---------------------------------- Adj R-squared = 0.9976 Total | 6352.67318 46,505 .136601939 Root MSE = .01822 ------------------------------------------------------------------------------ dwnom1new | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- dwnom1 | .9756203 .0002233 4369.04 0.000 .9751827 .976058 _cons | -.0017729 .0000845 -20.98 0.000 -.0019385 -.0016072 ------------------------------------------------------------------------------ . regress dwnom2new dwnom2 Source | SS df MS Number of obs = 46,506 -------------+---------------------------------- F(1, 46504) > 99999.00 Model | 10091.1505 1 10091.1505 Prob > F = 0.0000 Residual | 250.073645 46,504 .005377465 R-squared = 0.9758 -------------+---------------------------------- Adj R-squared = 0.9758 Total | 10341.2242 46,505 .222368007 Root MSE = .07333 ------------------------------------------------------------------------------ dwnom2new | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- dwnom2 | 1.009491 .0007369 1369.88 0.000 1.008046 1.010935 _cons | .008995 .0003401 26.45 0.000 .0083284 .0096616 ------------------------------------------------------------------------------ . regress spread1new spread1 if (vardum==1) Source | SS df MS Number of obs = 92,182 -------------+---------------------------------- F(1, 92180) > 99999.00 Model | 10512.7982 1 10512.7982 Prob > F = 0.0000 Residual | 56.4450622 92,180 .000612335 R-squared = 0.9947 -------------+---------------------------------- Adj R-squared = 0.9947 Total | 10569.2432 92,181 .114657502 Root MSE = .02475 ------------------------------------------------------------------------------ spread1new | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread1 | 1.029077 .0002484 4143.47 0.000 1.02859 1.029564 _cons | -.0000282 .0000816 -0.35 0.730 -.000188 .0001317 ------------------------------------------------------------------------------ . regress mid1new mid1 if (vardum==1) Source | SS df MS Number of obs = 92,182 -------------+---------------------------------- F(1, 92180) > 99999.00 Model | 11859.8461 1 11859.8461 Prob > F = 0.0000 Residual | 140.163429 92,180 .001520541 R-squared = 0.9883 -------------+---------------------------------- Adj R-squared = 0.9883 Total | 12000.0095 92,181 .130178774 Root MSE = .03899 ------------------------------------------------------------------------------ mid1new | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mid1 | .9820736 .0003516 2792.80 0.000 .9813844 .9827629 _cons | .0000695 .0001284 0.54 0.589 -.0001823 .0003212 ------------------------------------------------------------------------------ . regress spread2new spread2 if (vardum==1) Source | SS df MS Number of obs = 92,182 -------------+---------------------------------- F(1, 92180) > 99999.00 Model | 23803.1281 1 23803.1281 Prob > F = 0.0000 Residual | 1170.16423 92,180 .01269434 R-squared = 0.9531 -------------+---------------------------------- Adj R-squared = 0.9531 Total | 24973.2924 92,181 .270915833 Root MSE = .11267 ------------------------------------------------------------------------------ spread2new | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- spread2 | 1.060118 .0007742 1369.34 0.000 1.058601 1.061636 _cons | -.0007929 .0003711 -2.14 0.033 -.0015203 -.0000655 ------------------------------------------------------------------------------ . regress mid2new mid2 if (vardum==1) Source | SS df MS Number of obs = 92,182 -------------+---------------------------------- F(1, 92180) > 99999.00 Model | 29244.8144 1 29244.8144 Prob > F = 0.0000 Residual | 318.065259 92,180 .00345048 R-squared = 0.9892 -------------+---------------------------------- Adj R-squared = 0.9892 Total | 29562.8796 92,181 .320704696 Root MSE = .05874 ------------------------------------------------------------------------------ mid2new | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mid2 | .9959004 .0003421 2911.28 0.000 .99523 .9965709 _cons | -.0001018 .0001935 -0.53 0.599 -.0004811 .0002775 ------------------------------------------------------------------------------

NOMINATE Data, Roll Call Data, and Software

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