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One Congress at a Time DW-NOMINATE (Nokken-Poole)

Updated 1 June 2015

Below are the legislator coordinate files from a One-Congress-at-a-Time DW-NOMINATE scaling developed by Nokken and Poole (Nokken, Timothy P. and Keith T. Poole. 2004. "Congressional Party Defection in American History." Legislative Studies Quarterly, 29:545-568, 2004). The aim is to allow the maximum amount of movement of legislators from Congress to Congress. First, the DW-NOMINATE two-dimensional constant coordinate model, that is, every legislator has the same ideal point throughout his or her career in the two dimensional space, is run to convergence. Second, holding the roll call outcomes from the two dimensional constant model fixed, an ideal point for every legislator in every Congress is estimated. Changes in ideal points of members from Congress to Congress can then be analyzed against the background of the fixed cutting lines. Nokken and Poole used this method to compare party switchers to members who did not switch parties between any pair of Congresses.

If you use the second dimension be sure to multiply it by the weight. For the House the weight is 0.3988 and for the Senate the weight is 0.5638.

The format of the legislator files is:

 1.  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)
 5.  State Name
 6.  Party Code:  100 = Dem., 200 = Repub. (See PARTY3.DAT)
 7.  Name
 8.  1st Dimension Coordinate
 9.  2nd Dimension Coordinate
10.  Log-Likelihood
11.  Number of Votes
12.  Number of Classification Errors
13.  Geometric Mean Probability

House Congress by Congress Legislator Coordinates: HL01113NP2_2014.DAT (Text File, 37,521 lines)
Senate Congress by Congress Legislator Coordinates: SL01113NP2_2014.DAT (Text File, 9,063 lines)

Site Links

NOMINATE Data, Roll Call Data, and Software
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Course Web Pages: Carnegie-Mellon University (1997 - 2000)
Analyzing Spatial Models of Choice and Judgment with R
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