Technical Appendix: The Model for Voting on the Amash Amendment

I took a more conventional, less spatially-oriented approach than that used on Voteview.  I used logit analysis to estimate a model using the two DW-NOMINATE scores and a variety of dummy variables to measure other possible influences like when the Member was elected and the committees on which the Member serves.  The dataset consists of the 342 Members who served in the 112th Congress (and thus for whom the DW-NOMINATE scores are publicly available) and voted on the Amash Amendment.

It soon became clear that the relationship between support for the amendment and ideological position was more complex than a simple linear model would predict.  What piques our interest in this vote is how the two parties divided over the amendment more than the division between the parties themselves.  I thus included separate terms for each ideological dimension within each party.  After doing so, including a dummy variable for party has no independent effect.

I also examined various measures of seniority in an effort to see whether there is any truth to pundits’ observation of a generational divide between older and newer members over the issue of domestic surveillance.  It turns out that the generational divide is especially pronounced for Republicans.  Those who were first elected to Congress in 2008 or 2010 were more likely to vote for the Amash amendment regardless of ideology.  For the Democrats, the results are more muted.  Those Democrats who were voted into office alongside President Obama in 2008 were especially likely to oppose him on domestic surveillance.  However the few Democrats who were first elected in 2010 were no more likely to support the amendment than Democrats elected in 2004 or before.

Finally there is a strong effect for committee memberships.  Members who serve on the House Armed Services or Select Intelligence committees were much more likely to vote against the Amash amendment.  The effect was especially pronounced for members of the Intelligence Committee.

Favored Amendment to Cut Funds for NSA Metadata Collection
Logit, 342 observations

             coefficient   std. error      z      p-value 
  Constant    −4.11799      0.699829    −5.884    4.00e-09 ***

DW-Nominate Scores

Dimension 1 ("Liberal-Conservative")
  Dems       −12.2661       1.82358     −6.726    1.74e-11 ***
  Reps         5.13557      0.972324     5.282    1.28e-07 ***
Dimension 2 (?)
  Dems        −0.879454     0.662692    −1.327    0.1845  
  Reps        −0.302442     0.642723    −0.4706   0.6380  

First Elected to Congress
  Dems 2008     3.25482      1.07614      3.025    0.0025   ***
  Reps 2008-10  0.751204     0.334374     2.247    0.0247   **

Committee Memberships
  Armed Srvcs  −1.03730      0.453937    −2.285    0.0223   **
  Intel        −3.15582      0.992305    −3.180    0.0015   ***

Estimated R2 (McFadden) = 0.276671
Number of cases 'correctly predicted' = 258 (75.4%)

While we have come some way to understanding the factors motivating Members’ votes on the Amash amendment, the model still cannot account for the votes of about a quarter of the House.