//
// Codebook for Justices and Court Datasets
// Andrew D. Martin and Kevin M. Quinn
// 
// Updated: October 9, 2004

We now distribute two datasets that contain ideal point estimates and estimates of the location of the median justice using the methodology of:

Andrew D. Martin and Kevin M. Quinn. 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953-1999." Political Analysis. 10: 134-153.

Both datasets are distributed as ASCII text files, Stata DTA files, SPSS SAV files, and Microsoft Excel files.  Please email admartin@wustl.edu with any questions or comments.

// Justices

term            Term
justice         Justice Last Name
code            Supreme Court Database Justice Code
post_mn         Ideal Point [Posterior Mean]
post_sd         Posterior Standard Deviation of Ideal Point
post_med        Posterior Median of Ideal Point
post_025        2.5 Percentile of Ideal Point
post_975        97.5 Percentile of Ideal Point

Note: We recommend using the posterior mean (post_mn) as the estimate the ideal point of each justice in each term.

// Court

term            Term
med             Location of Median Justice [Posterior Mean]
med_sd          Posterior Standard Deviation of Median Justice
min             Location of the Minimum Justice [Posterior Mean]
max             Location of the Minimum Justice [Posterior Mean]
justice         Justice Most Likely to Be Median
just_pr         Posterior Probability of Most Likely Justice
Harlan-Stone    Posterior Probability that Justice is the Median

Note: We recommend using the posterior mean to locate the median justice (med).