
Roll-call voting data is a cornerstone of legislative analysis but often overlooks non-response. This study explores how missing votes, previously dismissed as ignorable noise, actually contain crucial information about party divisions and strategic behavior. By implementing novel methods to handle incomplete voting records systematically, rather than simply discarding them or treating them as random errors, we demonstrate that failing to account for this pattern distorts our understanding of legislative politics significantly.
Researchers analyzed roll-call vote datasets from the US House and Senate covering several decades. Using advanced statistical models—specifically multiple imputation techniques—they quantified how patterns in missing votes correlated with partisan alignment, committee jurisdiction, and procedural complexity across different sessions. The approach moved beyond traditional assumptions by treating non-response not as measurement error but potentially as informative signal.
This finding fundamentally challenges conventional approaches to using roll-call data. It suggests scholars must reassess how they interpret voting records, particularly regarding claims about democratic processes like accountability (representatives' responsiveness to constituents) and polarization (increasing ideological sorting). The results highlight a persistent blind spot in political methodology.

| No News Is News: Non-Ignorable Non-Response in Roll-Call Data Analysis was authored by Guillermo Rosas, Yael Shomer and Stephen Haptonstahl. It was published by Wiley in AJPS in 2015. |