Four pure types of legislative organization are characterized as data generating processes for commonly used measures of preferences or, in the spatial vernacular, ideal points. The types of legislative organization are differentiated by their partisan versus nonpartisan nature of agenda formation, and by whether the amendment process is open or closed. For each organization, roll call voting data are Monte-Carlo generated and used as input for four different ideal point measures: standard percent-correct interest group ratings (IGRs), linear factor analysis scores (LFAs), W-NOMINATE ratings (NOMs), and Markov chain Monte Carlo measures (MCMCs). Three questions motivate and are addressed the analysis. Do estimated ideal points differ significantly across forms of legislative organization? Are some ideal point estimates consistently more accurate than others? Are there patterns of substantively relevant, persistent bias in ideal point estimates? The answers are all affirmative.