If the publication decisions of journals are a function of the statistical significance of research findings, the published literature may suffer from “publication bias.” This paper describes a method for detecting publication bias. We point out that to achieve statistical significance, the effect size must be larger in small samples. If publications tend to be biased against statistically insignificant results, we should observe that the effect size diminishes as sample sizes increase. This proposition is tested and confirmed using the experimental literature on voter mobilization.

This article uses spatial voting theory to analyze the properties of linear regressions that employ interest group ratings as measures of legislator policy preferences. Such regressions, in general, yield inconsistent results. In particular, least-squares estimation of a bivariate regression which contains an interest group rating as a regressor produces an inflated slope estimate. Instrumenting for the rating with a second rating, as proposed by Brunell et al. (1999), does not fix this problem, and this is because errors in both sets of ratings are correlated. Finally, estimation of a trivariate regression that contains an interest group rating and a party indicator on its right-hand side yields inconsistent slope estimates and, in particular, a party coefficient estimate of unreliable sign. Hence, regressions including both ratings and party indicators are not useful tools in the debate on whether party affiliation has an independent impact on legislator behavior.

This paper presents a method for inferring the distribution of voter ideal points on a single dimension from individual-level binary choice data. The statistical model and estimation technique draw heavily on the psychometric literature on test taking and, in particular, on the work of Bock and Aitkin (1981) and are similar to several recent methods of estimating legislative ideal points (Londregan 2000; Bailey 2001). I present Monte Carlo results validating the method. The method is then applied to determining the partisan and ideological basis of support for presidential candidates in 1992 and to U.S. mass and congressional partisan realignment on abortion policy since 1973.