Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

The observed probability "p" is the social scientist's primary tool for evaluating the outcome of statistical hypothesis tests. The small-sample accuracy of nonnull asymptotic distributions of several functions of "p" was studied. Implications for use of the approximations are discussed. (SLD)

Asserts that traditional approaches to multivariate analysis, including standard linear regression techniques, ignore the special character of count data. Explicates three suitable alternatives to standard regression techniques, a simple Poisson regression, a modified Poisson regression, and a negative binomial model. (MJP)