"The goal of this proposal is to develop and promote Bayesian hypothesis tests for social scientists. By and large, social scientists have ignored the Bayesian revolution in statistics, and, consequently, most social scientists still assess the veracity of experimental effects using the same methodology that was used by their advisors and the advisors before them. This state of affairs is undesirable: social scientists conduct groundbreaking, innovative research only to analyze their results using methods that are old-fashioned or even inappropriate. This imbalance between the science and the statistics has gradually increased the pressure on the field to change the way inferences are drawn from their data. However, three requirements need to be fulfilled before social scientists are ready to adopt Bayesian tests of hypotheses. First, the Bayesian tests need to be developed for problems that social scientists work with on a regular basis; second, the Bayesian tests need to be default or objective; and, third, the Bayesian tests need to be available in a user-friendly computer program. This proposal seeks to make major progress on all three fronts.Concretely, the projects in this proposal build on recent developments in the field of statistics and use the default Jeffreys-Zellner-Siow priors to compute Bayesian hypothesis tests for regression, correlation, the t-test, and different versions of analysis of variance (ANOVA). A similar approach will be used to develop Bayesian hypothesis tests for logistic regression and the analysis of contingency tables, as well as for popular latent process methods such as factor analysis and structural equation modeling. We aim to implement the various tests in a new computer program, Bayes-SPSS, with a similar look and feel as the frequentist spreadsheet program SPSS (i.e., Statistical Package for the Social Sciences). Together, these projects may help revolutionize the way social scientists analyze their data."