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SummaryVery little research has investigated whether believing in paranormal, conspiracy, and pseudoscientific claims are related, even though they share the property of having no epistemic warrant. The present study investigated the association between these categories of epistemically unwarranted beliefs. Results revealed moderate to strong positive correlations between the three categories of epistemically unwarranted beliefs, suggesting that believers in one type tended to also endorse other types. In addition, one individual difference measure, looking at differences in endorsing ontological confusions, was found to be predictive of both paranormal and conspiracy beliefs. Understanding the relationship between peoples' beliefs in these types of claims has theoretical implications for research into why individuals believe empirically unsubstantiated claims. Copyright © 2014 John Wiley & Sons, Ltd.

Recent literature demonstrates that uniformity of population variances and covariances is a sufficient but not a necessary requirement for valid F ratios in repeated measures designs; the tests will be valid if the loss restrictive condition of circularity is satisfied. The circularity assumptions of various repeated measures designs are presented and the empirical literature is reviewed and interpreted in light of these assumptions. An empirical investigation is then presented which compares numerous data analytic strategies when circularity assumptions have been violated, Results indicate that adjusted univariate and multivariate tests are comparable with respect to Type I error control and power. Furthermore, it is shown that by formulating planned comparisons researchers can by pass all or some circularity constraints.

The problem of determining test bias in prediction using regression models is reexamined. Past approaches have made use of separate regression analyses in each subgroup, moderated multiple regression analysis using subgroup coding, and hierarchical multiple regression strategies. Although it is agreed that hierarchical multiple regression analysis is preferable to either of the former methods, the approach presented here differs with respect to the hypothesis testing procedure to be employed in such an analysis. This paper describes the difficulties in testing hypotheses about the existence of bias in prediction using step-up methods of analysis. Some shortcomings of previously recommended approaches for testing these hypotheses are discussed. Finally, a step-down hierarchical multiple regression procedure is recommended. Analysis of real data illustrates the potential usefulness of the step-down procedure.