Meta-analysis in JASP

The JASP meta-analysis module was supported by a SSMART grant from the Berkeley Initiative for Transparency in the Social Sciences (BITSS), an initiative of the Center for Effective Global Action (CEGA). The new release of JASP supports an extensive arrange of commonly used techniques for meta-analysis. These include fixed and random effects analysis, fixed and mixed effects meta-regression, forest and…

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How to Test Interval-Null Hypotheses in JASP

As discussed on the Bayesian Spectacles blog, some statisticians have an visceral dislike of the point-null hypothesis, fueled in part by the conviction that the point-null is never true. Back in 1938, Berkson (pp. 526-527) already summarized the argument: “I believe that an observant statistician who has had any considerable experience with applying the chi-square test repeatedly will agree with…

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How to Do a Hierarchical Regression in JASP

The latest JASP version, 0.8.3, introduced a plethora of new features, including hierarchical regression. This blog post briefly describes this analysis. In traditional linear regression, predictors are selected that form a statistical model; this model is then compared to the null model that includes only the intercept term. Performance of the specified model is then assessed by metrics such as…

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Exact P-Values Upon Request: Breaking with an APA Guideline

The 6th edition of the APA style manual (American Psychological Association, 2010) states the following on the topic of reporting p-values: “When reporting p values, report exact p values (e.g., p = .031) to two or three decimal places. However, report p values less than .001 as p < .001. The tradition of reporting p values in the form p…

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The Mysterious VS-MPR

For its frequentist analyses, JASP offers familiar output: effect sizes, confidence intervals, and p-values. However, JASP also provides a more mysterious measure: the "Vovk-Sellke maximum p-ratio" or VS-MPR.

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