The Publication Manual of the American Psychological Association (American Psychological Association, 2001, American Psychological Association, 2010) calls for the reporting of effect sizes and their confidence intervals. Estimates of effect size are useful for determining the practical or theoretical importance of an effect, the relative contributions of factors, and the power of an analysis. We surveyed articles published in 2009 and 2010 in the Journal of Experimental Psychology: General, noting the statistical analyses reported and the associated reporting of effect size estimates. Effect sizes were reported for fewer than half of the analyses; no article reported a confidence interval for an effect size. The most often reported analysis was analysis of variance, and almost half of these reports were not accompanied by effect sizes. Partial η2 was the most commonly reported effect size estimate for analysis of variance. For t tests, 2/3 of the articles did not report an associated effect size estimate; Cohen's d was the most often reported. We provide a straightforward guide to understanding, selecting, calculating, and interpreting effect sizes for many types of data and to methods for calculating effect size confidence intervals and power analysis.

Effect size estimates: Current use, calculations, and interpretation.

The statistical analysis of mediation effects has become an indispensable tool for helping scientists investigate processes thought to be causal. Yet, in spite of many recent advances in the estimation and testing of mediation effects, little attention has been given to methods for communicating effect size and the practical importance of those effect sizes. Our goals in this article are to (a) outline some general desiderata for effect size measures, (b) describe current methods of expressing effect size and practical importance for mediation, (c) use the desiderata to evaluate these methods, and (d) develop new methods to communicate effect size in the context of mediation analysis. The first new effect size index we describe is a residual-based index that quantifies the amount of variance explained in both the mediator and the outcome. The second new effect size index quantifies the indirect effect as the proportion of the maximum possible indirect effect that could have been obtained, given the scales of the variables involved. We supplement our discussion by offering easy-to-use R tools for the numerical and visual communication of effect size for mediation effects.

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Effect size measures for mediation models: quantitative strategies for communicating indirect effects.

We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data- analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.

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The New Statistics Why and How

Objective: We evaluate and quantify the effects of human, robot, and environmental factors on perceived trust in human-robot interaction (HRI)Background: To date, reviews of trust in HRI have been

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A meta-analysis of factors affecting trust in human-robot interaction.

The calculation and use of effect sizes—such as d for mean differences and r for correlations—has increased dramatically in second language (L2) research in the last decade. Interpretations of these effects, however, have been rare and, when present, have largely defaulted to Cohen's levels of small (d = .2, r = .1), medium (.5, .3), and large (.8, .5), which were never intended as prescriptions but rather as a general guide. As Cohen himself and many others have argued, effect sizes are best understood when interpreted within a particular discipline or domain. This article seeks to promote more informed and field-specific interpretations of d and r by presenting a description of L2 effects from 346 primary studies and 91 meta-analyses (N > 604,000). Results reveal that Cohen's benchmarks generally underestimate the effects obtained in L2 research. Based on our analysis, we propose a field-specific scale for interpreting effect sizes, and we outline eight key considerations for gauging relative magnitude and practical significance in primary and secondary studies, such as theoretical maturity in the domain, the degree of experimental manipulation, and the presence of publication bias.

How Big Is “Big”? Interpreting Effect Sizes in L2 Research

Similar to previous editions, the Publication Manual of the American Psychological Association (APA), Sixth Edition provides guidelines on all aspects of writing style and formatting for writers, e

Kate E Decleene

Papers

Publication Manual of the American Psychological AssociationPublication Manual of the American Psychological Association.