Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA’s such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.

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Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

The use of interrater reliability (IRR) and interrater agreement (IRA) indices has increased dramatically during the past 20 years. This popularity is, at least in part, because of the increased role of multilevel modeling techniques (e.g., hierarchical linear modeling and multilevel structural equation modeling) in organizational research. IRR and IRA indices are often used to justify aggregating lower-level data used in composition models. The purpose of the current article is to expose researchers to the various issues surrounding the use of IRR and IRA indices often used in conjunction with multilevel models. To achieve this goal, the authors adopt a question-and-answer format and provide a tutorial in the appendices illustrating how these indices may be computed using the SPSS software.

Answers to 20 Questions About Interrater Reliability and Interrater Agreement

List of figures List of tables List of boxes Introduction Part I. Effect Sizes and the Interpretation of Results: 1. Introduction to effect sizes 2. Interpreting effects Part II. The Analysis of Statistical Power: 3. Power analysis and the detection of effects 4. The painful lessons of power research Part III. Meta-Analysis: 5. Drawing conclusions using meta-analysis 6. Minimizing bias in meta-analysis Last word: thirty recommendations for researchers Appendices: 1. Minimum sample sizes 2. Alternative methods for meta-analysis Bibliography Index.

The essential guide to effect sizes : statistical power, meta-analysis, and the interpretation of research results

The Resistance to Change Scale was designed to measure an individual's dispositional inclination to resist changes. In Study 1, exploratory analyses indicated 4 reliable factors: Routine Seeking, Emotional Reaction to Imposed Change, Cognitive Rigidity, and Short-Term Focus. Studies 2, 3, and 4 confirmed this structure and demonstrated the scale's convergent and discriminant validities. Studies 5, 6, and 7 demonstrated the concurrent and predictive validities of the scale in 3 distinct contexts. The scale can be used to account for the individual-difference component of resistance to change and to predict reactions to specific change.

/pdf/resistance-to-change-developing-an-individual-differences-3waxnpz7dj.pdf

Resistance to change: Developing an individual differences measure.

This paper presents and develops a quantitative method of literature reviewing and evaluating empirical research, meta-regression analysis or MRA. Economics is theory-driven. Yet, we must learn empirically if economics is to advance. MRA offers a more objective statistical method to summarize our empirical knowledge and to explain the wide study-to-study variation in economic research. MRA is used to assess the evidence for Ricardian equivalence, and its findings are contrasted to those of conventional narrative review. Furthermore, MRA establishes a platform from which to distinguish genuine empirical effect from the exploitable specification error that infests applied econometrics.

https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.15.3.131

Wheat From Chaff: Meta-Analysis As Quantitative Literature Review

1. The Power of Statistical Tests. 2. A Simple and General Model for Power Analysis. 3. Power Analyses for Minimum-Effect Tests. 4. Using Power Analyses. 5. Correlation and Regression. 6. t-Tests and the Analysis of Variance. 7. Multi-Factor ANOVA Designs. 8. Split-Plot Factorial and Multivariate Analyses. 9. The Implications of Power Analyses. Appendices.

Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests

Statistical power analysis , Statistical power analysis , کتابخانه دیجیتال جندی شاپور اهواز

Statistical power analysis

Researchers are often interested in testing the hypothesis that the effects of treatments, interventions, and so on are negligibly small rather than testing the hypothesis that treatments have no effect whatsoever. A number of procedures for conducting such tests have been suggested but have yet to be widely adopted. In this article, simple methods of testing such minimum-effect hypotheses are illustrated in a variety of applications of the general linear model. Tables and computational routines that can be used in conjunction with the familiar F test to evaluate the hypothesis that the effects of treatments or interventions exceed some minimum level are also provided. One of the most common statistical procedures in the behavioral and social sciences is to test the hypothesis that treatments or interventions have no effect, or that the correlation between two variables is equal to zero, and so on. Cohen (1994) referred to these procedures as "nil hypothesis" tests, a label that differentiates them from the more general category of null hypothesis tests (which allow researchers to test the hypothesis that the difference between two treatments is equal to any specific figure, including but not limited to zero) and that makes it explicit that this particular class of tests is used to evaluate the plausibility of the hypothesis that treatments or interventions have no true effect whatsoever. Although nil hypothesis tests are extremely common, there is a substantial controversy about their value and meaning (Chow, 1988; Cohen, 1994; Cortina & Dunlap, 1997; Cowles, 1989; Harlow, Mulaik, & Steiger, 1997; Meehl, 1978; Morrison & Henkel, 1970; Murphy, 1990; Schmidt, 1992, 1996). One of the many criticisms of nil hypothesis tests is that they ask the wrong question. That is, these tests ask whether treatments, interventions, and so forth have any effect. The question many researchers (especially those interested in the application of science to solve practical problems) want to ask is whether the

Testing the hypothesis that treatments have negligible effects : Minimum-effect tests in the general linear model

Selection outcomes under banding are affected by characteristics of the selection system and the applicant pool. This study examined the effects of eight parameters on the proportions hired from higher- and lower-scoring groups: (a) selection ratio; (b) reliability; (b) fixed vs. sliding bands; (d) top-down vs. random within-band selection; (e) preferential vs. nonpreferential selection; (f) mean differences; (g) standard deviation differences; and (h) proportion of applicants from the lower-scoring group. Simulation results were analyzed in a fully-crossed eight-way ANOVA. Higher-order interactions among selection system and applicant pool characteristics had virtually no effect on selection outcomes; the proportion of the applicant pool from the lower-scoring group accounted for nearly half the variance in out-comes. Other important effects are, in order, the effects of standard deviation differences, mean differences, preferential hiring, and the selection ratio. Applicant pool characteristics have considerably more influence on selection outcomes than do selection system characteristics.

Modeling the effects of banding in personnel selection

Schmidt (1991) argued that all banding procedures in personnel selection are fatally flawed logically (p. 265). The basis of his critique is the inconsistency inherent in banding, that is, that large test score differences within bands are ignored, whereas small differences between scores just within and just outside of the band are treated as meaningful. This critique is well-founded, but it does not by itself constitute a fatal logical flaw, nor is it in any way unique to banding. Rather, a wide range of measurement and decision-making systems commonly used in the behavioral sciences suffer from the same inconsistency exhibited by banding. It is illogical to reject banding on the grounds of this inconsistency, but to accept the results of a wide range of measurement and decision-making systems that share this same basic flaw. Our analyses suggest that the logical critique advanced by Schmidt does not provide a basis for making decisions about the appropriateness of banding.

Brett Myors

Papers

Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests

Statistical power analysis

Testing the hypothesis that treatments have negligible effects : Minimum-effect tests in the general linear model

Modeling the effects of banding in personnel selection

Evaluating the Logical Critique of Banding