Estimating the Coefficient of Cross-Validity in Multiple Regression: A Comparison of Analytical and Empirical Methods

Kromrey, Jeffrey D.; Hines, Constance V.

doi:10.1080/00220973.1996.9943806

Cited by 20 publications

(29 citation statements)

References 17 publications

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“…In previous studies of statistical bias, researchers have operationally defined unbiased estimators as those estimators which produce values within +0.01 and -0.01 of the corresponding parameter values (Kromrey & Hines, 1996;Yin & Fan, 2001). We used the same criterion in the present study (although Wang & Thompson, 2007, did not) to describe an unbiased estimate.…”

Section: Discussionmentioning

confidence: 93%

Choosing the Best Correction Formula for the Pearsonr²Effect Size

Skidmore

Thompson

2011

The Journal of Experimental Education

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In the present Monte Carlo simulation study, the authors compared bias and precision of 7 sampling error corrections to the Pearson r 2 under 6 × 3 × 6 conditions (i.e., population ρ values of 0.0, 0.1, 0.3, 0.5, 0.7, and 0.9, respectively; population shapes normal, skewness = kurtosis = 1, and skewness = -1.5 with kurtosis = 3.5; ns = 10, 20, 40, 60, 100, and 200, respectively). Limited previous studies focused primarily on the efficacy only of multiple R 2 corrections applied to the Pearson r 2 . The authors' results indicate that the Pratt and the Olkin-Pratt Extended corrections more consistently provided unbiased estimates across the sample sizes, ρ values, and shape conditions that they investigated, although the Ezekiel correction arguably is also reasonable. The precisions of the estimates were homogeneous across the 108 simulation conditions. EDUCATION RESEARCHERS ARE IN the business of cumulating knowledge across studies to inform judgments about educational practices, programs and policies. And even when we are conducting a single study rather than a metaanalysis, we must apply "meta-analytic thinking" (Cumming & Finch, 2001, p. 532). Thompson (2002b) defined meta-analytic thinking as both (a) the prospective formulation of study expectations and design by explicitly invoking prior effect sizes and (b) the retrospective interpretation of new results,

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Section: Discussionmentioning

confidence: 93%

Choosing the Best Correction Formula for the Pearsonr²Effect Size

Skidmore

Thompson

2011

The Journal of Experimental Education

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“…As noted by Kromrey and Hines (1996), regression weights that are developed in one sample and applied to a new sample will almost always yield a smaller explained variance coefficient of effect size. Pedhazur (1982) recommended the splitting of one sample into two samples, in the absence of multiple samples, to address effect size variation issues.…”

Section: Samplementioning

confidence: 97%

Return-to-Work Outcome Rates of African American Versus White Veterans Served by State Vocational Rehabilitation Agencies

Moore

Wang

Johnson

et al. 2015

Rehabilitation Counseling Bulletin

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Hispanic, and individuals from the Other racial category make up 80%, 0.6%, 1.3%, 6%, and 1.4%, respectively, of the universe of veterans. An estimated 15.4% of GWOT, 16.9% of Persian War veterans, and 8.9% of Vietnam War era veterans are African Americans. African American female veterans make up about 20.1% of all women veterans. Remarkably, nearly 1 in every 3, or 33%, of veterans who served in the Persian Gulf War I and GWOT were African American (National Center for Veterans Analysis and Statistics, 2013).Thousands of these veterans have returned home with more than honor, having sustained service-connected physical and/or mental disabilities (Feist

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“…Thus, these are the three factors invoked in the statistical correction for the positive bias in uncorrected effect sizes, which attempt to remove this bias and produce more accurate effect estimates (see Kromrey and Hines, 1996). Examples of such estimates are the regression "adjusted R 2 " and the ANOVA omega 2 ( 2 ) and epsilon 2 (⑀ 2 ).…”

Section: Corrected Variance-accounted-for Statisticsmentioning

confidence: 99%

Higher Education: Handbook of Theory and Research

2005

Higher Education: Handbook of Theory and Research

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University of Twente) as a senior researcher in 1993, he has done research on a wide range of projects, mostly in the area of higher education economics. He has written extensively on topics such as funding methodologies, marketization, financial management, and per student costs. As a staff member of CHEPS he has been involved in commissioned research projects, training seminars and consultancies on the area of higher education policy and institutional management.

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Estimating the Coefficient of Cross-Validity in Multiple Regression: A Comparison of Analytical and Empirical Methods

Cited by 20 publications

References 17 publications

Choosing the Best Correction Formula for the Pearsonr²Effect Size

Choosing the Best Correction Formula for the Pearsonr²Effect Size

Return-to-Work Outcome Rates of African American Versus White Veterans Served by State Vocational Rehabilitation Agencies

Higher Education: Handbook of Theory and Research

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Estimating the Coefficient of Cross-Validity in Multiple Regression: A Comparison of Analytical and Empirical Methods

Cited by 20 publications

References 17 publications

Choosing the Best Correction Formula for the Pearsonr2Effect Size

Choosing the Best Correction Formula for the Pearsonr2Effect Size

Return-to-Work Outcome Rates of African American Versus White Veterans Served by State Vocational Rehabilitation Agencies

Higher Education: Handbook of Theory and Research

Contact Info

Product

Resources

About

Choosing the Best Correction Formula for the Pearsonr²Effect Size

Choosing the Best Correction Formula for the Pearsonr²Effect Size