Biomedical subjects

William P Dunlap

Publications and source records attributed to William P Dunlap.

5 recordsLinked to original sources

PubMed2006

Skew and internal consistency.

The effects of skew on the standardized item alpha were examined with Monte Carlo techniques. Alphas computed from normal variables were compared with alphas from lognormal variables, ranks, and skewed versus normal Likert-type variables. The extent and direction of skew were varied, as was the size of the population interitem correlation (rho), the number of items, and the number of categories for Likert-type variables. Because the average interitem correlation affects alpha and skew affects the average interitem correlation, the effect of skew on the average interitem correlation also was examined. Results indicated that skew decreased the average interitem correlation and produced small decreases in alpha that were largest when skew was large, rho was small, items were skewed in opposite directions, and there were fewer items.

Humans↗

PubMed2004

Computing aspects of power for multiple regression.

Rules of thumb for power in multiple regression research abound. Most such rules dictate the necessary sample size, but they are based only upon the number of predictor variables, usually ignoring other critical factors necessary to compute power accurately. Other guides to power in multiple regression typically use approximate rather than precise equations for the underlying distribution; entail complex preparatory computations; require interpolation with tabular presentation formats; run only under software such as Mathmatica or SAS that may not be immediately available to the user; or are sold to the user as parts of power computation packages. In contrast, the program we offer herein is immediately downloadable at no charge, runs under Windows, is interactive, self-explanatory, flexible to fit the user's own regression problems, and is as accurate as single precision computation ordinarily permits.

Humans↗

PubMed2003

Accurate tests of statistical significance for r(WG) and average deviation interrater agreement indexes.

The authors demonstrated that the most common statistical significance test used with r(WG)-type interrater agreement indexes in applied psychology, based on the chi-square distribution, is flawed and inaccurate. The chi-square test is shown to be extremely conservative even for modest, standard significance levels (e.g., .05). The authors present an alternative statistical significance test, based on Monte Carlo procedures, that produces the equivalent of an approximate randomization test for the null hypothesis that the actual distribution of responding is rectangular and demonstrate its superiority to the chi-square test. Finally, the authors provide tables of critical values and offer downloadable software to implement the approximate randomization test for r(WG)-type and for average deviation (AD)-type interrater agreement indexes. The implications of these results for studying a broad range of interrater agreement problems in applied psychology are discussed.

Humans↗

PubMed2003

Some thoughts regarding the sensitivity of t to error in estimated within-group variance.

A. J. Riopelle (2003) has eloquently demonstrated that the null hypothesis assessed by the t test involves not only mean differences but also error in the estimation of the within-group standard deviation, s. He is correct in his conclusion that the precision of the interpretation of a significant t and the null hypothesis tested is complex, particularly when sample sizes are small. In this article, the author expands on Riopelle's thoughts by comparing t with some equivalent or closely related tests that make the reliance of t on the accurate estimation of error perhaps more salient and by providing a simulation that may address more directly the magnitude of the interpretational problem.

Humans↗

PubMed2002

On the bias of Huffcutt and Arthur's (1995) procedure for identifying outliers in the meta-analysis of correlations.

This study documents how the use of A. I. Huffcutt & W. A. Arthur's (1995) sample adjusted meta-analytic deviancy (SAMD) statistic for identifying outliers in correlational meta-analyses results in inaccuracies in mean r. Monte Carlo simulations found that use of the SAMD resulted in the overidentification of small relative to large correlations as outliers. Furthermore, this tendency to overidentify small correlations was found to increase as the magnitude of the population correlation increased and resulted in mean rs that overestimated the population correlation. The implications for meta-analysts are discussed, and 2 possible solutions are offered.

Bias↗