Sampling variance in attenuated correlation coefficients: a Monte Carlo study
Article Abstract:
Research in validity generalization has generated renewed interest in the sampling error of the Pearson correlation coefficient. The standard estimator for the sampling variance of the correlation was derived under assumptions that do not consider the presence of measurement error or range restriction in the data. The accuracy of the estimator in attenuated or restricted data has not been studied. This article presented the results of computer simulations that examined the accuracy of the sampling variance estimator in data containing measurement error. Sample sizes of n = 25, n = 60, and n = 100, are used, with the reliability ranging from .10 to 1.00, and the population correlation ranging from .10 to 0.90. Results demonstrated that the estimator has a slight negative bias, but may be sufficiently accurate for practical applications if the sample size is at least 60. In samples of this size, the presence of measurement error does not add greatly to the inaccuracy of the estimator. (Reprinted by permission of the publisher.)
Publication Name: Journal of Applied Psychology
Subject: Social sciences
ISSN: 0021-9010
Year: 1988
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Bias in validity generalization variance estimates: a reply to Hoben Thomas
Article Abstract:
In a recent article Thomas (1988) derived the expected value of the true variance estimate used in validity generalization studies. Based on computations of the expected values for certain scenarios, Thomas made a number of critical assertions regarding the variance estimate. This article shows that Thomas's arguments regarding deficiencies in the variance estimate used in validity generalization studies are misleading. Contrary to Thomas's extremely negative assessment of the situation, there is no really convincing reason to doubt or abandon the estimates of true validity variance obtained in applied research from the Callender-Osburn and other closely related methods. Rather, there is strong evidence to indicate that populations of true validities with meaningful differences in mean and variance can be reliably distinguished, provided that a sufficient amount of base data are available. (Reprinted by permission of the publisher.)
Publication Name: Journal of Applied Psychology
Subject: Social sciences
ISSN: 0021-9010
Year: 1990
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Unbiased estimation of sampling variance of correlations
Article Abstract:
In a recent article, P.E. Spector and E. L. Levine (1987) asserted that the estimate of sampling error variance used in validity generalization studies is biased when the number of correlations is relatively small. In addition, Spector and Levine maintained that the bias is such that the sampling error variance estimate seriously overestimates the actual variance of observed correlations. A partial replication of Spector and Levine's study showed that the alleged bias was due to a distributional artifact and that the sampling error estimate is not seriously biased. We review evidence from several Monte Carlo studies indicating that the sampling error estimate is quite accurate. (Reprinted by permission of the publisher.)
Publication Name: Journal of Applied Psychology
Subject: Social sciences
ISSN: 0021-9010
Year: 1988
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