Browsing by Author "Callender, John Clyde"
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Item A Monte Carlo investigation of the accuracy of two models for validity generalization(1978) Callender, John Clyde; Osburn, Hobart G.; Campion, James E.; Maxwell, Scott E.; Blakeney, Roger N.A Multiplicative Model for the generalization of validity developed by Callender and Osburn (1978) was tested by Monte Carlo methods. The model provides a method for estimating the mean and variance of distributions of unattenuated unrestricted population validities by removing the effects of range restriction, criterion unreliability and chance sampling error. The model generally builds on concepts previously outlined by Schmidt and Hunter (1977). The accuracy of the model was compared with the model originally proposed by Schmidt and Hunter on hypothetical infinite sample size cases and on small sample size cases (N= 30, 68, 200) by means of computer simulation of sample restricted attenuated data sets. The correctness of the computer simulation was verified analytically and empirically. Both models were than applied to estimate the known mean and variance of the population distributions of unrestricted unattenuated validities. The Multiplicative Model was found to give reasonably accurate estimates of the mean and variance of the population correlations and of lower credibility values. The errors made by the model were generally conservative, tending to overestimate the variance of the population correlations. The Schmidt-Hunter model was less accurate and in some conditions made substantial nonconservative errors. It was concluded that the Multiplicative Model could be recommended for future use in validity generalization analysis. A Monte Carlo study of the accuracy for a formula for the standard error of a correlation was also conducted. Results indicated that it was nearly as accurate as the more familiar Fisher's z formula. The Multiplicative Model was applied to four distributions of validities summarized by Ghiselli (1966). It was found that the variation in true validities was greater in each case than that previously reported by Schmidt and Hunter (1977). The analysis indicated that validity generalization was supported for two of the four distributions.Item The estimation of the reliability of composite measures by optimized splits(1976) Callender, John Clyde; Osburn, Hobart G.; Brown, Eric R.; Frankiewicz, Ronald G.The primary purpose of the study was to compare maximized split-half reliability coefficients, the traditional Kuder-Richardson 20 or Alpha coefficient, and Guttman's coefficient. A related objective was to evaluate the effectiveness of a computer program which maximizes split-half coefficients. A number of hypothetical 10-item and 40-item tests were constructed from the total of 100 items which make up Forms S and T of the Test of Chemical Comprehension. Responses of 380 individuals who had taken both forms of the test provided the data for the study. Sampling errors were introduced by repeated divisions of the 380 individuals into two complementary samples of 190 each. The effects of sampling error on the three types of reliability coefficients were investigated for each item set by comparing original sample and cross-validated coefficients with the coefficient obtained in the total group. The major findings were: (a) The computer program produced split-half coefficients which were only slightly smaller than the largest of all possible coefficients, but did not produce halves which had equal means and variances. (b) Maximized split-half coefficients exceeded both and Alpha coefficients by average amounts ranging from .05 to .20. Larger differences between the coefficients were found in less homogeneous sets of items. (c) Sample L[lowered 2] and Alpha coefficients underestimated and sample maximized split-half coefficients overestimated the largest reliability coefficient in the total group. It was concluded that the population reliability may be estimated more accurately by applying cross-validation procedures to maximized split-half coefficients than by computing the other coefficients, but that additional research with an expanded sampling design is needed to adequately test this possibility.