A brief comment on Thurstone's note on a reanalysis of Davis' reading tests

A comparison of factorial analyses made by Thurstone and by the writer shows that they differ in three important respects. The results of the two analyses are compared with respect to their social utility, which is offered as a proper criterion for judging the merit of factorial analyses performed by different mathematical procedures.     

A note on preliminary tests of equality of variances

Preliminary tests of equality of variances used before a test of location are no longer widely recommended by statisticians, although they persist in some textbooks and software packages. The present study extends the findings of previous studies and provides further reasons for discontinuing the use of preliminary tests. The study found Type I error rates of a two‐stage procedure, consisting of a preliminary Levene test on samples of different sizes with unequal variances, followed by either a Student pooled‐variances t test or a Welch separate‐variances t test. Simulations disclosed that the twostage procedure fails to protect the significance level and usually makes the situation worse. Earlier studies have shown that preliminary tests often adversely affect the size of the test, and also that the Welch test is superior to the t test when variances are unequal. The present simulations reveal that changes in Type I error rates are greater when sample sizes are smaller, when the difference in variances is slight rather than extreme, and when the significance level is more stringent. Furthermore, the validity of the Welch test deteriorates if it is used only on those occasions where a preliminary test indicates it is needed. Optimum protection is assured by using a separate‐variances test unconditionally whenever sample sizes are unequal.     

A note on correcting for chance success in objective tests

The conventional scoring formula to correct for guessing is derived and is compared with a regression method for scoring which has been recently proposed by Hamilton. It is shown that the usual formula,S=R–W/(n–1), yields a close approximation (correct within one point) to the maximum-likelihood estimate of an individual's true score on the test, if we assume that the individual knows or does not know the answer to each item, that guessing at unknown items is random, and that success at guessing is governed by the binomial law. It is also shown that the usual scoring formula yields an unbiased estimate of the individual's true score, when the true score is defined as the mean score over an indefinitely large number of independent attempts at the test or at equivalent (parallel) tests.     

A note on permutation tests of significance for multiple regression coefficients

In the vast majority of psychological research utilizing multiple regression analysis, asymptotic probability values are reported. This paper demonstrates that asymptotic estimates of standard errors provided by multiple regression are not always accurate. A resampling permutation procedure is used to estimate the standard errors. In some cases the results differ substantially from the traditional least squares regression estimates.