A test of the equality of standard errors of measurement

A procedure is proposed for testing the significance of group differences in the standard error of measurement of a psychological test. Wilks' criterion is used to assure that the tests used in ascertaining reliability and hence variance of errors of measurement may be assumed parallel for each group. Votaw's criterion may be used to check whether the test scores of all the groups have the same mean, variance, and covariance. It is possible, however, for the variance and reliability of the test to differ widely from group to group, so that Votaw's criterion is not satisfied even though the variance of errors of measurement stays relatively constant. For this case a modification of Neyman and Pearson's criterion is developed to test agreement among standard errors of measurement despite group differences in mean, variance, and reliability of the test.The author wishes to acknowledge the helpful criticisms of Dr. Harold Gulliksen, who suggested the problem.     

What Is Music? Is There a Definitive Answer?

Philosophers frequently defend definitions by appealing to intuitions and contemporary folk classificatory norms. I raise methodological concerns that undermine some of these defenses. Focusing on Andrew Kania's recent definition of music, I argue that the way in which it has been developed leads to problems, and I show that a number of other definitions of interest to philosophers of art (and others) run into similar problems.     

Psychometric properties of the serial colour word test

Abstract.— Lower bound reliabilities for the various parameters of two previously suggested models for the serial colour word test—one linear and one quadratic —are derived and estimated. The quadratic model gave mostly very low reliabilities, and the stronger, linear model also gave only a few high reliabilities. Validity was studied by means of discriminant functions (4 groups being used; 3 clinical and 1 normal). The validity of the test was low. The linear model gave the best results under cross validation.