A statistical test for means of samples from skew populations

This paper presents a test for determining significance of differences between means of samples which are drawn from positively skewed populations, more specifically, those having a Pearson Type III distribution function. The quantity 2npx _g /x _p (wherep equals the mean squared divided by the variance andn is the number of cases in the sample), which distributes itself as Chi Square for 2np degrees of freedom, may be referred to the tables of Chi Square for testing hypotheses about the value of the true mean. For two independent samples, the larger mean divided by the smaller mean, which distributes itself asF for 2n ₁ p ₁ and 2n ₂ p ₂ degrees of freedom, may be referred to theF distribution tables for testing significance of difference between means. The test assumes that the range of possible scores is from zero to infinity. When a lower theoretical score limit (c) exists which is not zero, the quantity (Mean —c) should be used instead of the mean in all calculations.     

A computer program for testing and analyzing random generation behavior in normal and clinical samples: The Mittenecker Pointing Test

Random sequence generation tests have proved to be a useful diagnostic tool for the identification of clinically relevant impairments of executive functions and for the study of cognitive functioning in healthy individuals. The most prevalent variety, random number generation, involves several limitations, however. In the original Mittenecker Pointing Test (MPT; Mittenecker, 1958), subjects were instructed to point successively and as randomly as possible at nine unlabeled circles irregularly arranged on a cardboard. With the computer program presented here, Mittenecker’s classical test has been transferred to a contemporary format. The MPT can be applied using a standard PC keyboard and computes a series of sophisticated measures of deviations from randomness on the basis of information theory analysis. Because of its easy and well-controlled administration and reduced demands on memory and attention, the automatized MPT offers a wide range of application possibilities in normal but also in severely impaired clinical samples.     

The genetic and environmental architecture to the stability of IQ: Results from two independent samples of kinship pairs

Existing research has revealed that IQ remains relatively stable over the life course, though questions remain about how stable IQ is and whether the stability of IQ varies across different developmental periods of the life course. Despite this stability, there are also questions surrounding the factors that might explain the stability of IQ. Against this backdrop, we conduct bivariate genetic models to estimate genetic, shared environmental, and nonshared environmental influences on the stability of IQ. To do so, we analyze kinship pairs drawn from two separate longitudinal samples: The National Collaborative Perinatal Project (CPP) and the National Longitudinal Study of Adolescent Health (Add Health). Across both samples, IQ was found to be relatively stable. Moreover, the genetic analyses revealed that between 66% and 83% of the stability in IQ was due to genetic factors and between 43% and 69% of the change in IQ was due to genetic factors. The remainder of the stability and change in IQ was the result of a combination of shared and nonshared environmental influences. Importantly, some substantive race differences emerged in respect to genetic and environmental influences on the stability of IQ. We conclude with a discussion of the limitations of the study and avenues for future research.     

The mean and variance of the selection differential after top‐down selection from a mixture of normal samples

A procedure is presented for determining the mean and variance of the selection differential for top‐down selections in which the candidates come from populations that have a different average score on the selection measure. Although the procedure is based on the same stochastic model and requires identical data to the currently available method for estimating the mean selection differential, it has the advantage that the resulting expressions are valid for finite‐sample selection decisions and that the variance of the selection differential can also be assessed. The difference between the two procedures is illustrated by means of an example application, and it is shown how the present results are particularly helpful in determining the expected utility of personnel selection decisions.