Repeated challenge studies: A comparison of union-intersection testing with linear modeling

Challenge studies are often implemented for assessing whether a subject is sensitive to a certain agent or allergen. In particular, researchers test groups of subjects to determine if there really exists a causal relationship between some agent of interest and a response. To answer such a question, we need to detect the presence of the phenomenon in just one individual. Typically, however, there are a large number of unknown risk factors associated with the response and a potentially small population prevalence. Hence, standard statistical techniques, by averaging the treatment effect across the group, may miss a significant response of a single individual and lead to inconclusive results. We develop an alternative approach based on union-intersection testing that will allow a practitioner to correctly examine observations on an individual apart from the other subjects and test the hypothesis of interest: Does the phenomenon exist in the population? More specifically, we show how this technique adjusts for the multiple number of tests encountered when analyzing data for each individual subject separately. Furthermore, we demonstrate power calculations for the determination of sample size prior to performing the study. The performance of the union-intersection approach in comparison to linear models and semiparametric techniques is considered through sample size calculations and simulations. The union-intersection testing methodology out performs the Kolmogorov tests. However, the nested linear model performs as well if not better than the union-intersection tests. To illustrate the ideas presented in the paper, we provide an application in which we analyze psychological data collected by way of a challenge study design.     

Testing the significance of the successive components in redundancy analysis

In this paper we study the interrelationships between two sets of data measured on the same subjects via redundancy analysis. We consider redundancy analysis from an inferential point of view. Under the hypothesis of multinormality, tests of significance are obtained for each successive redundancy component so that only the significant factors are retained for prediction purposes. An example illustrates the method. The authors would like to thank the Editor and the referees for their helpful comments. This research has been partly financed by NSERC (Canada).     

Interpreting canonical correlation analysis through biplots of structure correlations and weights

This paper extends the biplot technique to canonical correlation analysis and redundancy analysis. The plot of structure correlations is shown to the optimal for displaying the pairwise correlations between the variables of the one set and those of the second. The link between multivariate regression and canonical correlation analysis/redundancy analysis is exploited for producing an optimal biplot that displays a matrix of regression coefficients. This plot can be made from the canonical weights of the predictors and the structure correlations of the criterion variables. An example is used to show how the proposed biplots may be interpreted.     

On kristof's test for a linear relation between true scores of two measures

The hypothesis that two variables have a perfect disattenuated correlation and hence measure the same trait, except for errors of measurement, is discussed. Equivalently, the underlying variables, the true scores, are related linearly. We show that several previously proposed ad hoc tests are in fact likelihood ratio tests. The cases when the linear relation is specified and when it is unspecified are both discussed.This work was done while the author was at Purdue University Under Air Force Grant AFOSR-72-2350B.     

The rowwise correlation between two proximity matrices and the partial rowwise correlation

This paper discusses rowwise matrix correlation, based on the weighted sum of correlations between all pairs of corresponding rows of two proximity matrices, which may both be square (symmetric or asymmetric) or rectangular. Using the correlation coefficients usually associated with Pearson, Spearman, and Kendall, three different rowwise test statistics and their normalized coefficients are discussed, and subsequently compared with their nonrowwise alternatives like Mantel'sZ. It is shown that the rowwise matrix correlation coefficient between two matricesX andY is the partial correlation between the entries ofX andY controlled for the nominal variable that has the row objects as categories. Given this fact, partial rowwise correlations (as well as multiple regression extensions in the case of Pearson's approach) can be easily developed.The author wishes to thank the Editor, two referees, Jan van Hooff, and Ruud Derix for their useful comments, and E. J. Dietz for a copy of the algorithm of the Mantel permutation test.     

The influence of correlations between noncritical features and reinforcement on stimulus generalization

One strategy to program for generalization is to vary noncritical features in teaching exemplars, thereby avoiding noncritical features from being highly correlated with reinforcement and thus gaining faulty stimulus control. In the current translational evaluation, 2 groups of adults of typical development were taught to respond to arbitrary stimuli with experimenter‐defined critical and noncritical features in a matching‐to‐sample task. The teaching arrangement used for 1 group programmed for low correlation between noncritical features and reinforcement; the teaching arrangement used for the other group programmed for high correlation between noncritical features and reinforcement. Participants in the former group displayed (a) faster acquisition of matching, (b) less variability in correct responding, and (c) a decreased likelihood of faulty stimulus control developing during training. The results contribute towards advancing the study of stimulus control and developing an explicit technology of generalization to better serve consumers of the application of our science.     

The influence of vestibular system and fetal presentation on handedness,cognitive and motor development: A comparison between cephalic and breech presentation

Genetics are undoubtedly implicated in the ontogenesis of laterality. Nonetheless, environmental factors, such as the intrauterine environment, may also play a role in the development of functional and behavioral lateralization. The aim of this study was to test the Left-Otolithic Dominance Theory (LODT; Previc, 1991) by investigating a hypothetical developmental pattern where it is assumed that a breech presentation, which is putatively associated with a dysfunctional and weakly lateralized vestibular system, can lead to weak handedness and atypical development associated with language and motor difficulties. We used the ALSPAC cohort of children from 7 to 10 years of age to conduct our investigation. Our results failed to show an association between the vestibular system and fetal presentation, nor any influence of the latter on hand preference, hand performance, or language and motor development. Bayesian statistical analyses supported these findings. Contrary to our LODT-derived hypotheses, this study offers evidence that fetal presentation does not influence the vestibular system's lateralization and seems to be a poor indicator for handedness. Nonetheless, we found that another non-genetic factor, prematurity, could lead to atypical development of handedness.