A case of extreme simplicity of the core matrix in three-mode principal components analysis

In three-mode Principal Components Analysis, theP ×Q ×R core matrixG can be transformed to simple structure before it is interpreted. It is well-known that, whenP=QR,G can be transformed to the identity matrix, which implies that all elements become equal to values specified a priori. In the present paper it is shown that, whenP=QR − 1,G can be transformed to have nearly all elements equal to values spectified a priori. A cllsed-form solution for this transformation is offered. Theoretical and practical implications of this simple structure transformation ofG are discussed. Constructive comments from anonymous reviewers are gratefully acknowledged.     

Contrast and undermatching with regular or irregular alternation of components

Behavioral contrast and response-ratio sensitivity to reinforcement were compared in multiple schedules in which components alternated strictly or according to a pseudorandom sequence. Average component durations in the two regimes were always 60 s, and order of presentation of component alternation regimes was counterbalanced across subjects. In Part 1, the reinforcer rate in one component was reduced from 60 per hour to zero, while that in the other component was unchanged. Positive behavioral contrast occurred in the constant component in that response rates increased, but neither the reliability nor the magnitude of contrast was affected by the manner in which components alternated. Part 2 was similar, except that a number of different reinforcer rates were used in the varied component. Neither contrast nor sensitivity of response ratios to changes in reinforcer ratios depended on the regime of component alternation. Thus, the predictability in time of future reinforcement conditions, which is a feature of regular multiple scheduling, does not appear to be a determinant of multiple-schedule performance.     

Simplimax: Oblique rotation to an optimal target with simple structure

Factor analysis and principal component analysis are usually followed by simple structure rotations of the loadings. These rotations optimize a certain criterion (e.g., varimax, oblimin), designed to measure the degree of simple structure of the pattern matrix. Simple structure can be considered optimal if a (usually large) number of pattern elements is exactly zero. In the present paper, a class of oblique rotation procedures is proposed to rotate a pattern matrix such that it optimally resembles a matrix which has an exact simple pattern. It is demonstrated that this method can recover relatively complex simple structures where other well-known simple structure rotation techniques fail.This research has been made possible by a fellowship from the Royal Netherlands Academy of Arts and Sciences. The author is obliged to Jos ten Berge for helpful comments on an earlier version.     

Linear relations amongk sets of variables

A family of solutions for linear relations amongk sets of variables is proposed. It is shown how these solutions apply fork=2, and how they can be generalized from there tok3.The family of solutions depends on three independent choices: (i) to what extent a solution may be influenced by differences in variances of components within each set; (ii) to what extent the sets may be differentially weighted with respect to their contribution to the solution—including orthogonality constraints; (iii) whether or not individual sets of variables may be replaced by an orthogonal and unit normalized basis.Solutions are compared with respect to their optimality properties. For each solution the appropriate stationary equations are given. For one example it is shown how the determinantal equation of the stationary equations can be interpreted.     

The asymptotic distribution of commonality components

Commonality components have been defined as a method of partitioning squared multiple correlations. In this paper, the asymptotic joint distribution of all 2 ^k – 1 squared multiple correlations is derived. The asymptotic joint distribution of linear combinations of squared multiple correlations is obtained as a corollary. In particular, the asymptotic joint distribution of commonality components are derived as a special case. Simultaneous and nonsimultaneous asymptotic confidence intervals for commonality components can be obtained from this distribution.This work was supported in part by the Spencer Foundation and the National Science Foundation.The authors are grateful to Bryna Siegel-Gorlick for her help in obtaining the data used in Example 4.3, and to the referees for their comments and suggestions.     

Stimulus and reinforcer relativity in multiple schedules: Local and dimensional effects on sensitivity to reinforcement

Pigeons' responses in two successive components of multiple schedules were reinforced according to variable-interval schedules of reinforcement that varied over five different conditions. Within each session of all conditions, line orientations of 0°, 30°, or 45° in Component 1 alternated with orientations of 45°, 60°, or 90° in Component 2. Response rates were recorded in three successive subintervals of each component. Ratios were taken between the response rate in each Component 1 line orientation and the response rate in each Component 2 orientation. These ratios were found to be power functions of the corresponding ratios of obtained reinforcement rates. Sensitivity of response ratios to changes in reinforcer ratios, given by the value of the exponent of the power function, increased systematically with increasing disparity between the dimensional values of orientation stimuli. In addition, sensitivity decreased systematically over successive subintervals of components, that is, with increasing time since component alternation. Dimensional and local (subinterval) effects interacted in that sensitivity increased with stimulus disparity to a far greater extent in the first subinterval than later in components. The data could be described by a combination of rectangular hyperbolae which attributed the interaction between local and dimensional effects to limits set by local effects on the extent that stimulus differences could affect sensitivity.     

Openness relates to COVID-19 vaccination rates across 48 United States but politics trump personality

Does geographic variation in personality across the United States relate to COVID-19 vaccination rates? To answer this question, we combined multiple state-level datasets: (a) Big Five personality averages (i.e., extraversion, agreeableness, conscientiousness, neuroticism, and openness; Rentfrow et al., 2008), (b) COVID-19 full-vaccination rates (CDC, 2021a), (c) health-relevant demographic covariates (population density, per capita gross domestic product, and racial/ethnic data; Webster et al., 2021), and (d) political and religiosity data. Analyses showed openness as the strongest correlate of full-vaccination rates (r = 0.51). Controlling for other traits, demographic covariates, and spatial dependence, openness remained significantly related to full-vaccination rates (r_p = 0.55). Adding political and religiosity data to this model diminished openness effects for full-vaccination rates to non-significance (r_p = 0.26); however, extraversion emerged as a significant correlate of full-vaccination rates (r_p = 0.37). Although politics are paramount, we suspect that states with higher average openness scores are more conducive to novel thinking and behavior—dispositions that may be crucial in motivating people to take newly-developed vaccines based on new technologies to confront a novel coronavirus.     

The Unreal Child1

Unrepresented mental states lead to an impaired ability to feel emotions and trust in oneself, one’s history and in the world. The article explores the question of how representations of oneself and the relevant other, the mother, become possible in the course of therapy when dissociative processes previously made this impossible, and what role unconscious communication plays in the analytic realm. This question will be explored by examining the theories of André Green, Philip Bromberg, and Howard Levine.     

Effect sizes in ANCOVA and difference-in-differences designs

It is common practice in both randomized and quasi-experiments to adjust for baseline characteristics when estimating the average effect of an intervention. The inclusion of a pre-test, for example, can reduce both the standard error of this estimate and—in non-randomized designs—its bias. At the same time, it is also standard to report the effect of an intervention in standardized effect size units, thereby making it comparable to other interventions and studies. Curiously, the estimation of this effect size, including covariate adjustment, has received little attention. In this article, we provide a framework for defining effect sizes in designs with a pre-test (e.g., difference-in-differences and analysis of covariance) and propose estimators of those effect sizes. The estimators and approximations to their sampling distributions are evaluated using a simulation study and then demonstrated using an example from published data.