自我同情对大学生拖延行为的影响：经验回避和羞耻的链式中介作用

为探讨自我同情对拖延的影响及其作用机制,采用自我同情量表、大学生拖延量表、大学生羞耻量表和接纳与行动量表对795名大学生进行调查,结果发现：(1)自我同情对大学生拖延行为具有负向预测作用;(2)羞耻在自我同情与大学生拖延行为的负向效应中起到中介作用;(3)经验回避、羞耻在自我同情和大学生拖延行为之间起链式中介作用。     

Overcoming Procrastination: The Effect of Implementation Intentions1

A hypothesized solution for procrastination is the formation of an implementation intention ( Van Eerde, 2000 ). University students (N = 152) were assessed using the Aitken Procrastination Inventory ( Aitken, 1982 ) and were asked to report to an experiment. Half of the participants formed implementation intentions to attend. An odds ratio indicated that participants who formed implementation intentions were nearly 8 times more likely to keep their appointments than those who did not. Low procrastinators reported more often for the experiment than did high procrastinators (Low = 49.4%; High = 30.1%), χ²(1, N = 152) = 5.84, p < .016. The interaction between implementation intentions and procrastination was not significant, χ²(1, N = 152) = 0.28, p < .60.     

Use of systematic desensitization in the treatment of airplane phobic patients

There have been several studies of the phenomenon of airplane phobia mostly dealing with professional aviators and aircrews (Goorney, 1960; Aitken, 1970). There has also been a report of the use of penthranization in the in vivo desensitization of an airplane phobic patient (Mildman, 1969). Recently one case of successful in vivo desensitization using flight simulators and flight instruction has been reported (Bernstein and Beaty, 1971).This paper describes the application of Wolpe's (Wolpe, 1969) technique of systematic desensitization for persons with an airplane phobia.     

Generating correlation matrices with model error for simulation studies in factor analysis: A combination of the Tucker-Koopman-Linn model and Wijsman’s algorithm

Most simulation studies in factor analysis follow a process of constructing population correlation matrices from the common-factor model and generating sample correlation matrices from the population matrices. In the common-factor model, the population correlation matrix is perfectly fit by the model’s containing common and unique factors. However, since no mathematical model accounts exactly for the real-world phenomena that it is intended to represent, the Tucker-Koopman-Linn model (1969) is more realistic for generating correlation matrices than the conventional common-factor model because the former incorporates model error. In this paper, a procedure for generating population and sample correlation matrices with model error by combining the Tucker-Koopman-Linn model and Wijsman’s algorithm (1959) is presented. The SAS/ IML program for generating correlation matrices is described, and an example is also provided.     

Simultaneous factor analysis of several Gramian matrices

Given several Gramian matrices, a least-square fit to all the matrices by one factor matrix, with a predetermined number of factors, is shown to be the principal axes solution of the average of the matrices.     

Logical matrices and non-structural consequence operators

In the present paper, we study some properties of matrices for non-structural consequence operators. These matrices were introduced in a former work (see [3]). In sections 1. and 2., general definitions and theorems are recalled; in section 3. a correspondence is studied, among our matrices and Wójcicki's ones for structural operators. In section 4. a theorem is given about operators, induced by submatrices or epimorphic images, or quotient matrices of a given one. Such matrices are used to characterize lattices of non-structural consequence operators, by constructing lattices, antiisomorphic to them (see section 5.). In the last section, a sufficient condition is given for a non-structural operator to be finite.     

Some clarifications of the CANDECOMP algorithm applied to INDSCAL

Carroll and Chang have claimed that CANDECOMP applied to symmetric matrices yields equivalent coordinate matrices, as needed for INDSCAL. Although this claim has appeared to be valid for all practical purposes, it has gone without a rigorous mathematical footing. The purpose of the present paper is to clarify CANDECOMP in this respect. It is shown that equivalent coordinate matrices are not granted at global minima when the symmetric matrices are not Gramian, or when these matrices are Gramian but the solution not globally optimal.Part of this research has been supported by The Netherlands Organization for Scientific Research (NWO), PSYCHON-grant (560-267-011).     

Simultaneous analysis of coupled data matrices subject to different amounts of noise

In many areas of science, research questions imply the analysis of a set of coupled data blocks, with, for instance, each block being an experimental unit by variable matrix, and the variables being the same in all matrices. To obtain an overall picture of the mechanisms that play a role in the different data matrices, the information in these matrices needs to be integrated. This may be achieved by applying a data‐analytic strategy in which a global model is fitted to all data matrices simultaneously, as in some forms of simultaneous component analysis (SCA). Since such a strategy implies that all data entries, regardless the matrix they belong to, contribute equally to the analysis, it may obfuscate the overall picture of the mechanisms underlying the data when the different data matrices are subject to different amounts of noise. One way out is to downweight entries from noisy data matrices in favour of entries from less noisy matrices. Information regarding the amount of noise that is present in each matrix, however, is, in most cases, not available. To deal with these problems, in this paper a novel maximum‐likelihood‐based simultaneous component analysis method, referred to as MxLSCA, is proposed. Being a stochastic extension of SCA, in MxLSCA the amount of noise in each data matrix is estimated and entries from noisy data matrices are downweighted. Both in an extensive simulation study and in an application to data stemming from cross‐cultural emotion psychology, it is shown that the novel MxLSCA strategy outperforms the SCA strategy with respect to disclosing the mechanisms underlying the coupled data.     

Orthogonal procrustes rotation for two or more matrices

Necessary and sufficient conditions for rotating matrices to maximal agreement in the least-squares sense are discussed. A theorem by Fischer and Roppert, which solves the case of two matrices, is given a more straightforward proof. A sufficient condition for a best least-squares fit for more than two matrices is formulated and shown to be not necessary. In addition, necessary conditions suggested by Kristof and Wingersky are shown to be not sufficient. A rotation procedure that is an alternative to the one by Kristof and Wingersky is presented. Upper bounds are derived for determining the extent to which the procedure falls short of attaining the best least-squares fit. The problem of scaling matrices to maximal agreement is discussed. Modifications of Gower's method of generalized Procrustes analysis are suggested.     

Identifying a Reordering of Rows and Columns for Multiple Proximity Matrices Using Multiobjective Programming

This paper is concerned with a problem where K (n×n) proximity matrices are available for a set of n objects. The goal is to identify a single permutation of the n objects that provides an adequate structural fit, as measured by an appropriate index, for each of the K matrices. A multiobjective programming approach for this problem, which seeks to optimize a weighted function of the K indices, is proposed, and illustrative examples are provided using a set of proximity matrices from the psychological literature. These examples show that, by solving the multiobjective programming model under different weighting schemes, the quantitative analyst can uncover information about the relationships among the matrices and often identify one or more permutations that provide good to excellent index values for all matrices.     

Some clarifications of the TUCKALS2 algorithm applied to the IDIOSCAL problem

Kroonenberg and de Leeuw have suggested fitting the IDIOSCAL model by the TUCKALS2 algorithm for three-way components analysis. In theory, this is problematic because TUCKALS2 produces two possibly different coordinate matrices, that are useless for IDIOSCAL unless they are equal. Kroonenberg has claimed that, when IDIOSCAL is fitted by TUCKALS2, the resulting coordinate matrices will be identical. In the present paper, this claim is proven valid when the data matrices are semidefinite. However, counterexamples for indefinite matrices are also constructed, by examining the global minimum in the case where the data matrices have the same eigenvectors. Similar counterexamples have been considered by ten Berge and Kiers in the related context of CANDECOMP/PARAFAC to fit the INDSCAL model.     

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results than those based on standard asymptotics. But the procedure needs a matrix to play the role of the population covariance matrix. The closer the matrix is to the true population covariance matrix, the more valid the bootstrap inference is. The current paper proposes a class of covariance matrices by combining theory and data. Thus, a proper matrix from this class is closer to the true population covariance matrix than those constructed by any existing methods. Each of the covariance matrices is easy to generate and also satisfies several desired properties. An example with nine cognitive variables and a confirmatory factor model illustrates the details for creating population covariance matrices with different misspecifications. When evaluating the substantive model, bootstrap or simulation procedures based on these matrices will lead to more accurate conclusion than that based on artificial covariance matrices.     

Fungible Correlation Matrices: A Method for Generating Nonsingular,Singular, and Improper Correlation Matrices for Monte Carlo Research

For a fixed set of standardized regression coefficients and a fixed coefficient of determination (R-squared), an infinite number of predictor correlation matrices will satisfy the implied quadratic form. I call such matrices fungible correlation matrices. In this article, I describe an algorithm for generating positive definite (PD), positive semidefinite (PSD), or indefinite (ID) fungible correlation matrices that have a random or fixed smallest eigenvalue. The underlying equations of this algorithm are reviewed from both algebraic and geometric perspectives. Two simulation studies illustrate that fungible correlation matrices can be profitably used in Monte Carlo research. The first study uses PD fungible correlation matrices to compare penalized regression algorithms. The second study uses ID fungible correlation matrices to compare matrix-smoothing algorithms. R code for generating fungible correlation matrices is presented in the supplemental materials.     

Three-Way Tucker2 Component Analysis Solutions of Stimuli × Responses × Individuals Data with Simple Structure and the Fewest Core Differences

Multivariate stimulus-response designs can be described by a three-way array of stimuli by responses by individuals. Its underlying structure can be represented by a network based on the Tucker2 component model in which stimulus components are connected with response components by means of the links that differ between individuals. For each individual such links are represented in a slice of the extended core array. For a proper understanding of these links, it is desirable that [1] the individual core slices as well as the component matrices have simple structures and [2] the differences of core slices between individuals are as few as possible. For attaining [1] and [2] we propose a method in which both the component matrices and the core slices of a Tucker2 solution are transformed simultaneously in order that the component matrices match simple target matrices and the core slices are summarized by a simple target slice. The proposed method is evaluated in a simulation study and illustrated with a three-way data array of semantic differential ratings.     

Oscillations: Zen & Psychoanalytic Versions

The author provides a personal and experiential account of Zen Buddhism and psychoanalysis. The notion of oscillations serves as an organizing structure. Drawing from the British psychoanalyst Wilfred Bion and the American Zen teacher Robert Aitken, the notion of suffering, meaning here to permit, is considered as the central motivating force and organizing principle for both disciplines. As a critique of traditional psychoanalytic writing an experiment in dialogue is offered that draws from a variety of writing styles including prose, poetry, free-association, stream of consciousness, traditional teaching stories and case material to discuss various experiential states such as linearity, circularity, resistance, ambivalence, passion, rage and the potential for a mutually supportive dynamic between Zen and psychoanalysis.     

The retention of imagined and seen spatial matrices

The nature of imagery and its relationship to perception were probed by having students recall the contents of 4 × 4 spatial matrices after they constructed the matrices through processes of imagery, seeing, or verbal coding. In the imagery and verbal conditions, students listened to auditory messages which described the matrices. Students in the imagery condition were instructed to imagine the matrix being described; students in the verbal condition received no imagery instructions; and students in the seeing condition saw the matrix for an inspection period prior to recall. Recall was best when students saw the matrices, intermediate in the imagery situation, and poorest in the verbal situation. Recall was best for the corner cells of seen and imagined matrices, indicating the spatial nature of these processes; no such effect was found for verbal processing. Forgetting over a 10-sec interval occurred only for verbal processing. Implications for understanding the nature of visual imagery and perception are discussed.     

On the concordance among empirical confusion matrices for visual and tactual letter recognition

In this article, we examine the concordance among 19 empirical confusion matrices for visual and tactual recognition of capital letters of the alphabet. As a measure of concordance, we employed an index based on within-stimulus triads of letters. Unlike correlation measures of agreement that are based on a one-to-one matching of matrix elements, the selected index directly captures the internal structures of the confusion matrices prior to the comparison. Permutation tests revealed statistically significant concordance among 166 of 171 pairs of matrices in the study. Concordance of confusion structure among tactual matrices tended to be somewhat stronger than concordance among the visual matrices.     

Orthogonal rotations to maximal agreement for two or more matrices of different column orders

Methods for orthogonal Procrustes rotation and orthogonal rotation to a maximal sum of inner products are examined for the case when the matrices involved have different numbers of columns. An inner product solution offered by Cliff is generalized to the case of more than two matrices. A nonrandom start for a Procrustes solution suggested by Green and Gower is shown to give better results than a random start. The Green-Gower Procrustes solution (with nonrandom start) is generalized to the case of more than two matrices. Simulation studies indicate that both the generalized inner product solution and the generalized Procrustes solution tend to attain their global optima within acceptable computation times. A simple procedure is offered for approximating simple structure for the rotated matrices without affecting either the Procrustes or the inner product criterion.The authors are obliged to Charles Lewis for helpful comments on a previous draft of this paper and to Frank Brokken for preparing a computer program that was used in this study.     

Evaluation of factor analytic research procedures by means of simulated correlation matrices

In order to study the effectiveness of factor analytic methods, a procedure was developed for computing simulated correlation matrices which are more similar to real data correlation matrices than are those matrices computed from the factor analysis structural model. In the present investigation, three methods of factor extraction were studied as applied to 54 simulated correlation matrices which varied in proportion of variance derived from a major factor domain, number of factors in the major domain, and closeness of the simulation procedure to the factor analysis structural model. While the factor extraction methods differed little from one another in quality of results for matrices more dissimilar to the factor analytic model, major differences in quality of results were associated with fewer factors in the major domain, higher proportion of variance from the major domain, and closeness of the simulation procedure to the factor analysis structural model.The research was jointly supported by the University of Illinois and the office of Naval Research under contracts Nonr 1834(39) and N00014-67-A-0305-0003.