Oblique factors and components with independent clusters

Relationships between the results of factor analysis and component analysis are derived when oblique factors have independent clusters with equal variances of unique factors. The factor loadings are analytically shown to be smaller than the corresponding component loadings while the factor correlations are shown to be greater than the corresponding component correlations. The condition for the inequality of the factor/component contributions is derived in the case with different variances for unique factors. Further, the asymptotic standard errors of parameter estimates are obtained for a simplified model with the assumption of multivariate normality, which shows that the component loading estimate is more stable than the corresponding factor loading estimate.     

Homogeneity analysis withk sets of variables: An alternating least squares method with optimal scaling features

Homogeneity analysis, or multiple correspondence analysis, is usually applied tok separate variables. In this paper we apply it to sets of variables by using sums within sets. The resulting technique is called OVERALS. It uses the notion of optimal scaling, with transformations that can be multiple or single. The single transformations consist of three types: nominal, ordinal, and numerical. The corresponding OVERALS computer program minimizes a least squares loss function by using an alternating least squares algorithm. Many existing linear and nonlinear multivariate analysis techniques are shown to be special cases of OVERALS. An application to data from an epidemiological survey is presented.This research was partly supported by SWOV (Institute for Road Safety Research) in Leidschendam, The Netherlands.     

Inferential procedures for correlation coefficients corrected for attenuation

A model and computational procedure based on classical test score theory are presented for determination of a correlation coefficient corrected for attenuation due to unreliability. Next, variance-covariance expressions for the sample estimates defined earlier are derived, based on application of the delta method. Results of a Monte Carlo study are presented in which the adequacy of the derived expressions was assessed for a large number of data forms and potential hypotheses encountered in the behavioral sciences. It is shown that, based on the proposed procedures, confidence intervals for single coefficients are reasonably precise. Two-sample hypothesis tests, for both independent and dependent samples, are also accurate. However, for hypothesis tests involving a larger number of coefficients than two—both independent and dependent—the proposed procedures require largens for adequate precision. Results of a preliminary power analysis reveal no serious loss in efficiency resulting from correction for attenuation. Implications for practice are discussed.Support for the research reported in this article was provided by the Natural Sciences and Engineering Research Council of Canada. The authors acknowledge with thanks the constructive comments of the editor and three anonymous reviewers.     

Component analysis with different sets of constraints on different dimensions

Many of the classical multivariate data analysis and multidimensional scaling techniques call for approximations by lower dimensional configurations. A model is proposed, in which different sets of linear constraints are imposed on different dimensions in component analysis and classical multidimensional scaling frameworks. A simple, efficient, and monotonically convergent algorithm is presented for fitting the model to the data by least squares. The basic algorithm is extended to cover across-dimension constraints imposed in addition to the dimensionwise constraints, and to the case of a symmetric data matrix. Examples are given to demonstrate the use of the method.The work reported in this paper has been supported by the Natural Sciences and Engineering Research Council of Canada, grant number A6394, and by the McGill-IBM Cooperative Grant, both granted to the first author. The research of H. A. L. Kiers has been made possible by a fellowship of the Royal Netherlands Academy of Arts and Sciences. We thank Michael Hunter for his helpful comments on earlier drafts of this paper.     

小学儿童“互反可逆性“发展的研究

本文运用成分分析法研究经典的皮亚杰有关儿童“互反可逆性”的理论。     

Behavioral contrast for key pecking as a function of component duration when only one component varies

Pigeons pecked keys for food reinforcers delivered by multiple variable-interval 2-min variable-interval 2-min schedules. Positive behavioral contrast was created by changing one component to extinction; negative contrast was achieved by changing one component to a variable-interval 15-s schedule. The duration of each component was varied independently of the other from 5 to 960 s. The size of positive contrast was greatest when the extinction component was 30 or 60 s long. It did not change significantly with changes in the duration of the variable-interval 2-min component. The absolute size of negative contrast decreased with increases in the duration of the variable-interval 2-min component. It did not change significantly with changes in the duration of the variable-interval 15-s component. These results show that the size of contrast is determined primarily by the duration of the component that provides the less favorable conditions of reinforcement. These results are not predicted by current theories.     

On quantifying different types of categorical data

In quantifying categorical data, constraints play an important role in characterizing the outcome. In the Guttman-type quantification of contingency tables and multiple-choice data (incidence data), the trivial solution due to the marginal constraints is typically removed before quantification; this removal, however, has the effect of distorting the shape of the total space. Awareness of this is important for the interpretation of the quantified outcome. The present study provides some relevant formulas for those cases that are affected by the trivial solution and those cases that are not. The characterization of the total space used by the Guttman-type quantification and pertinent discussion are presented.This study was supported by a grant from The Natural Sciences and Engineering Research Council of Canada to S. Nishisato.     

FUNCTIONAL ANALYSIS OF A PROGRAM FOR TRAINING HIGH AND LOW PREFERENCE PEERS TO MODIFY DISRUPTIVE CLASSROOM BEHAVIOR1

Although considerable evidence has accumulated regarding the effectivenes of peers as behavior managers, little attention has been directed toward the analysis of components of training programs. Also, it is not known whether high and low preference peers have different reinforcing effects. In this study, a two-phase peer training program was introduced to five high preference and five low preference peer managers. The Managers were assigned one to each of 10 grade-four elementary pupils with high rates of disruptive behavior. Peer Training I was composed of instructions to monitor and reinforce target subject behavior on a time-sample schedule, to record this reinforcement, and to ignore inappropriate behavior in target subjects. Peer Training II introduced, in addition, backup reinforcement for the monitoring behavior of managers. Measures were taken of target subject behavior, antecedent and consequent attention from managers and other peers, monitoring behavior of managers, teacher behavior, and preference ratings of peer managers by target subjects. Significant improvements in target subject behavior occurred following Peer Training I and further improvements occurred following Peer Training II. These changes were paralleled by decreases in the frequency of antecedent and consequent manager attention to problem behaviors, and improvements in the number and accuracy of monitoring checks. Recorded reductions in teacher attention to individual children and to target subjects were not statistically significant. Posttest preference scores improved significantly over pretest scores for low preference managers, while remaining high for high preference managers.     

职务评价的方差分析法在制定职务等级中的应用

职务评价的方差分析法的技术核心是通过方差分析确定职务可比价值各成分的权重。此方法的有效性和实用性已在协助国有企业内部分配改革的制定工资标准中得到验证,并显示其普遍性的方法论意义：职务评价值是职务价值的线性映射：凡涉及职务价值差异的问题都可用此方法解决。本研究应用方差分析法为国有金融系统行员制改革中制定统一的职务等级标准提供技术支持和科学依据。评价程序中,职务分析、职务分类、计算职务评价值等前三个步骤与制定工资标准的作法相同。然后,推算各类职务评价值的变异范围（以９５％置信区间代表）,再将各类职务按管理层次合并为大类。最后,寻求一个合理划分各管理层次大类变异范围的约数,划分整个评价值变异范围。对某银行系统４５２种职务的８４１个样本评价的结果,所确定的职务等级数、各职务大类的等级跨度、位次,都符合该银行系统的人事管理经验及改革设想     

Positive and negative contrast as a function of component duration for key pecking and treadle pressing

Pigeons responded on several multiple schedules for food reinforcers. The duration of the components varied from four seconds to 16 minutes. The absolute size of positive (Experiment 1) and negative (Experiment 2) behavioral contrast varied inversely with component duration when key pecks produced the reinforcers. The absolute size of negative contrast varied directly with component duration, when treadle presses produced the reinforcers (Experiment 3). These results conform to theories that suggest that positive and negative contrast are symmetrical when pigeons peck keys. They also conform to theories that suggest that the same principles do not govern contrast when pigeons peck keys as when they press treadles. Finally, the results support the measurement of behavioral contrast by the differences between baseline rates of responding and the rates emitted when contrast is present.