Part and bipartial canonical correlation analysis

Extending the definitions of part and bipartial correlation to sets of variates, the notion of part and bipartial canonical correlation analysis are developed and illustrated.     

Spatial,non-spatial and hybrid models for scaling

In this paper, hierarchical and non-hierarchical tree structures are proposed as models of similarity data. Trees are viewed as intermediate between multidimensional scaling and simple clustering. Procedures are discussed for fitting both types of trees to data. The concept of multiple tree structures shows great promise for analyzing more complex data. Hybrid models in which multiple trees and other discrete structures are combined with continuous dimensions are discussed. Examples of the use of multiple tree structures and hybrid models are given. Extensions to the analysis of individual differences are suggested.1976 Psychometric Society Presidential Address.While people too numerous to list here have contributed ideas, inspiration, and other help, I particularly wish to acknowledge the contributions of Sandra Pruzansky, without whom this paper could not have been written. I would also like to acknowledge the past contributions of my long-time colleague Jih-Jie Chang, without whose help I probably would not have beenasked to write it.     

Factor simplicity index and transformations

A scale-invariant index of factorial simplicity is proposed as a summary statistic for principal components and factor analysis. The index ranges from zero to one, and attains its maximum when all variables are simple rather than factorially complex. A factor scale-free oblique transformation method is developed to maximize the index. In addition, a new orthogonal rotation procedure is developed. These factor transformation methods are implemented using rapidly convergent computer programs. Observed results indicate that the procedures produce meaningfully simple factor pattern solutions.This investigation was supported in part by a Research Scientist Development Award (K02-DA00017) and research grants (MH24149 and DA01070) from the U. S. Public Health Service. The assistance of Andrew L. Comrey, Henry F. Kaiser, Bonnie Barron, Marion Hee, and several anonymous reviewers is gratefully acknowledged.     

Tests of homogeneity of means and covariance matrices for multivariate incomplete data

Existing test statistics for assessing whether incomplete data represent a missing completely at random sample from a single population are based on a normal likelihood rationale and effectively test for homogeneity of means and covariances across missing data patterns. The likelihood approach cannot be implemented adequately if a pattern of missing data contains very few subjects. A generalized least squares rationale is used to develop parallel tests that are expected to be more stable in small samples. Three factors were varied for a simulation: number of variables, percent missing completely at random, and sample size. One thousand data sets were simulated for each condition. The generalized least squares test of homogeneity of means performed close to an ideal Type I error rate for most of the conditions. The generalized least squares test of homogeneity of covariance matrices and a combined test performed quite well also.Preliminary results on this research were presented at the 1999 Western Psychological Association convention, Irvine, CA, and in the UCLA Statistics Preprint No. 265 (http://www.stat.ucla.edu). The assistance of Ke-Hai Yuan and several anonymous reviewers is gratefully acknowledged.     

认知行为实验研究中最佳素材容量的选择与确定：多元概化理论应用

心理学实验首先必须保证结果的有效性和可靠性。实验素材容量是影响实验结果的关键因素之一。本文的目的是通过运用多元概化理论分析几个典型认知行为实验的素材容量对实验结果精度的影响, 从而探讨最佳的实验素材容量的确定问题。研究结果发现, 在IAT测验上, 相容与不相容任务的素材容量为50时最佳, 可靠性指数为0.92; 在图-词干扰范式中, 素材容量以48时为最佳, 可靠性指数为0.95; 对线索提示范式, 50%有效提示中, 有效与无效提示最佳素材容量为35, 可靠性指数是0.97。研究表明, 多元概化理论可以很好地用于确定认知行为实验中素材的最佳容量。     

大学生网络成瘾量表（修订版）的多元概化分析*

用多元概化理论考察大学生网络成瘾量表在大学生群体中应用的测量学性能。以随机测量模式的概化设计,针对1200名在校大学生进行问卷调查。结果显示双因子结构的相关程度在0.92以上,五因子结构的相关程度均在0.76~0.97间;整体量表的概化系数和可靠性指数均达到了0.94以上,而双因子结构各因子在0.90左右,五因子结构各因子均在0.74~0.85间。所以,整体量表及各因子在大学生群体中应用的信效度较高,可用作常模和标准参照测验;无论双因子还是五因子结构,CIAS-R各因子在分值比和项目数上,设计非常合理和完善。     

Heterogeneity Coefficients for Mahalanobis' D as a Multivariate Effect Size

The Mahalanobis distance D is the multivariate generalization of Cohen's d and can be used as a standardized effect size for multivariate differences between groups. An important issue in the interpretation of D is heterogeneity, that is, the extent to which contributions to the overall effect size are concentrated in a small subset of variables rather than evenly distributed across the whole set. Here I present two heterogeneity coefficients for D based on the Gini coefficient, a well-known index of inequality among values of a distribution. I discuss the properties and limitations of the two coefficients and illustrate their use by reanalyzing some published findings from studies of gender differences.     

高校教师教学水平评价多元概化理论权重效应分析

探讨高校教师教学水平评价多元概化理论合成信度的权重效应。使用"高校教师教学水平评价问卷",要求534名学生对16名教师进行评价,对收集到的数据作嵌套设计的多元概化理论分析。结果表明：（1）在高校教师教学水平评价中,多元概化理论合成信度估计权重、先验权重和效果权重产生不同效应;（2）结合高校教师教学水平评价,在充分分析三种权重对合成信度影响的基础上,提出了多元概化理论"合成信度三种权重效应分析模式图",能为正确使用多元概化理论权重提供科学参考。