概化理论研究及应用前景

1972年,Cronbach和他的同事们提出概化理论之后,概化理论在行为与心理测量领域得到了广泛的应用,较之经典测量理论,它的优势逐渐地显露：(1)测量的多种误差来源可以在同一个分析中分别估计;(2)可以指导决策者选择最优测量方案;(3)提供可靠性系数：概化系数(G系数)和依存性指标(φ系数)用于不同的决策任务;(4)排除了严格平行测验的假设。概化理论以它的精确性和可藏性受到了信度测量领域研究者们的青睐,本文旨在对概化理论的基本框架、产生、发展及应用前景进行详细论述。     

青少年学习倦怠量表的多元概化分析

运用多元概化理论考察青少年学习倦怠量表（ASBI）的测量学属性。结果发现,ASBI各因子以及全量表的概化系数和可靠性指数均达到较好水平,既可用于常模参照测验,也可用于标准参照测验;各因子的分量比例还不尽完善,低成就感对全域总分的贡献略低于在全量表中的分值比重,对相对误差方差贡献最大的是低成就感,学业疏离对绝对误差方差的贡献最大;今后ASBI的修订工作,可考虑对学业疏离和低成就感因子条目质量的改进,同时适当增加身心耗竭因子的条目数以提高其测量信度。     

多元整合元分析法评介

在传统元分析中, 每个研究只能提取一个效果量, 以满足各效果量相互独立的条件。若出现单个研究多效果量的情况, 就需要采用多元元分析技术予以处理。多元元分析主要包括多元线性模型法和多元整合法, 其中, 多元整合法是应用最为广泛的一种方法, 可较为有效地解决多效果量非独立性问题, 还可借助传统元分析的固定效应模型和混合效应模型进行统计分析。但多元整合法在应用中还存在着统计软件的开发问题、同质性Q检验显著性的稳定问题、不同质混合效果量的同质性检验的可行性问题。     

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。     

Power and sample size calculations for multivariate linear models with random explanatory variables

This article considers the problem of power and sample size calculations for normal outcomes within the framework of multivariate linear models. The emphasis is placed on the practical situation that not only the values of response variables for each subject are just available after the observations are made, but also the levels of explanatory variables cannot be predetermined before data collection. Using analytic justification, it is shown that the proposed methods extend the existing approaches to accommodate the extra variability and arbitrary configurations of the explanatory variables. The major modification involves the noncentrality parameters associated with the F approximations to the transformations of Wilks likelihood ratio, Pillai trace and Hotelling-Lawley trace statistics. A treatment of multivariate analysis of covariance models is employed to demonstrate the distinct features of the proposed extension. Monte Carlo simulation studies are conducted to assess the accuracy using a child’s intellectual development model. The results update and expand upon current work in the literature.The author wishes to thank the associate editor and the referees for comments which improve the paper considerably. This research was partially supported by a grant from the Natural Science Council of Taiwan.     

A distance-based variety of nonlinear multivariate data analysis, including weights for objects and variables

In the distance approach to nonlinear multivariate data analysis the focus is on the optimal representation of the relationships between the objects in the analysis. In this paper two methods are presented for including weights in distance-based nonlinear multivariate data analysis. In the first method, weights are assigned to the objects while the second method is concerned with differential weighting of groups of variables. When each analysis variable defines a group the latter method becomes a variable weighting method. For objects the weights are assumed to be given; for groups of variables they may be given, or estimated. These weighting schemes can also be combined and have several important applications. For example, they make it possible to perform efficient analyses of large data sets, to use the distance-based variety of nonlinear multivariate data analysis as an addition to loglinear analysis of multiway contingency tables, and to do stability studies of the solutions by applying the bootstrap on the objects or the variables in the analysis. These and other applications are discussed, and an efficient algorithm is proposed to minimize the corresponding loss function.This study is funded by The Netherlands Organization for Scientific Research (NWO) by grant nr. 030-56403 for the PIONEER project Subject Oriented Multivariate Analysis to the third author.