Pseudo maximum likelihood estimation and a test for misspecification in mean and covariance structure models

Using the theory of pseudo maximum likelihood estimation the asymptotic covariance matrix of maximum likelihood estimates for mean and covariance structure models is given for the case where the variables are not multivariate normal. This asymptotic covariance matrix is consistently estimated without the computation of the empirical fourth order moment matrix. Using quasi-maximum likelihood theory a Hausman misspecification test is developed. This test is sensitive to misspecification caused by errors that are correlated with the independent variables. This misspecification cannot be detected by the test statistics currently used in covariance structure analysis.For helpful comments on a previous draft of the paper we are indebted to Kenneth A. Bollen, Ulrich L. Küsters, Michael E. Sobel and the anonymous reviewers of Psychometrika. For partial research support, the first author wishes to thank the Department of Sociology at the University of Arizona, where he was a visiting professor during the fall semester 1987.     

Fitting the off-diagonal dedicom model in the least-squares sense by a generalization of the harman and jones minres procedure of factor analysis

Harshman's DEDICOM model providesa framework for analyzing square but asymmetric materices of directional relationships amongn objects or persons in terms of a small number of components. One version of DEDICOM ignores the diagonal entries of the matrices. A straight-forward computational solution for this model is offered in the present paper. The solution can be interpreted as a generalized Minres procedure suitable for handing asymmetric matrices.     

An investigation of the normative and factor analytic composition of six questionnaires used for subject selection

Six self-report instruments which have been widely used for subject screening were administered to large samples of males and female college students. Normative information and bivariate correlations between scales were derived. Principal components analysis was applied to the individual scales to determine substructures. Finally, factor scores from the scales were examined in terms of second-order factors from an additional principal components analysis. The results are discussed in terms of comparisons with the results of previously published reports and implications for the discriminant validity of the scales and factor scores. The use of factor scores for more homogeneous subject selection is suggested.We wish to thank Arnold Holzman, Cynthia Volinsky, Ellen Tuckner, and Patricia Tolchin for their assistance in administering the questionnaires. Appreciation is also extended to Paul Malloy for his aid and suggestions concerning data analysis and to an anonymous reviewer for insightful comments which improved the quality of the final report. This study was conducted while the first author was at the State University of New York at Binghamton.     

Factor analysis by instrumental variables methods

Three alternative estimation procedures for factor analysis based on the instrumental variables method are presented. These procedures are justified by the method of least squares. Formulas for asymptotic standard errors of factor loadings are derived. The procedures are empirically compared to the method of maximum likelihood. The conclusion, based on the data used in this study, is that two of the procedures seem to work well.     

The technical drift of applied behavior analysis

Four dimensions (applied, analytic, general, conceptual) were selected from Baer, Wolf, and Risley's (1968) seminal article on the nature of applied behavior analysis and were monitored throughout the first 10 volumes of the Journal of Applied Behavior Analysis. Each of the experimental articles in Volumes 1 through 6 and the first half of Volumes 7 through 10 was rated on each of these dimensions. The trends showed that applied behavior analysis is becoming a more purely technical effort, with less interest in conceptual questions. We are using simpler experimental designs and are conducting fewer analogue studies. Although concern for maintenance is increasing, other forms of generality are being measured or analyzed less often. These trends are discussed in terms of a technical drift in applied behavior analysis.     

Statistics as psychometrics

In this paper, modern statistics is considered as a branch of psychometrics and the question of how the central problems of statistics can be resolved using psychometric methods is investigated. Theories and methods developed in the fields of test theory, scaling, and factor analysis are related to the principle problems of modern statistical theory and method. Topics surveyed include assessment of probabilities, assessment of utilities, assessment of exchangeability, preposterior analysis, adversary analysis, multiple comparisons, the selection of predictor variables, and full-rank ANOVA. Reference is made to some literature from the field of cognitive psychology to indicate some of the difficulties encountered in probability and utility assessment. Some methods for resolving these difficulties using the Computer-Assisted Data Analysis (CADA) Monitor are described, as is some recent experimental work on utility assessment.1980 Psychometric Society presidential address.I am indebted to Paul Slovic and David Libby for valuable consultation on the issues discussed in this paper and to Nancy Turner and Laura Novick for assistance in preparation.Research reported herein was supported under contract number N00014-77-C-0428 from the Office of Naval Research to The University of Iowa, Melvin R. Novick, principal investigator. Opinions expressed herein reflect those of the author and not those of sponsoring agencies.     

An individual differences additive model: An alterating least squares method with optimal scaling features

An individual differences additive model is discussed which represents individual differences in additivity by differential weighting of additive factors. A procedure for estimating the model parameters for various data measurement characteristics is developed. The procedure is evaluated using both Monte Carlo and real data. The method is found to be very useful in describing certain types of developmental change in cognitive structure, as well as being numerically robust and efficient.The work reported here was partly supported by Grant A6394 to the first author by the Natural Sciences and Engineering Research Council of Canada.     

A note on identification in the oblique and orthogonal factor analysis models

Conditions for removing the indeterminancy due to rotation are given for both the oblique and orthogonal factor analysis models. The conditions indicate why published counterexamples to conditions discussed by Jöreskog are not identifiable.The author would like to thank Gordon Bechtel and the reviewers for their comments and suggestions.     

Power of the likelihood ratio test in covariance structure analysis

A procedure for computing the power of the likelihood ratio test used in the context of covariance structure analysis is derived. The procedure uses statistics associated with the standard output of the computer programs commonly used and assumes that a specific alternative value of the parameter vector is specified. Using the noncentral Chi-square distribution, the power of the test is approximated by the asymptotic one for a sequence of local alternatives. The procedure is illustrated by an example. A Monte Carlo experiment also shows how good the approximation is for a specific case.This research was made possible by a grant from the Dutch Organization for Advancement of Pure Research (ZWO). The authors also like to acknowledge the helpful comments and suggestions from the editor and anonymous reviewers.     

儿童拒绝敏感性与自我知觉的关系:一项交叉滞后研究

采用儿童拒绝敏感性问卷与儿童自我知觉量表对596名中小学儿童进行了为期一年的追踪调查,通过交叉滞后回归分析考察儿童拒绝敏感性与多种自我知觉间的预测关系。结果发现：（1）自我知觉的年级差异显著,初中儿童的自我知觉水平较低;（2）在两次测量中,拒绝敏感性与多种自我知觉均呈显著的负相关;（3）控制性别、年级以及自身稳定性之后,第一年的多种自我知觉均可以负向预测第二年的拒绝敏感性,而第一年的拒绝敏感性仅可以负向预测第二年的体貌及一般自我知觉;（4）年级调节了拒绝敏感性与一般和行为自我知觉间的纵向关系,并在小学与初中儿童中表现出不同的模式。研究证实自我知觉是儿童拒绝敏感性形成的重要原因之一,为儿童社会认知及心理健康的相关研究提供新的实证依据。