Finite mixtures in confirmatory factor-analysis models

In this paper, various types of finite mixtures of confirmatory factor-analysis models are proposed for handling data heterogeneity. Under the proposed mixture approach, observations are assumed to be drawn from mixtures of distinct confirmatory factor-analysis models. But each observation does not need to be identified to a particular model prior to model fitting. Several classes of mixture models are proposed. These models differ by their unique representations of data heterogeneity. Three different sampling schemes for these mixture models are distinguished. A mixed type of the these three sampling schemes is considered throughout this article. The proposed mixture approach reduces to regular multiple-group confirmatory factor-analysis under a restrictive sampling scheme, in which the structural equation model for each observation is assumed to be known. By assuming a mixture of multivariate normals for the data, maximum likelihood estimation using the EM (Expectation-Maximization) algorithm and the AS (Approximate-Scoring) method are developed, respectively. Some mixture models were fitted to a real data set for illustrating the application of the theory. Although the EM algorithm and the AS method gave similar sets of parameter estimates, the AS method was found computationally more efficient than the EM algorithm. Some comments on applying the mixture approach to structural equation modeling are made.Note: This paper is one of the Psychometric Society's 1995 Dissertation Award papers.—EditorThis article is based on the dissertation of the author. The author would like to thank Peter Bentler, who was the dissertation chair, for guidance and encouragement of this work. Eric Holman, Robert Jennrich, Bengt Muthén, and Thomas Wickens, who served as the committee members for the dissertation, had been very supportive and helpful. Michael Browne is appreciated for discussing some important points about the use of the approximate information in the dissertation. Thanks also go to an anonymous associate editor, whose comments were very useful for the revision of an earlier version of this article.     

初级指挥官人格类型与PM领导行为类型有效性的关系

通过对陆军初级指挥官人格心理类型与领导行为有效性的心理学研究 ,探讨个人的领导潜能、人格心理类型对领导行为有效性的影响。研究发现 :1方差分析表明 ,具有不同领导行为的指挥官在人格维度上存在明显的统计学差异。提示了指挥官的领导行为取向与人格因素有密切的关系。2卡方分析结果也表明 ,PM型行为的军官在其人格特征上具有“ESTJ”组合特征 ,而 pm型的军官则正好相反 ;另外 ,pm型的军官在成就动机上获得高分的人数最少。3系统聚类分析显示 ,“成就动机”、“感觉”和“判断”三种人格因素与军事指挥官完成整体领导职能有着更为密切的关系。     

计量测验效度的一种方法——测量目标结构空间的实证性因素分析

该文提出了计量测验效度的一种统一方法。首先,根据测量目标由测验编制者提出一组可能的线性拟合模型,拟合模型的集合称作“测量目标结构空间”。然后运用实证性因素分析对各个假设的拟合模型进行多方面的拟合良好性检验。若其中的某一假设模型能最优地拟合预测资料,则该模型中的公共因素的方差总贡献与观测变量的总方差的比就可定义为该测验的效度     

教育因素对认知功能年老化过程的影响

应用言语－操作和人机对话两种方式共１２项作业,完成年龄和教育因素对认知功能影响的研究,被试共７３４人,４６－７５岁．结果表明：（１）年龄和教育因素对认知成绩的影响因作业而异,分别为年龄影响明显（图象自由回忆和无意义图形再认）、教育因素影响明显（卡片分类、图象分类和矩阵填图）、年龄和教育因素影响均明显（符号数字、词对联想学习、听觉和视觉数字广度）或均不明显（心算）．（２）四项非词语性作业速度明显受年龄和教育因素的双重影响．     

Application of the bootstrap methods in factor analysis

A Monte Carlo experiment is conducted to investigate the performance of the bootstrap methods in normal theory maximum likelihood factor analysis both when the distributional assumption is satisfied and unsatisfied. The parameters and their functions of interest include unrotated loadings, analytically rotated loadings, and unique variances. The results reveal that (a) bootstrap bias estimation performs sometimes poorly for factor loadings and nonstandardized unique variances; (b) bootstrap variance estimation performs well even when the distributional assumption is violated; (c) bootstrap confidence intervals based on the Studentized statistics are recommended; (d) if structural hypothesis about the population covariance matrix is taken into account then the bootstrap distribution of the normal theory likelihood ratio test statistic is close to the corresponding sampling distribution with slightly heavier right tail.This study was carried out in part under the ISM cooperative research program (91-ISM · CRP-85, 92-ISM · CRP-102). The authors would like to thank the editor and three reviewers for their helpful comments and suggestions which improved the quality of this paper considerably.     

An integrated mindfulness and acceptance-based program for young elite female basketball players: Exploratory study of how it works and for whom it works best

In order to better understand how an integrated mindfulness and acceptance-based intervention works and for whom it works best, study objectives were to examine (i) the trajectories of mindfulness skills and performance-related outcomes during the intervention, and athletes’ perceptions of the impact of the intervention; and (ii) the potential moderating effects of personality characteristics on changes in targeted variables associated with the intervention. The sample consisted of 40 young elite female basketball players (M = 16.33, SD = 0.75 years) from three incoming groups at the French Federal Basketball Center over a 3-year period. All players participated in a 15-week Mindfulness BasketBall Integrated program. They completed online questionnaires measuring personality traits 10 months before the intervention, as well as pre-, mid- and post-intervention measurements of mindfulness skills, intensity and directional interpretation of stress, and performance satisfaction. They also participated in semi-structured social validation interviews conducted one month after the intervention. The results of the multilevel growth curve (MGCA) and thematic analyses revealed how the mindfulness skills and performance-related outcomes evolved over the course of the intervention and how these changes were perceived by the athletes. Specifically, the MGCA showed significant linear increases in acceptance, positive stress direction, and performance satisfaction. The complementary social validation data indicated perceived improvements in mindfulness skills and performance. The MGCA also showed that baseline personality traits moderated the effects of the program on acceptance and experience of stress. These findings may be used to inform the design of more effective integrated mindfulness and acceptance-based interventions.     

Factor structure of the basic personality inventory with incarcerated offenders

The item and scale factor structure of the Basic Personality Inventory (BPI) was examined in a sample of 486 offenders incarcerated for violent and sexual crimes. Separate principal-component analyses of the items for each of the 11 clinical scales, critical item scale, and social desirability scale indicated a one-dimensional factor solution for all scales except Depression and Persecutory Ideation. The Depression scale's two factors were Hopelessness and Depressive Affect and the Persecutory Ideation scale's two factors were General Paranoia and Perception of External Control. Although the factors for these two scales may assist in interpretation, the correlations between the factors and the total score of their respective scale were high. Confirmatory factor analysis of the 220 items from the 11 clinical scales supported the factorial logic of the scoring key. Analysis of the 11 clinical scales resulted in two factors: General Psychopathology/Adjustment and Antisocial Orientation. The results suggest that all but two scales can be viewed as unidimensional thereby allowing for a straightforward clinical interpretation. These analyses support the internal structure of the BPI and lend credence to external validity work with forensic populations.     

初中生友谊质量与孤独感的变化轨迹:一项多元潜变量增长模型分析

为考察初中生友谊质量与孤独感的变化轨迹及两者间的关系,采用关系网络问卷、孤独感量表对西安市的875名初中生（M=12.73±0.18;男生494名）进行三年追踪测量,使用多元潜变量增长模型进行分析。结果发现：（1）初中三年,中学生的友谊质量逐渐提高,孤独感水平逐渐下降;（2）初一时的友谊质量和孤独感水平存在显著的个体差异,初中三年期间二者的发展速度也存在个体差异;（3）初一时的友谊质量可显著预测初一时的孤独感水平（β=-1.77,p<0.001）及随后的发展速度（β=0.20,p<0.05）;（4）男生的孤独感水平初始值高于女生,友谊质量的初始值则低于女生;高SES个体孤独感的起始值高于低SES个体。本研究结果揭示了初中三年期间个体友谊质量与孤独感的动态变化关系及友谊质量对孤独感的预测作用,对改善青少年孤独感的现状有一定的现实指导意义。     

The effect of additional variables on factor indeterminacy in models with a single common factor

Determinate solutions for the indeterminate common factor ofp variables satisfying the single common factor model are not unique. Therefore an infinite sequence of additional variables that conform jointly with the originalp variables to the original single common factor model does not determine a unique solution for the indeterminate factor of thep variables (although the solution is unique for the factor of the infinite sequence). Other infinite sequences may be found to determine different solutions for the factor of the originalp variables. The paper discusses a number of theorems about the effects of additional variables on factor indeterminacy in a model with a single common factor and draws conclusions from them for factor theory in general.     

An alternative to the methodology for analysis of covariance

A general statistical model for simultaneous analysis of data from several groups is described. The model is primarily designed to be used for the analysis of covariance. The model can handle any number of covariates and criterion variables, and any number of treatment groups. Treatment effects may be assessed when the treatment groups are not randomized. In addition, the model allows for measurement errors in the criterion variables as well as in the covariates. A wide variety of hypotheses concerning the parameters of the model can be tested by means of a large sample likelihood ratio test. In particular, the usual assumptions of ANCOVA may be tested.Research reported in this paper has been partly supported by the Swedish Council for Social Science Research under project Statistical methods for analysis of longitudinal data, project director Karl G. Jöreskog, and partly by the Bank of Sweden Tercentenary Foundation under project Structural Equation Models in the Social Sciences, project director Karl G. Jöreskog.