Noniterative estimation and the choice of the number of factors in exploratory factor analysis

Based on the usual factor analysis model, this paper investigates the relationship between improper solutions and the number of factors, and discusses the properties of the noniterative estimation method of Ihara and Kano in exploratory factor analysis. The consistency of the Ihara and Kano estimator is shown to hold even for an overestimated number of factors, which provides a theoretical basis for the rare occurrence of improper solutions and for a new method of choosing the number of factors. The comparative study of their estimator and that based on maximum likelihood is carried out by a Monte Carlo experiment.The author would like to express his thanks to Masashi Okamoto and Masamori Ihara for helpful comments and to the editor and referees for critically reading the earlier versions and making many valuable suggestions. He also thanks Shigeo Aki for his comments on physical random numbers.     

FACAIC: Model selection algorithm for the orthogonal factor model using AIC and CAIC

This paper describes the authors' FORTRAN algorithm FACAIC for choosing the number of factors for an orthogonal factor model using Akaike's Information Criterion. FACAIC utilizes the IMSL subroutine OFCOMM.The authors dedicate this algorithm to Professor Hirotugu Akaike in appreciation of his pioneering work on AIC which was originally intended for the factor analysis and other statistical model identification problems.     

Some properties of the correlation matrix of dichotomous guttman items

Both the elements and the eigenvalues of the Pearson correlation matrix of dichotomous Guttman-scalable items can be expressed as simple functions of the number of items if the score distribution is uniform and there is an equal number of items at each difficulty level. Even when these special conditions do not hold, the correlations can often be expressed in a simple form by assuming a particular score distribution.The author thanks Neil Dorans, Bob Mislevy, Ledyard R Tucker, and Howard Wainer for their comments.     

Intergenerational transmission of antisocial behavior: How do kids become antisocial adults?

Exposure to many potential environmental risk factors for child antisocial behavior is associated with one of the strongest predictors of antisocial behavior, a family history of antisociality. Because most studies of putative environmental factors do not take into account genetic propensities for antisocial behavior shared between parent and child, the possibility of genetic contributions to these “environmental” markers is typically not evaluated. In this paper, we review research on the environmental correlates of antisociality, their association with parental antisociality, and highlight findings from studies that have controlled for either genetic propensities or parental antisociality.     

Evaluating the Sampling Performance of Exploratory and Cross-Validated DETECT Procedure with Imperfect Models

Among the methods proposed for identifying the number of latent traits in multidimensional IRT models, DETECT has attracted the attention of both methodologists and applied researchers as a nonparametric counterpart to other procedures. The current study investigated the overall performance of the DETECT procedure and its outcomes using a real-data sampling design recommended by MacCallum (2003 MacCallum, R.C. (2003). Working with imperfect models. Multivariate Behavioral Research, 38 (1), 113–139. http://dx.doi.org/10.1207/S15327906MBR3801_5[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) and compared the results from a purely simulated data set that was generated with a well-specified “perfect” model. The comparison revealed that the sampling behavior of the maximized DETECT value and R-ratio statistics was quite robust to minor factors and other model misspecifications that potentially exist in the real data set, as there were negligible differences between the results of the real and simulated data sets. Item classification accuracy was also nearly identical for the real and simulated data sets. The accuracy of the identified number of dimensions reported by DETECT was the only outcome with an obvious difference between the purely simulated data set and the real data set. While the difference was small for smaller sample sizes, the identified number of dimensions was more accurate for larger sample sizes when the population data set was purely simulated. In many instances, exploratory DETECT analysis outperformed the cross-validated DETECT analysis in terms of overall accuracy.     

内隐序列学习意识的具身机制

内隐序列学习意识已有三类理论如全局工作平台理论、神经可塑性理论、新异刺激理论都忽略了身体感受的关键因素, 难以揭示意识产生的根本原因。具身意识理论和研究发现, 运动/情感镜像神经元系统及与自我、认知控制系统的交互, 是初级/高级意识产生的本源, 但未涉及内隐序列学习规则意识这个对人类学习认知至关重要的领域。内隐序列学习研究实质上已接近揭示其学习机制正是感知觉运动具身学习, 其意识机制很可能是感知觉运动/情感具身意识, 并且其意识加工脑区与具身意识脑区有关键重合。未来研究可采用Granger因果大脑网络技术证明内隐序列学习意识的具身本源, 并考察已有三类意识理论的具身基础, 以及探索意识影响因素的具身机制。     

关系因素与个体因素在儿童早期社会能力中的作用

采用儿童早期行为评价量表、亲子关系量表和师生关系量表,对443名幼儿的父母和教师进行了测查,在生态系统理论的框架下探讨了儿童性别、年级及父子、母子、师生关系对早期社会能力的影响。结果发现:小班和中班儿童的师生关系好于托班;女孩的社会能力高于男孩,小班和中班儿童的社会能力高于托班。控制了性别与年级的作用之后,父子、母子与师生关系对儿童社会能力都有显著的正向预测作用,但三者的平均值具有最强的预测作用,这一结果支持了多重关系作用于儿童发展的"平均化假说"。     

Path analysis of variables affecting 36-month outcome in a population of multi-risk children

Path analysis was employed with data collected during a longitudinal neuro-behavioral follow-up study of multi-risk children. The relative impact of maternal medical history, drug abuse, and day-to-day functioning during pregnancy on the course of labor and delivery and on neonatal outcome was examined. The subsequent impact of each of these sets of variables on the child's developmental status at 36 months was then explored. An additional factor, which incorporated postnatal environmental and family functioning characteristics, was also included in the model. Maternal drug abuse had a significant effect on the course of labor and delivery, due in part to the impact of maternal drug abuse on family functioning. The results indicate that in this multi-risk population, environmental and psychosocial variables are important predictors of the child's developmental prospects.     

Factor Retention Using Machine Learning With Ordinal Data

Determining the number of factors in exploratory factor analysis is probably the most crucial decision when conducting the analysis as it clearly influences the meaningfulness of the results (i.e., factorial validity). A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we evaluated the Factor Forest with ordinal data based on different numbers of categories (2–6 categories) and compared it to common factor retention criteria. It showed higher overall accuracy for all types of ordinal data than all common factor retention criteria that were used for comparison (Parallel Analysis, Comparison Data, the Empirical Kaiser Criterion and the Kaiser Guttman Rule). The results indicate that the Factor Forest is applicable to ordinal data with at least five categories (typical scale in questionnaire research) in the majority of conditions and to binary or ordinal data based on items with less categories when the sample size is large.