中文读写能力及其相关因素的结构模型

运用问卷调查法考查了中文儿童读写能力及其相关因素之间的关系.通过验证性因素分析对量表的信度和效度指标进行了分析,发现量表具有较好的项目信度和结构效度.结构模型分析发现阅读听写能力影响书面语意义理解和书写技能,而书面语意义理解和书写技能对书面表达有显著影响.家庭阅读背景影响儿童的口语能力和阅读听写能力,口语能力影响意义理解和阅读听写能力,基本知觉加工影响口语能力和阅读听写,动作技能影响书写技能.上述结果表明中文字词识别、书写技能和意义理解水平都影响书面表达,即段落产生能力.而口语能力、家庭阅读背景、基本知觉加工和动作技能分别对读写能力的不同侧面产生影响.本研究还分析了模型的年龄和性别特点.     

Simple matrix methods for analyzing diffusion models of choice probability, choice response time, and simple response time

Diffusion processes (e.g., Wiener process, Ornstein-Uhlenbeck process) are powerful approaches to model human information processes in a variety of psychological tasks. Lack of mathematical tractability, however, has prevented broad applications of these models to empirical data. This tutorial explains step by step, using a matrix approach, how to construct these models, how to implement them on a computer, and how to calculate the predictions made by these models. In particular, we present models for binaries choices for unidimensional and multiattribute choice alternatives; for simple reaction time tasks; and for three alternatives choice problems.     

The Yale-Brown Obsessive-Compulsive Scale: Confirmatory Factor Analytic Findings

The psychometric properties of the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) have recently been heavily examined. Specifically, a number of researchers have been interested in determining the factor structure of this scale to find whether it best forms a one, two, or three factor model. The present study continued this examination by considering different scaling models using confirmatory factor analysis with a sample of individuals diagnosed with Obsessive-Compulsive Disorder (OCD). One hundred and forty-six individuals diagnosed with OCD participated and were administered the Y-BOCS and scales measuring depression (Hamilton Depression Rating Scale) and anxiety (Hamilton Anxiety Rating Scale). It was found that the Y-BOCS forms two different two-factor models. One model consisted of an obsessions and compulsions factor; the other composed of disturbance and symptom severity factors. It was likewise found that depression and anxiety were related to both factors in one model (disturbance and symptom severity). The finding that depression was related to obsessions and anxiety to compulsions was found, as in a previous factor analysis of the Y-BOCS. These findings suggest that OCD may be best characterized as a multidimensional syndrome that may not be adequately examined by a single unitary factor as described in the Y-BOCS.     

Conditions for factor (in)determinacy in factor analysis

The subject of factor indeterminacy has a vast history in factor analysis (Guttman, 1955; Lederman, 1938; Wilson, 1928). It has lead to strong differences in opinion (Steiger, 1979). The current paper gives necessary and sufficient conditions for observability of factors in terms of the parameter matrices and a finite number of variables. Five conditions are given which rigorously define indeterminacy. It is shown that (un)observable factors are (in)determinate. Specifically, the indeterminacy proof by Guttman is extended to Heywood cases. The results are illustrated by two examples and implications for indeterminacy are discussed.     

Efficient nonparametric approaches for estimating the operating characteristics of discrete item responses

Rationale and the actual procedures of two nonparametric approaches, called Bivariate P.D.F. Approach and Conditional P.D.F. Approach, for estimating the operating characteristic of a discrete item response, or the conditional probability, given latent trait, that the examinee's response be that specific response, are introduced and discussed. These methods are featured by the facts that: (a) estimation is made without assuming any mathematical forms, and (b) it is based upon a relatively small sample of several hundred to a few thousand examinees.Some examples of the results obtained by the Simple Sum Procedure and the Differential Weight Procedure of the Conditional P.D.F. Approach are given, using simulated data. The usefulness of these nonparametric methods is also discussed.This research was mostly supported by the Office of Naval Research (N00014-77-C-0360, N00014-81-C-0569, N00014-87-K-0320, N00014-90-J-1456).     

Integrating personality and career adaptability into vocational interest space

The relations between interests, personality and career adaptability were explored in two separate studies. In the first study, the RIASEC measure Occupational Preference Scale was applied along with personality inventories HEXACO-PI-(R)-100 and IPIP-50 on a sample of 602 university students and young adults. In the second study, PGI-Short, HEXACO-60 and Career Adapt-Abilities Scale were applied on a sample of 981 high-school graduates. Results from both studies were discussed together, and general conclusions about overlapping of interests, personality and career adaptability domains were drawn on the basis of correlational analyses and property vector fitting. Both studies have shown weak to moderate relations between interests and personality. In the HEXACO framework, it was found that Openness to Experience was positively related to creative interests, Emotionality was positively related to social interests and negatively related to technical interests, Extraversion was positively related to social and managing interests, and Honesty–Humility was negatively related to interests for business and finance. In the Big Five framework, Agreeableness was related to Social and Artistic interests, and Intellect to Artistic interests. The HEXACO personality domains showed predictive advantage for explaining interests in comparison to Big Five dimensions. The relation between career adaptability and interests was weak, and almost negligible when personality was included in hierarchical regression analysis. Career adaptability was weakly related to highly prestigious interests. Adaptability facets Concern, Control and Confidence were oriented toward data pole of interest space. The general factor of interests was weakly correlated with Openness Extraversion, Career Adaptability, and adaptability facets Confidence and Curiosity. Observed findings are as expected and in line with previous research.     

Multi‐set factor analysis by means of Parafac2

We consider multi‐set data consisting of observations, k = 1,…, K (e.g., subject scores), on J variables in K different samples. We introduce a factor model for the J × J covariance matrices , k = 1,…, K, where the common part is modelled by Parafac2 and the unique variances , k = 1,…, K, are diagonal. The Parafac2 model implies a common loadings matrix that is rescaled for each k, and a common factor correlation matrix. We estimate the unique variances by minimum rank factor analysis on for each k. The factors can be chosen orthogonal or oblique. We present a novel algorithm to estimate the Parafac2 part and demonstrate its performance in a simulation study. Also, we fit our model to a data set in the literature. Our model is easy to estimate and interpret. The unique variances, the factor correlation matrix and the communalities are guaranteed to be proper, and a percentage of explained common variance can be computed for each k. Also, the Parafac2 part is rotationally unique under mild conditions.     

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.