结构方程模式的发展与应用

结构方程模式(Structural Equation Modeling, SEM)在教育、心理研究中得到非常广泛的应用,但目前还有一些理论和技术上的问题有待于澄清。尤其在国内,许多研究者对这种统计方法不十分了解,因此导致了对该程序的误用以及对结果的错误解释。为此,文章首先对SEM的设计和估计方面的一些问题加以讨论;其次指出SEM在近期的一些发展以及在某些方面的最新应用;最后文章还对SEM的局限性进行了简要评述。     

Model specification search using a genetic algorithm with factor reordering for a simple structure factor analysis model

Abstract:  Many techniques for automated model specification search based on numerical indices have been proposed, but no single decisive method has yet been determined. In the present article, the performance and features of the model specification search method using a genetic algorithm (GA) were verified. A GA is a robust and simple metaheuristic algorithm with great searching power. While there has already been some research applying metaheuristics to the model fitting task, we focus here on the search for a simple structure factor analysis model and propose a customized algorithm for dealing with certain problems specific to that situation. First, implementation of model specification search using a GA with factor reordering for a simple structure factor analysis is proposed. Then, through a simulation study using generated data with a known true structure and through example analysis using real data, the effectiveness and applicability of the proposed method were demonstrated.     

家庭因素与青少年犯罪的关系研究述评

随着犯罪理论（如社会控制理论、依恋理论、社会学习理论和问题行为理论）的不断发展,家庭因素与青少年犯罪关系的研究重点已由最初的家庭结构变量（如家庭完整性、家庭经济状况）逐渐扩展并聚焦在家庭功能变量（如家庭气氛、亲子依恋和亲子沟通等）和家庭成员行为变量（如教养方式、父母监控等）。近年来该研究领域开始深入探讨青少年认知过程在教养方式与青少年犯罪之间所起的中介作用,以及儿童青少年的人格、社区环境、神经生物学等变量与家庭因素的交互作用。最后本文总结了该领域研究的最新趋势和以往研究的不足     

Testing the Domain Specificity of Creativity with Kaufman Domains of Creativity Scale: A Meta-Analytic Confirmatory Factor Analysis

Domain-specificity is a topic of debate within the field of creativity. To shed light on this issue, we conducted a meta-analysis of cross-domain correlations based on the Kaufman Domains of Creativity Scale (K-DOCS). To evaluate the model fit of one general factor versus two factors that encompass the primary K-DOCS subscales (Scholarly, Everyday, Artistic, Scientific, and Performance), we employed the one-stage meta-analytic structural equation modeling (OSMASEM) approach. Poor fit of these models would provide evidence of domain-specificity, as the proposed models would not outperform the independence model. Our analysis included 45 correlation matrices from 30 studies, with a total sample size of 31,136 participants. The results provided support for a general domain of creativity, as well as a two-factor solution consisting of Arts and Sciences factors. Among the subscales, the highest correlation was found between the Artistic and Performance domains (r = .478), while the smallest correlation was observed between the Everyday and Scientific domains (r = .178). Furthermore, moderator analyses incorporating age and gender revealed that the Scientific and Everyday subscales exhibited a stronger factor load in older participants compared to younger participants. Implications are discussed for research and practice.     

The Importance of the Default Mode Network in Creativity—A Structural MRI Study

Anecdotal reports as well as behavioral studies have suggested that creative performance benefits from unconscious processes. So far, however, little is known about how creative ideas arise from the brain. In the current study, we aimed to investigate the neural correlates of creativity by means of structural MRI research. Given that unconscious and less controlled processes are important in creative thinking, structural brain research may find a positive correlation between well‐established creativity measures and cortical thickness in brain structures of the default mode network (i.e., the counterpart of the cognitive control network). Individuals performed the Alternative Uses task by which an individual's cognitive flexibility and the average uniqueness and average creativity of a participant's ideas were assessed. We computed optimized voxel‐based‐morphometry (VBM) to explore the correlation between inter‐individual differences in creativity and inter‐individual differences in gray matter volume. For all creativity measures, a positive correlation was found between creative performance and gray matter volume of the default mode network. These findings support the idea that the default mode network is important in creativity, and provide neurostructural support for the idea that unconscious forms of information processing are important in creativity. Theoretical and practical implications are discussed.     

The Chinese Version of the Revised Creativity Domain Questionnaire (CDQ‐R): First Evidence for its Factorial Validity and Systematic Association with the Big Five

The present study examines the factor structure of a Chinese version of the Revised Creativity Domain Questionnaire (CDQ‐R; Kaufman, Waterstreet, Ailaouni, Whitcomb, Roe, & Riggs, 2009) as well as its relation to Big Five personality traits within a Chinese sample (N = 787). Analyses indicate the appropriateness of the Chinese version of the CDQ‐R in terms of internal consistency, factorial validity as well as convergent and divergent validity concerning the Big Five personality factors. Revealing some culture‐specific variation, confirmatory factor analysis indicated a slight superiority of a five‐factor model for this Chinese sample over the existing four‐factor model established with American samples. This higher level of differentiation in terms of one factor of the creativity domain could be explained on the basis of the specific characteristics of the Chinese culture.     

心身障碍共病与慢性疼痛关联的结构模型

常见的心身障碍共病可以理解为一种新的精神疾病结构模型：情绪障碍、抑郁、焦虑及躯体化障碍等归为内化性疾病谱系的组成元素,物质滥用和反社会行为障碍归为外化性疾病谱系的组成元素。本文分析探讨评估慢性疼痛与这一模型的联系。社会心理和生物学的研究证据表明慢性疼痛与内化性障碍密切相关,提示内化-外化性疾病模型可以作为一个有效的研究架构,为探讨慢性疼痛与情感、抑郁焦虑及其他相关精神障碍的关联共病机制提供新的研究方向和新思路。     

Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models

Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.     

Rejoinder: Remaining Challenges in Investigating Grade-Retention Effectiveness

This rejoinder, in response to the commentaries of Steiner, Park, and Kim (this issue) and Reshetnyak, Cham, and Hughes (this issue), discusses remaining challenges in grade retention research. First, a same-age comparison assumes that the instruments used in different grades measure ability equally well. We discuss the importance of evaluating the properties of the scaling process to address whether this assumption has been met. Second, we discuss issues in the selection of covariates to be included in the weights. Third, we discuss the unconfoundedness assumption and the problem of remaining imbalance. Finally, we provide an empirical illustration showing that studying grade-retention effectiveness comes with multiple methodological decisions that are rooted in a bias–variance trade-off.     

Using Marginal Structural Modeling for Grade Retention Effects

Vandecandelaere, Vansteelandt, De Fraine, and Van Damme (this issue) described marginal structural modeling (MSM) and used it to estimate the effects of a time-varying intervention, retention (holding back) in school grades, on students' math achievement. This commentary supplements Vandecandelaere et al. (this issue) and discusses several topics in retention studies and MSM. First, we discuss the importance of equating time-varying confounders in retention studies. Second, we discuss same-grade and same-age comparisons in retention studies. Third, we discuss one important section in the authors' overview of MSM: why standard methods (e.g., ANCOVA, propensity score analysis) cannot properly adjust for time-varying confounders. Finally, using the grade retention analyses in Vandecandelaere et al. (this issue) as an example, we provide our insights on four aspects of MSM: (a) covariate selection, (b) estimation of weights, (c) evaluation of balance properties, and (d) missing data handling.