探索性结构方程建模(ESEM):EFA和CFA的整合

探索性结构方程建模(ESEM)是在测量模型部分使用了类似于EFA模型的SEM.作为一种高级统计方法,ESEM整合了EFA和CFA两种因子分析方法的功能和优点.通过ESEM,研究者既可以灵活地探索因子结构,又可以系统地验证因子模型,为潜变量的关系分析提供更适宜的测量模型.ESEM已经在某些社科领域的研究中得到应用,是一种值得推介的因子分析方法.ESEM的具体应用问题,例如因子旋转方法的选用、测验信度评价等,仍有待探讨.     

Evaluating the Factor Structure of the MIDI Personality Scale Using Exploratory Structural Equation Modeling

This study investigated the factor structure of the 26‐item Midlife Development Inventory (MIDI) Personality Scale in a sample of 2,720 Americans. It was found that whereas confirmatory factor analysis (CFA) did not provide an acceptable fit to the data, exploratory structural equation modeling (ESEM) provided an acceptable fit. The results of ESEM revealed that the a priori five‐factor structure of personality was generally consistent with the data, and all items had salient loadings on their target factors. ESEM also revealed that some of the items contributed significantly to more than one personality factor. The results are in line with previous research, and indicate that ESEM is more suitable than CFA for the study of personality traits.     

探索性因素分析、目标旋转与因素结构的一致性

在因素结构水平上评估跨群体的一致性是在心理学研究中常常遇见的一个问题,对此问题的解答,可以选择探索性因素分析→目标旋转→一致性评估这一途径。本文首先介绍正交目标旋转的简单原理,然后介绍其在心理研究中的应用以及相关软件和程序。目标旋转之后,结构一致性的量化可以采用一致性系数等指标,这些指标可采用一定的实证分布、近似处理或经验标准进行统计检验。之后采用一项实证数据,演示探索性因素分析、目标旋转以及结构一致性的评估方法。     

Structural Equation Modeling of Social Networks: Specification,Estimation, and Application

Psychologists are interested in whether friends and couples share similar personalities or not. However, no statistical models are readily available to test the association between personalities and social relations in the literature. In this study, we develop a statistical model for analyzing social network data with the latent personality traits as covariates. Because the model contains a measurement model for the latent traits and a structural model for the relationship between the network and latent traits, we discuss it under the general framework of structural equation modeling (SEM). In our model, the structural relation between the latent variable(s) and the outcome variable is no longer linear or generalized linear. To obtain model parameter estimates, we propose to use a two-stage maximum likelihood (ML) procedure. This modeling framework is evaluated through a simulation study under representative conditions that would be found in social network data. Its usefulness is then demonstrated through an empirical application to a college friendship network.     

探索性因素分析——最近10年的评述

目的：(1)介绍国外心理统计学界对探索性因素分析中几个重大问题的基本观点;(2)系统地评述过去10年里(1991～2000年)我国心理学研究对这一技术的使用情况;(3)强调运用这一技术时值得注意的一些事项,以期这一技术对我国心理学研究发挥更大的作用。方法：作检索了《心理学报》和《心理科学》在1991～2000年间与探索性因素分析有关的章,对其中以探索性因素分析为主要研究方法的重点章(feature article)进行了编码,统计了频数及百分比分布。结果：我国心理学研究在积极使用探索性因素分析这一先进统计技术时还存在一些问题,主要表现在：(1)在确定因素个数时,倾向于机械地依靠桌个单一方法来作决定,(2)大量使用正交旋转,(3)过于依赖SPSS,(4)对因素分析过程中的重要信息／结果报告不够。结论：探索性因素分析在过去十年得到了广泛的应用,如能吸收国外同行的一些观点,探索性因素分析这一技术在我国心理学研究中必将获得更广泛有效的应用。     

Factor structure and initial validation of a brief measure of perceived emotional and physical distress tolerance in post-9/11 US Veterans

Distress tolerance (i.e., perceived or actual capacity to tolerate aversive internal states) has received considerable research attention as a transdiagnostic risk-factor underlying the development and maintenance of psychopathology. Lower levels of emotional distress tolerance have been linked to psychopathology (e.g. Posttraumatic Stress Disorder) within Military populations; however, the association of physical distress tolerance to psychopathology in this population has been under-researched. This research gap may be due in part to a paucity of comprehensive, temporally stable and brief measures of distress tolerance that have been validated within Military populations, which may hinder further examination and refinement of the construct. Addressing this problem, the current study evaluates the psychometric properties of a novel and brief measure of emotional and physical distress tolerance in a sample of United States post-9/11 Veterans. Participants were 307 Veterans (M_age = 38.9, 67.7% male) who completed the 10-item Distress Tolerance Inventory at baseline and annual follow-up. Exploratory structural equation modeling was used to examine the optimal latent factor structure and longitudinal invariance of the DTI measurement model, along with correlational analyses to examine the convergent properties of the DTI subscales. The DTI reflected a longitudinally invariant two-factor structure (emotional and physical distress tolerance), with excellent internal consistency and preliminary evidence of convergent validity. Thus, the DTI represents a brief, reliable and temporally stable measure of physical and emotional distress tolerance.     

To Balance or not to Balance: Confirmatory Factor Analysis of the Affect-Balance Scale

This study examined the psychometric quality of the Affect-Balance Scale (ABS) (Bradburn, 1969) using data collected from 292 middle-aged and older adults, living independently. The dimensionality of the scale was examined, the quality of individual items was tested, and the validity of the ABS was studied. Using a tetrachoric correlation matrix with the robust weighted least squares (WLSMV) estimation method of the Mplus program, we found that two moderately correlated (r = -0.37) constructs are needed to adequately account for the pattern of item scores in the ABS. Two of the 10 ABS items were found to be problematic. When raw sum scores were used in analysis, the correlation between the positive-affect and the negative-affect subscales was lower (r = -0.17), indicating that random and nonrandom measurement error masked the relationship between the two. While affect-balance correlated substantially with five criterion well-being measures, the negative-affect subscale (which constitutes half of the ABS) had a similar pattern of correlations, with only slightly lower magnitude. The theoretical construct of nobreak 'balance' is also questioned. The 'balance' scoring method (subtracting the negative-affect subscale score from the positive-affect subscale score) nets exactly the same score as does summing scores from both subscales together. Accordingly, the summed scores have the very same correlations with other variables as do the balance scores.     

Factor structure and measurement invariance of the multidimensional driving style inventory across gender and age: An ESEM approach

The Multidimensional Driving Style Inventory (MDSI) is the most comprehensive measure of typical driving behavior to date and has been frequently used to compare driving styles across different groups of drivers, particularly between gender- and age-related groups. However, the factor structure of MDSI has not been clearly established and its measurement invariance has not been demonstrated. The goal of the present study was to examine the internal structure and measurement invariance of the MDSI across gender and age. A sample of 1277 drivers from Argentina responded to the Argentinian version of the MDSI. Exploratory structural equation modeling (ESEM) was used to test the factor structure and measurement invariance across females (n = 602) and males (n = 675), and across young (18–29, n = 558), adult (30–49, n = 395) and older (50 and older, n = 317) drivers. The results showed that a 36-item six-factor ESEM model represented by risky, angry, dissociative, anxious, distress-reduction and careful and patient driving styles was the best model based on fit indices and interpretability. Configural, weak and strong invariance of the six-factor ESEM model across gender and age was also supported. The MDSI in its Argentinian version is equivalent across gender and age, supporting the validity of previous research findings examining gender and age differences in driving styles. Future studies should examine the measurement invariance of the MDSI across other relevant driving-related variables.     

3～6岁幼儿好奇心结构探索与验证性因子分析

以幼儿园3～6岁幼儿为对象,通过80名幼儿教师对480名幼儿好奇心行为特征描述的开放式问卷调查,编制出60个项目的初始问卷,对500名幼儿的初测结果进行探索性因子分析后,形成了33个项目的正式问卷,对1000名幼儿的评价结果进行验证性因子分析,结果表明:教师评价的3～6岁幼儿好奇心结构包括敏感、对未知事物的关注、好问、喜欢摆弄、探索持久和好奇体验6个因子。     

Model Fit and Item Factor Analysis: Overfactoring,Underfactoring, and a Program to Guide Interpretation

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker–Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.     

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent—but mostly unresolved—issues in the area of dynamic multilevel modeling.     

题目因素分析：基于SEM和基于IRT的两类方法

心理和教育测量一般只能达到顺序量表的水平,其测量数据与被测因子间并非简单线性关系。题目因素分析是用来描述测量题目与因子间非线性关系的统计模型。题目因素分析主要有基于结构方程模型和基于项目反应理论两类方法,两类方法之间存在紧密的联系,甚至可以看作是同一模型的两种表现形式。本文详细阐述了该关系,同时对两类方法在参数估计、模型拟合指标、测量一致性检验和支撑软件等方面的特点进行了分析和比较,以便研究者选择最为适合其研究的方法。     

Reliability of Summed Item Scores Using Structural Equation Modeling: An Alternative to Coefficient Alpha

A method is presented for estimating reliability using structural equation modeling (SEM) that allows for nonlinearity between factors and item scores. Assuming the focus is on consistency of summed item scores, this method for estimating reliability is preferred to those based on linear SEM models and to the most commonly reported estimate of reliability, coefficient alpha.     

Model Implied Instrumental Variables (MIIVs): An Alternative Orientation to Structural Equation Modeling

Few dispute that our models are approximations to reality. Yet when it comes to structural equation models (SEMs), we use estimators that assume true models (e.g. maximum likelihood) and that can create biased estimates when the model is inexact. This article presents an overview of the Model Implied Instrumental Variable (MIIV) approach to SEMs from Bollen (1996). The MIIV estimator using Two Stage Least Squares (2SLS), MIIV-2SLS, has greater robustness to structural misspecifications than system wide estimators. In addition, the MIIV-2SLS estimator is asymptotically distribution free. Furthermore, MIIV-2SLS has equation-based overidentification tests that can help pinpoint misspecifications. Beyond these features, the MIIV approach has other desirable qualities. MIIV methods apply to higher order factor analyses, categorical measures, growth curve models, dynamic factor analysis, and nonlinear latent variables. Finally, MIIV-2SLS permits researchers to estimate and test only the latent variable model or any other subset of equations. In addition, other MIIV estimators beyond 2SLS are available. Despite these promising features, research is needed to better understand its performance under a variety of conditions that represent empirical applications. Empirical and simulation examples in the article illustrate the MIIV orientation to SEMs and highlight an R package MIIVsem that implements MIIV-2SLS.     

Less is not always more: The case of the 36-item short form of the Zimbardo Time Perspective Inventory

Recently, a shortened version of the Zimbardo Time Perspective Inventory (ZPTI; Zimbardo & Boyd, 1999) was proposed as a “gold standard” (Sircova et al., 2014, p. 9). In this study, we examined the internal consistency and structural validity of this version of the ZPTI in samples of adolescents from the United Kingdom (N = 913) and the United States (N = 815), and adults from Australia (N = 667). Results provided support for the internal consistency of ZPTI scores, but structural validity analyses indicated poor fit and numerous problematic items. The findings call into question the use of scores on this shortened version of the ZTPI.     

Exploratory structural equation modeling,bifactor models,and standard confirmatory factor analysis models: Application to the BASC-2 Behavioral and Emotional Screening System Teacher Form

Several psychological assessment instruments are based on the assumption of a general construct that is composed of multiple interrelated domains. Standard confirmatory factor analysis is often not well suited for examining the factor structure of such scales. This study used data from 1885 elementary school students (mean age = 8.77 years, SD = 1.47 years) to examine the factor structure of the Behavioral Assessment System for Children, Second Edition (BASC-2) Behavioral and Emotional Screening System (BESS) Teacher Form that was designed to assess general risk for emotional/behavioral difficulty among children. The modeling sequence included the relatively new exploratory structural equation modeling (ESEM) approach and bifactor models in addition to more standard techniques. Findings revealed that the factor structure of the BASC-2 BESS Teacher Form is multidimensional. Both ESEM and bifactor models showed good fit to the data. Bifactor models were preferred on conceptual grounds. Findings illuminate the hypothesis-generating power of ESEM and suggest that it might not be optimal for instruments designed to assess a predominant general factor underlying the data.     

基于多层结构方程模型的情境效应分析—— 兼与多层线性模型比较

多层(嵌套)数据的变量关系研究, 必须借助多层模型来实现。两层模型中, 层一自变量Xij按组均值中心化, 并将组均值 置于层2截距方程式中, 可将Xij对因变量Yij的效应分解为组间和组内部分, 二者之差被称为情境效应, 称为情境变量。多层结构方程模型(MSEM)将多层线性模型(MLM)和结构方程模型(SEM)相结合, 通过设置潜变量和多指标的方法校正了MLM在情境效应分析中出现的抽样误差和测量误差, 同时解决了数据的多层(嵌套)结构和潜变量的估计问题。除了分析原理的说明, 还以班级平均竞争氛围对学生竞争表现的情境效应为例进行分析方法的示范, 并比较MSEM和MLM的异同, 随后展望了MSEM情境效应模型、情境效应无偏估计方法和情境变量研究的拓展方向。     

The Childhood Executive Functioning Inventory (CHEXI): Factor structure,measurement invariance,and correlates in US preschoolers

This study tests the factor structure, measurement invariance, and correlates of the Childhood Executive Functioning Inventory (CHEXI) with a large and diverse sample of 3- to 5-year-olds (n = 844). Consistent with previous studies, a two-factor model that distinguishes working memory from inhibition provides the best fit to the observed data. This two-factor model has been shown to demonstrate strong measurement invariance for different subgroups of children (boys vs. girls, high vs. low income). Whereas boys tend to have greater working memory and inhibition difficulties (Cohen’s d = 0.15 and 0.20, respectively), children from low-income households tend to have more working memory problems than their peers from high-income households (Cohen’s d = 0.25). Finally, correlations between CHEXI scores, examiner reports of child behavior, and child performance on a battery of executive function (EF) tasks were investigated. CHEXI scores were found to be more consistently related to examiner reports of child behavior than child performance on EF tasks. Tthe strengths and weaknesses of the CHEXI as a questionnaire measure of EF are discussed, and directions for future research are suggested.     

Development and initial validation of the coaching life skills in sport questionnaire

ObjectivesResearchers have advocated for coaches to intentionally teach life skills to their athletes given the accrued developmental benefits. The purpose of this research was to develop and offer initial evidence of validity and reliability for a measure assessing the extent to which coaches are intentional in their approaches to teaching life skills through sport.DesignUsing two independent samples (n = 623; n = 817), three studies were conducted to develop and initially validate the Coaching Life Skills in Sport Questionnaire (CLSS-Q).MethodIn study one, the scale development and content validity processes of the initial measure were conducted. In study two, the factorial validity of the CLSS-Q was tested through exploratory structural equation modeling and confirmatory factor analyses. Moreover, scale invariance, discriminant and convergent validity, and construct reliability were assessed. In study three, concurrent validity was assessed with the combined sample (N = 1440) using two theoretically linked constructs.ResultsThe results of the three studies provide initial evidence for the validity and reliability of the 5-factor 36-item CLSS-Q.ConclusionsThe CLSS-Q represents a useful scale for researchers interested in examining levels of intentionality in coaches' teaching of life skills through sport. As scale development is an ongoing process, further research is needed to continue to accumulate evidence for the validity and reliability of the CLSS-Q.