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.     

A checklist for testing measurement invariance

The analysis of measurement invariance of latent constructs is important in research across groups, or across time. By establishing whether factor loadings, intercepts and residual variances are equivalent in a factor model that measures a latent concept, we can assure that comparisons that are made on the latent variable are valid across groups or time. Establishing measurement invariance involves running a set of increasingly constrained structural equation models, and testing whether differences between these models are significant. This paper provides a step-by-step guide to analysing measurement invariance.     

Factorial validity of the Mental Toughness Questionnaire-48

The purpose of this study was to assess the factorial validity of the Mental Toughness Questionnaire-48 (Clough, Earle, & Sewell, 2002). In total, 8207 participants (male n = 4019, female n = 3922, unspecified = 266) aged between 16 and 68 years (M = 37.00, SD = 12.09) completed the MTQ48. Model fit was assessed using confirmatory factor analysis (CFA) and exploratory structural equation modeling, in addition to the robust maximum likelihood estimator. Overall, our results support the factorial validity of the MTQ48 and indicate that the MTQ48 is a robust psychometric measure of mental toughness. Along with previous data, which supports the internal validity of the MTQ48 in addition to results of this study, it would appear that the MTQ48 is an acceptable method of assessing mental toughness.     

Latent curve analysis

As a method for representing development, latent trait theory is presented in terms of a statistical model containing individual parameters and a structure on both the first and second moments of the random variables reflecting growth. Maximum likelihood parameter estimates and associated asymptotic tests follow directly. These procedures may be viewed as an alternative to standard repeated measures ANOVA and to first-order auto-regressive methods. As formulated, the model encompasses cohort sequential designs and allow for period or practice effects. A numerical illustration using data initially collected by Nesselroade and Baltes is presented.The authors wish to thank John Nesselroade for providing us the data for our illustration and Karen Paul and Connie Tilse for assisting in the data analysis. This research was supported by a grant (No. AG03164) from the National Institute on Aging to the senior author.     

A multitrait-multimethod model with minimal assumptions

A new model of confirmatory factor analysis (CFA) for multitrait-multimethod (MTMM) data sets is presented. It is shown that this model can be defined by only three assumptions in the framework of classical psychometric test theory (CTT). All other properties of the model, particularly the uncorrelated-ness of the trait with the method factors are logical consequences of the definition of the model. In the model proposed there are as many trait factors as different traits considered, but the number of method factors is one fewer than the number of methods included in an MTMM study. The covariance structure implied by this model is derived, and it is shown that this model is identified even under conditions under which other CFA-MTMM models are not. The model is illustrated by two empirical applications. Furthermore, its advantages and limitations are discussed with respect to previously developed CFA models for MTMM data.     

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。     

Might “unique” factors be “common”? On the possibility of indeterminate common–unique covariances

The present paper shows that the usual factor analytic structured data dispersion matrix Λ Ψ Λ′ + Δ can readily arise from a set of scores y = Λ η + ε, where the “common” (η) and “unique” (ε) factors have nonzero covariance: Γ = Covε,η) ≠ 0. Implications of this finding are discussed for the indeterminacy of factor scores, and for the issue of invariance of factor analytic covariance models. The size of the problem is explored with numerical examples. I would like to acknowledge the large amount of effort and stimulating input supplied on the previous drafts of this paper from the reviewers, Associate Editor, and Editors of Psychometrika. Particular thanks go to William Meredith for his assistance with the final draft. Requests for reprints should be sent to Dave Grayson, 14 Poplar Grove, Lawson, NSW 2783, Australia     

The assessment of school engagement: examining dimensionality and measurement invariance by gender and race/ethnicity

The construct of school engagement has attracted growing interest as a way to ameliorate the decline in academic achievement and increase in dropout rates. The current study tested the fit of a second-order multidimensional factor model of school engagement, using large-scale representative data on 1103 students in middle school. In order to make valid model comparisons by group, we evaluated the extent to which the measurement structure of this model was invariant by gender and by race/ethnicity (European-American vs. African-American students). Finally, we examined differences in latent factor means by these same groups. From our confirmatory factor analyses, we concluded that school engagement was a multidimensional construct, with evidence to support the hypothesized second-order engagement factor structure with behavioral, emotional, and cognitive dimensions. In this sample, boys and girls did not substantially differ, nor did European-American and African-American students, in terms of the underlying constructs of engagement and the composition of these constructs. Finally, there were substantial differences in behavioral and emotional engagement by gender and by racial/ethnic groups in terms of second-order factor mean differences.     

Facets of the Childhood Anxiety Sensitivity Index Among Serbian Youth

Data from factor analytic studies using the Child Anxiety Sensitivity Index (CASI) suggest that global anxiety sensitivity (AS) is best represented by three or four underlying factors or facets. The aim of this study was to identify facets best representing the CASI structure in its Serbian version. Confirmatory factor analysis was used on data collected from 456 non-referred children in Serbia. A 13-item version of the CASI provided a better fit to the data than the original 18-item version. The four-factor model of the CASI-13 with disease, unsteady, mental incapacitation, and social concerns facets provided the best fits for the data and it was found to be fully invariant (configural, metric, and scalar invariance) across gender and age. Among Serbian children, hierarchical structure was found for a 13-item CASI version with a single higher-order factor of global AS represented by four underlying facets. Future research will consider these AS facets and their role in the development, maintenance, and exacerbation of anxiety symptoms in children.     

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.     

Examining Individual Differences in Interpersonal Influence: On the Psychometric Properties of the Generalized Opinion Leadership Scale (GOLS)

Opinion leadership describes an individual's tendency to informally influence others’ attitudes and overt behaviors. In contrast to contemporary views of opinion leadership as a highly domain-specific trait, this paper introduces a multi-faceted personality trait, generalized opinion leadership (GOL) that characterizes exceptionally influential individuals independent of a specific subject area. Two studies report on the psychometric properties of a scale to assess GOL. Study 1 is based on three independent samples (N = 1,575, N = 1,275, and N = 231) and demonstrates the factorial structure of the instrument and its measurement invariance across sex, age, and educational levels. Study 2 (N = 310) analyzes multitrait-multiinformant data to highlight the scale's discriminant validity with regard to innovativeness and trendsetting.