A Comparison of Method Effects in Two Confirmatory Factor Models for Structurally Different Methods

It is well known that measurement error in observable variables induces bias in estimates in standard regression analysis and that structural equation models are a typical solution to this problem. Often, multiple indicator equations are subsumed as part of the structural equation model, allowing for consistent estimation of the relevant regression parameters. In many instances, however, embedding the measurement model into structural equation models is not possible because the model would not be identified. To correct for measurement error one has no other recourse than to provide the exact values of the variances of the measurement error terms of the model, although in practice such variances cannot be ascertained exactly, but only estimated from an independent study. The usual approach so far has been to treat the estimated values of error variances as if they were known exact population values in the subsequent structural equation modeling (SEM) analysis. In this article we show that fixing measurement error variance estimates as if they were true values can make the reported standard errors of the structural parameters of the model smaller than they should be. Inferences about the parameters of interest will be incorrect if the estimated nature of the variances is not taken into account. For general SEM, we derive an explicit expression that provides the terms to be added to the standard errors provided by the standard SEM software that treats the estimated variances as exact population values. Interestingly, we find there is a differential impact of the corrections to be added to the standard errors depending on which parameter of the model is estimated. The theoretical results are illustrated with simulations and also with empirical data on a typical SEM model.     

Comparison of Approaches to Constructing Confidence Intervals for Mediating Effects Using Structural Equation Models

Regression mixture models, which have only recently begun to be used in applied research, are a new approach for finding differential effects. This approach comes at the cost of the assumption that error terms are normally distributed within classes. This study uses Monte Carlo simulations to explore the effects of relatively minor violations of this assumption. The use of an ordered polytomous outcome is then examined as an alternative that makes somewhat weaker assumptions, and finally both approaches are demonstrated with an applied example looking at differences in the effects of family management on the highly skewed outcome of drug use. Results show that violating the assumption of normal errors results in systematic bias in both latent class enumeration and parameter estimates. Additional classes that reflect violations of distributional assumptions are found. Under some conditions it is possible to come to conclusions that are consistent with the effects in the population, but when errors are skewed in both classes the results typically no longer reflect even the pattern of effects in the population. The polytomous regression model performs better under all scenarios examined and comes to reasonable results with the highly skewed outcome in the applied example. We recommend that careful evaluation of model sensitivity to distributional assumptions be the norm when conducting regression mixture models.     

高校教师教学效果的多因素模型的验证性分析

通过贵州省文科、理科和农科专业的二、三、四年级大学生填写的《大学生评价教师教学效果问卷》（学生用）,即SEEQ问卷,运用协方差结构模型验证了贵州地区大学生评价教师教学效果的七、九因素模型的适合性。结果表明：1．Marsh的一阶九因素结构比较稳定;2．七因素完整模型也比较稳定,比九因素结构模型更好;3．教学热情／组织、群体互动、人际和谐、知识宽度、考试／作业与阅读材料这五个维度影响学习／价值感;4．项目T1（教师讲课能在智力上激发学生,富有启发和激励性）可归为教学热情／组织性这个维度。     

Data-Generating Mechanisms Versus Constructively Defined Latent Variables in Multitrait–Multimethod Analysis: A Comment on Castro-Schilo,Widaman, and Grimm (2013)

In a recent article, Castro-Schilo, Widaman, and Grimm (2013) compared different approaches for relating multitrait–multimethod (MTMM) data to external variables. Castro-Schilo et al. reported that estimated associations with external variables were in part biased when either the correlated traits–correlated uniqueness (CT-CU) or correlated traits–correlated (methods–1) [CT-C(M–1)] models were fit to data generated from the correlated traits–correlated methods (CT-CM) model, whereas the data-generating CT-CM model accurately reproduced these associations. Castro-Schilo et al. argued that the CT-CM model adequately represents the data-generating mechanism in MTMM studies, whereas the CT-CU and CT-C(M–1) models do not fully represent the MTMM structure. In this comment, we question whether the CT-CM model is more plausible as a data-generating model for MTMM data than the CT-C(M–1) model. We show that the CT-C(M–1) model can be formulated as a reparameterization of a basic MTMM true score model that leads to a meaningful and parsimonious representation of MTMM data. We advocate the use confirmatory factor analysis MTMM models in which latent trait, method, and error variables are explicitly and constructively defined based on psychometric theory.     

Multitrait–Multimethod Assessment of Giftedness: An Application of the Correlated Traits–Correlated (Methods – 1) Model

Although the use of multiple criteria and informants is one of the most universally agreed on practices in the identification of gifted children, few studies to date have examined the convergent validity of multiple informants and objective ability tests in gifted identification. In this study, we illustrate the use of the correlated traits–correlated (methods – 1) or CT–C(M – 1) model (Eid, Lischetzke, Nussbeck, & Trierweiler, 2003) to examine the convergent validity of self, parent, and teacher ratings relative to objective cognitive ability tests in a sample of 145 4th to 6th graders. The CT–C(M – 1) analyses revealed that teacher ratings showed the highest convergence with the objective assessments, whereas self-ratings had the lowest reliabilities and insufficient validity. Parent ratings were more reliable and valid than self-reports, but were outperformed by teacher ratings for most abilities. Overall, the CT–C(M – 1) analyses showed that the convergent validity of the ratings relative to the objective test battery was highest for numerical and lowest for creative abilities. Furthermore, whereas part of the shared variance between parent and teacher ratings reflected true convergent validity, agreement between parent and self-reports was entirely due to a shared rater variance. Our analyses demonstrate the usefulness and proper interpretation of the CT–C(M – 1) approach for examining convergent validity and method effects in multitrait–multimethod data.     

Mixture Simultaneous Factor Analysis for Capturing Differences in Latent Variables Between Higher Level Units of Multilevel Data

Given multivariate data, many research questions pertain to the covariance structure: whether and how the variables (e.g., personality measures) covary. Exploratory factor analysis (EFA) is often used to look for latent variables that might explain the covariances among variables; for example, the Big Five personality structure. In the case of multilevel data, one might wonder whether or not the same covariance (factor) structure holds for each so-called data block (containing data of 1 higher level unit). For instance, is the Big Five personality structure found in each country or do cross-cultural differences exist? The well-known multigroup EFA framework falls short in answering such questions, especially for numerous groups or blocks. We introduce mixture simultaneous factor analysis (MSFA), performing a mixture model clustering of data blocks, based on their factor structure. A simulation study shows excellent results with respect to parameter recovery and an empirical example is included to illustrate the value of MSFA.     

The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.     

An SEM Approach for the Evaluation of Intervention Effects Using Pre-Post-Post Designs

A directly applicable latent variable modeling procedure for classical item analysis is outlined. The method allows one to point and interval estimate item difficulty, item correlations, and item-total correlations for composites consisting of categorical items. The approach is readily employed in empirical research and as a by-product permits examining the latent structure of tentative versions of multiple-component measuring instruments. The discussed procedure is straightforwardly utilized with the increasingly popular latent variable modeling software Mplus, and is illustrated on a numerical example.     

Effects of Missing Data Methods in SEM Under Conditions of Incomplete and Nonnormal Data

Using Monte Carlo simulations, this research examined the performance of four missing data methods in SEM under different multivariate distributional conditions. The effects of four independent variables (sample size, missing proportion, distribution shape, and factor loading magnitude) were investigated on six outcome variables: convergence rate, parameter estimate bias, MSE of parameter estimates, standard error coverage, model rejection rate, and model goodness of fit—RMSEA. A three-factor CFA model was used. Findings indicated that FIML outperformed the other methods in MCAR, and MI should be used to increase the plausibility of MAR. SRPI was not comparable to the other three methods in either MCAR or MAR.     

Mediated Effects with the Parallel Process Latent Growth Model: An Evaluation of Methods for Testing Mediation in the Presence of Nonnormal Data

This Monte Carlo simulation study investigated the impact of nonnormality on estimating and testing mediated effects with the parallel process latent growth model and 3 popular methods for testing the mediated effect (i.e., Sobel’s test, the asymmetric confidence limits, and the bias-corrected bootstrap). It was found that nonnormality had little effect on the estimates of the mediated effect, standard errors, empirical Type I error, and power rates in most conditions. In terms of empirical Type I error and power rates, the bias-corrected bootstrap performed best. Sobel’s test produced very conservative Type I error rates when the estimated mediated effect and standard error had a relationship, but when the relationship was weak or did not exist, the Type I error was closer to the nominal .05 value.     

福建省交通运输与经济发展关系的定量分析

本文利用相关系数和灰色综合关联度对福建省交通运输与国民经济发展之间的关系进行相关性分析,结果表明福建省交通运输与经济发展存在着高度的相关性;并通过灰色动态协调模型和运输弹性系数分析了"十五"时期和"十一五"时期福建省交通运输与经济发展的适应程度,得出福建省的交通运输虽然在"十五"时期有了较快的发展,但总体上仍不能适应经济发展的需求,且在各种交通运输方式中,公路运输的发展速度尤其缓慢;这主要是由于对交通运输的投资力度不够,福建省又受其自然地理条件的限制所造成的,根据以上定量分析的结果提出了一系列建议.     

PBL教学法在研究生课程"高级药物分析"教学中的探讨与实践

针对传统教学模式下理论讲授枯燥乏味、教师与学生互动较少等弊病,以生药学及药物分析专业研究生的"高级药物分析"课程为研究对象,探索在新形势下采用新的PBL教学模式,提高学生的学习兴趣,提高学生运用专业课知识解决实际问题的能力.通过我们在"高级药物分析"课程开展PBL教学研究与实践,发现PBL教学尤其适合于研究生阶段的专业课程教学.