Stepwise Analysis of Differential Item Functioning Based on Multiple-Group Partial Credit Model

Bock, Muraki, and Pfeiffenberger (1988) proposed a dichotomous item response theory (IRT) model for the detection of differential item functioning (DIF), and they estimated the IRT parameters and the means and standard deviations of the multiple latent trait distributions. This IRT DIF detection method is extended to the partial credit model (Masters, 1982; Muraki, 1993) and presented as one of the multiple-group IRT models. Uniform and non-uniform DIF items and heterogeneous latent trait distributions were used to generate polytomous responses of multiple groups. The DIF method was applied to this simulated data using a stepwise procedure. The standardized DIF measures for slope and item location parameters successfully detected the non-uniform and uniform DIF items as well as recovered the means and standard deviations of the latent trait distributions.This stepwise DIF analysis based on the multiple-group partial credit model was then applied to the National Assessment of Educational Progress (NAEP) writing trend data.     

Assessing Preknowledge Cheating via Innovative Measures: A Multiple-Group Analysis of Jointly Modeling Item Responses,Response Times,and Visual Fixation Counts

Many approaches have been proposed to jointly analyze item responses and response times to understand behavioral differences between normally and aberrantly behaved test-takers. Biometric information, such as data from eye trackers, can be used to better identify these deviant testing behaviors in addition to more conventional data types. Given this context, this study demonstrates the application of a new method for multiple-group analysis that concurrently models item responses, response times, and visual fixation counts collected from an eye-tracker. It is hypothesized that differences in behavioral patterns between normally behaved test-takers and those who have different levels of preknowledge about the test items will manifest in latent characteristics of the different data types. A Bayesian estimation scheme is used to fit the proposed model to experimental data and the results are discussed.     

PROC LCA: A SAS Procedure for Latent Class Analysis

Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.     

Multiple-Group Factor Analysis Alignment

This article presents a new method for multiple-group confirmatory factor analysis (CFA), referred to as the alignment method. The alignment method can be used to estimate group-specific factor means and variances without requiring exact measurement invariance. A strength of the method is the ability to conveniently estimate models for many groups. The method is a valuable alternative to the currently used multiple-group CFA methods for studying measurement invariance that require multiple manual model adjustments guided by modification indexes. Multiple-group CFA is not practical with many groups due to poor model fit of the scalar model and too many large modification indexes. In contrast, the alignment method is based on the configural model and essentially automates and greatly simplifies measurement invariance analysis. The method also provides a detailed account of parameter invariance for every model parameter in every group.     

混合效应随机微分方程的贝叶斯分析(英文)

采用带有随机微分方程的非线性混合效应模型对群体药物代谢动力学数据建模,通过在状态方程中引入随机项,将常微分方程扩展到随机微分方程.和常微分方程相比,随机微分方程可解决群体药物代谢动力学模型中相关残差问题.利用贝叶斯估计对非线性混合效应随机微分方程模型参数进行估计,给出群体参数及个体参数的精确后验分布,将Gibbs和Metropolis-Hastings算法相结合,给出参数估计值.通过计算机模拟和实例分析验证了方法的可靠性,结果表明利用非线性混合效应随机微分方程模型及贝叶斯估计方法分析群体药物代谢动力学数据是可行的.     

本科院校教学质量改进路径研究

基于学生学业参与的视角,利用2008年北京市高等教育学生发展调查数据,采用多群组结构方程模型分析我国本科院校教学质量改进的路径。研究发现:(1)本科教学质量改进路径包括直接和间接两类路径。提升教学质量,不仅仅是直接改进课程与教学方式,而且这类投入能够有效激励学生学业参与,从而间接提升教学质量。(2)在以学生学业参与为中介的间接改进路径中,程序性学业参与发挥主要中介作用。(3)相对其他层次院校,一般应用型大学采取以学生学业参与为中介的间接改进路径更有利于提升教学质量。     

CFI versus RMSEA: A comparison of two fit indexes for structural equation modeling

This article compares two structural equation modeling fit indexes—Bentler's ( 1990; Bentler & Bonett, 1980) Confirmatory Fit Index (CFI) and Steiger and Lind's (1980; Browne & Cudeck, 1993) Root Mean Square Error of Approximation (RMSEA). These two fit indexes are both conceptually linked to the noncentral chi‐square distribution, but CFI has seen much wider use in applied research, whereas RMSEA has only recently been gaining attention. The article suggests that use of CFI is problematic because of its baseline model. CFI seems to be appropriate in more exploratory contexts, whereas RMSEA is appropriate in more confirmatory contexts. On the other hand, CFI does have an established parsimony adjustment, although the adjustment included in RMSEA may be inadequate.     

Approximations to the Distributions of Fit Indexes for Misspecified Structural Equation Models

Approximations to the distributions of goodness-of-fit indexes in structural equation modeling are derived with the assumption of multivariate normality and slight misspecification of models. The fit indexes considered in this article are Joreskog and Sorbom's goodness-of-fit index (GFI) and the adjusted GFI, McDonald's absolute GFI, Steiger and Lind's root mean squared error of approximation, Steiger's Γ₁ and Γ₂, Bentler and Bonett's normed fit index, Bollen's incremental fit index and ρ₁, Tucker and Lewis's index ρ₂, and Bentler's fit index (McDonald and Marsh's relative noncentrality index). An approximation to the asymptotic covariance matrix for the fit indexes is derived by using the delta method. Furthermore, approximations to the densities of the fit indexes are obtained from the transformations of the asymptotically noncentral chi-square distributed variable. A simulation is carried out to confirm the accuracy of the approximations.     

Factor structure and specific variance of the WPPSI subtests at six age levels

The Wechsler Preschool and Primary Scale of Intelligence (WPPSI) was factor analyzed at each of six age levels between 4 and 6½ years through the method of principal factors. A two-factor solution, corresponding essentially to Wechsler's a priori grouping of the subtests into a Verbal and nonverbal or Performance scale, best described the 11 WPPSI subtests at each age level. Examination of the common, specific, and error variance components of the subtests across age levels indicated adequate test specificity to allow for individual interpretation of all subtests except Information and Comprehension when the null hypothesis model of ipsative interpretation of individual tests is employed.     

Application of IRT Fixed Parameter Calibration to Multiple-Group Test Data

ABSTRACT

In applications of item response theory (IRT), fixed parameter calibration (FPC) has been used to estimate the item parameters of a new test form on the existing ability scale of an item pool. The present paper presents an application of FPC to multiple examinee groups test data that are linked to the item pool via anchor items, and investigates the performance of FPC relative to an alternative approach, namely independent 0–1 calibration and scale linking. Two designs for linking to the pool are proposed that involve multiple groups and test forms, for which multiple-group FPC can be effectively used. A real-data study shows that the multiple-group FPC method performs similarly to the alternative method in estimating ability distributions and new item parameters on the scale of the item pool. In addition, a simulation study shows that the multiple-group FPC method performs nearly equally to or better than the alternative method in recovering the underlying ability distributions and the new item parameters.     

Testing Time-Invariance of Variable Specificity in Repeated Measure Designs Using Structural Equation Modeling

A structural equation modeling method for examining time-invariance of variable specificity in longitudinal studies with multiple measures is outlined, which is developed within a confirmatory factor-analytic framework. The approach represents a likelihood ratio test for the hypothesis of stability in the specificity part of the residual term associated with repeated administration of each measure. The procedure can be used in the search for parsimonious versions of multiwave multiple-indicator models, to test for variable specificity in them, and to examine assumptions underlying particular parameter estimation procedures in repeated measure designs. The outlined method is illustrated with empirical data.     

Browne's composite direct product model for MTMM correlation matrices

This article is about the analysis of the multitrait‐multimethod (MTMM) correlation matrix with the composite direct product model proposed by Browne (1984). Campbell and Fiske's (1959) criteria for convergent and discriminant validity have a direct interpretation in the composite direct product model. The model has a multiplicative property suggested as appropriate for MTMM correlation matrices by Campbell and O'Connell (1967, 1982), and it can be fitted to the correlation matrix by means of conventional software.     

Model Selection in Structural Equation Models With Continuous and Polytomous Data

Recently, analysis of structural equation models with polytomous and continuous variables has received a lot of attention. However, contributions to the selection of good models are limited. The main objective of this article is to investigate the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data and propose a model selection procedure for obtaining good models for the underlying substantive theory. The maximum likelihood estimate is obtained by a Monte Carlo Expectation Maximization algorithm, in which the E step is evaluated via the Gibbs sampler and the M step is completed via the method of conditional maximization. The convergence of the Monte Carlo Expectation Maximization algorithm is monitored by the bridge sampling. A model selection procedure based on Bayes factor and Occam's window search strategy is proposed. The effectiveness of the procedure in accounting for the model uncertainty and in picking good models is discussed. The proposed methodology is illustrated with a real example.     

Testing Factorial Invariance With Unbalanced Samples

In testing the factorial invariance of a measure across groups, the groups are often of different sizes. Large imbalances in group size might affect the results of factorial invariance studies and lead to incorrect conclusions of invariance because the fit function in multiple-group factor analysis includes a weighting by group sample size. The implication is that violations of invariance might not be detected if the sample sizes of the 2 groups are severely unbalanced. In this study, we examined the effects of group size differences on results of factorial invariance tests, proposed a subsampling method to address unbalanced sample size issue in factorial invariance studies, and evaluated the proposed approach in various simulation conditions. Our findings confirm that violations of invariance might be masked in the case of severely unbalanced group size conditions and support the use of the proposed subsampling method to obtain accurate results for invariance studies.     

Dependence on state funding,local educational opportunities,and access to high school credentials in israel

A major shortcoming of macro-spatial research undertaken to date in Israel pertains to the neglect of state investment-related measures and the extent to which they mediate unequal opportunities to learn (OTL) and educational opportunities between localities. In the present study, OTL refer to class size and high-school tracking patterns. Educational opportunities refer to two measures: the school system's holding power and access to educational credentials. Two specific questions were raised in this respect: first, we looked into the extent to which the size and ethnic stratification of Israeli localities, and their differential dependence on state financial schemes, affect available OTL and educational opportunities. Second, we examined how a locality's OTL affect educational opportunities. The data on which the present study is based are aggregated at locality level and refer to 89 localities, of which 21 were Arab and 78 were Jewish. Bentler's EQS Structural Equation Modeling (SEM) program was applied to test the research questions. The findings suggest that locality characteristics and dependence on state finance directly affect both OTL and educational opportunities. However, the school system's holding power and access to educational credentials is affected by differential mechanisms. On the one hand, locality-level variables and average class size significantly affect the school system's holding power. On the other hand, access to educational credentials is affected primarily by a locality's ethnic affiliation and its dependence on state funding. The implications of these findings for educational policy and reform are then discussed.     

Guilford's Concept of Social Intelligence Revisited

The purpose of this study was to verify, through the application of confirmatory factor analysis, Guilford's formulation of social intelligence as a multidimensional concept. O'Sullivan, Guildford and Demille (1965), using exploratory factor analysis, found six factors of cognitive social intelligence. Using LISREL, we reanalyzed their data and tested two distinctly different factor models: (1a) the orthogonal six‐factor model proposed by O'Sullivan et al., (1b) the same six‐factor model except that the factors were allowed to intercorrelate, and (2) a single‐factor model. The orthogonal six‐factor model did not fit the data and, although the oblique variant of this model was an improvement, the single‐factor model fitted the data better in addition to being more parsimonious. This suggests that Guilford's concept of social intelligence is unnecessarily complicated and exposes a weakness in his Structure of Intellect Model. On the positive side, measurement of cognitive social intelligence should be more straightforward and our findings indicate which of Guilford's recommended tests are the best to use.     

基于EKF和RBF的路面附着系数估计

针对车辆行驶下的路面附着系数估计问题,提出了扩展卡尔曼滤波算法(EKF,Extended Kalman Filter)与径向基神经网络(RBF,Radial Basis Functionneural network)相融合。通过扩展卡尔曼滤波算法得出路面附着系数估计所需要的车辆状态参数,结合轮速等直接数据采用径向基神经网络对路面附着系数进行估计。神经网络的训练样本通过Carsim/Simulink收集不同行驶工况,并采用差值寻优的方法对径向基神经网络算法中的决定系数进行优化。基于双移线工况验证了该算法在路面附着系数估计上具有较高的精准度。     

Two-Step Approach to Partial Factorial Invariance: Selecting a Reference Variable and Identifying the Source of Noninvariance

To date, no effective empirical method has been available to identify a truly invariant reference variable (RV) in testing measurement invariance under a multiple-group confirmatory factor analysis. This study proposes a method that, in selecting an RV, uses the smallest modification index (min-mod). The method’s performance is evaluated using 2 models: (a) a full invariance model, and (b) a partial invariance model. Results indicate that for both models the min-mod successfully identifies a truly invariant RV (Study 1). In Study 2, we use the RV found in Study 1 to further evaluate the performance of item-by-item Wald tests at locating a noninvariant variable. The results indicate that Wald tests overall performed better with an RV selected in a partial invariance model than an RV selected in a full invariance model, although in certain conditions their performances were rather similar. Implications and limitations of the study are also discussed.