On muthén’s maximum likelihood for two-level covariance structure models

Data in social and behavioral sciences are often hierarchically organized. Special statistical procedures that take into account the dependence of such observations have been developed. Among procedures for 2-level covariance structure analysis, Muthén’s maximum likelihood (MUML) has the advantage of easier computation and faster convergence. When data are balanced, MUML is equivalent to the maximum likelihood procedure. Simulation results in the literature endorse the MUML procedure also for unbalanced data. This paper studies the analytical properties of the MUML procedure in general. The results indicate that the MUML procedure leads to correct model inference asymptotically when level-2 sample size goes to infinity and the coefficient of variation of the level-1 sample sizes goes to zero. The study clearly identifies the impact of level-1 and level-2 sample sizes on the standard errors and test statistic of the MUML procedure. Analytical results explain previous simulation results and will guide the design or data collection for the future applications of MUML.This research was supported by NSF Grant DMS04-37167.We thank Dr.Bengt Muthén for providing key references. We are also grateful to three expert reviewers for their constructive comments that have led the paper to an improvement over the previous version.This revised article was published online in August 2005 with the PDF paginated correctly.     

A response Consistency statistic for the millon clinical multiaxial inventory (MCMI)

Response style in objective psychological testing is an important issue in the reliability and validity of tests as well as in the interpretation of test results. The MCMI provides two response-style indices, the validity scale and the weight factor. The present work presents an additional statistic to assess random response in subjects. The Consistency Coefficient is the correlation between the subjects' endorsement of even and odd items across the 20 MCMI scales. The distributions of 500 patient and 500 randomly generated profiles were compared. Good separation between these distributions was found. The subject data were extremely negatively skewed, whereas the randomly generated data were normally distributed. Data are presented that display positive and negative predictive values, as well as sensitivity and specificity across ranges of prevalence and cut score. These data facilitate the identification of subjects who respond to the MCMI in a random manner so that their scores can be interpreted accordingly.     

Extension of caution indices to mixed-format tests

Tatsuoka suggested several extended caution indices and their standardized versions, and these have been used as person-fit statistics by various researchers. However, these indices are only defined for tests with dichotomous items. This paper extends two of the popular standardized extended caution indices for use with polytomous items and mixed-format tests. Two additional new person-fit statistics are obtained by applying the asymptotic standardization of person-fit statistics for mixed-format tests. Detailed simulations are then performed to compute the Type I error rate and power of the four new person-fit statistics. Two real data illustrations follow. The new person-fit statistics appear to be satisfactory tools for assessing person fit for polytomous items and mixed-format tests.     

An EM algorithm for fitting two-level structural equation models

Maximum likelihood is an important approach to analysis of two-level structural equation models. Different algorithms for this purpose have been available in the literature. In this paper, we present a new formulation of two-level structural equation models and develop an EM algorithm for fitting this formulation. This new formulation covers a variety of two-level structural equation models. As a result, the proposed EM algorithm is widely applicable in practice. A practical example illustrates the performance of the EM algorithm and the maximum likelihood statistic.We are thankful to the reviewers for their constructive comments that have led to significant improvement on the first version of this paper. Special thanks are due to the reviewer who suggested a comparison with the LISREL program in the saturated means model, and provided its setup and output. This work was supported by National Institute on Drug Abuse grants DA01070, DA00017, and a UNH 2002 Summer Faculty Fellowship.     

Mean Comparison: Manifest Variable Versus Latent Variable

Mean comparisons are of great importance in the application of statistics. Procedures for mean comparison with manifest variables have been well studied. However, few rigorous studies have been conducted on mean comparisons with latent variables, although the methodology has been widely used and documented. This paper studies the commonly used statistics in latent variable mean modeling and compares them with parallel manifest variable statistics. Our results indicate that, under certain conditions, the likelihood ratio and Wald statistics used for latent mean comparisons do not always have greater power than the Hotelling T² statistics used for manifest mean comparisons. The noncentrality parameter corresponding to the T² statistic can be much greater than those corresponding to the likelihood ratio and Wald statistics, which we find to be different from those provided in the literature. Under a fixed alternative hypothesis, our results also indicate that the likelihood ratio statistic can be stochastically much greater than the corresponding Wald statistic. The robustness property of each statistic is also explored when the model is misspecified or when data are nonnormally distributed. Recommendations and advice are provided for the use of each statistic. The research was supported by NSF grant DMS-0437167 and Grant DA01070 from the National Institute on Drug Abuse. We would like to thank three referees for suggestions that helped in improving the paper.     

优势分析方法及其应用

优势分析是近年来由Budescu等人新发展起来的一种确定多元回归方程中各预测变量相对重要性的方法。与传统方法相比,优势分析突出的特点是,全面比较了在由全模型所衍生出来的所有子模型情况下,各预测变量(X1,X…XP)在解释或预测标准变量у时,它们之间的相对重要性。本文从基本原理以及具体操作过程对这一新的统计分析方法进行了详细介绍。     

Detecting Differential Item Functioning Using Multiple-Group Cognitive Diagnosis Models

This study proposes a multiple-group cognitive diagnosis model to account for the fact that students in different groups may use distinct attributes or use the same attributes but in different manners (e.g., conjunctive, disjunctive, and compensatory) to solve problems. Based on the proposed model, this study systematically investigates the performance of the likelihood ratio (LR) test and Wald test in detecting differential item functioning (DIF). A forward anchor item search procedure was also proposed to identify a set of anchor items with invariant item parameters across groups. Results showed that the LR and Wald tests with the forward anchor item search algorithm produced better calibrated Type I error rates than the ordinary LR and Wald tests, especially when items were of low quality. A set of real data were also analyzed to illustrate the use of these DIF detection procedures.     

A scaled difference chi-square test statistic for moment structure analysis

A family of scaling corrections aimed to improve the chi-square approximation of goodness-of-fit test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, Satorra-Bentler's (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a test of the restrictions that a null model sayM ₀ implies on a less restricted oneM ₁. IfT ₀ andT ₁ denote the goodness-of-fit test statistics associated toM ₀ andM ₁, respectively, then typically the differenceT _d =T ₀−T ₁ is used as a chi-square test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the modelsM ₀ andM ₁. As in the case of the goodness-of-fit test, it is of interest to scale the statisticT _d in order to improve its chi-square approximation in realistic, that is, nonasymptotic and nonormal, applications. In a recent paper, Satorra (2000) shows that the difference between two SB scaled test statistics for overall model fit does not yield the correct SB scaled difference test statistic. Satorra developed an expression that permits scaling the difference test statistic, but his formula has some practical limitations, since it requires heavy computations that are not available in standard computer software. The purpose of the present paper is to provide an easy way to compute the scaled difference chi-square statistic from the scaled goodness-of-fit test statistics of modelsM ₀ andM ₁. A Monte Carlo study is provided to illustrate the performance of the competing statistics. This research was supported by the Spanish grants PB96-0300 and BEC2000-0983, and USPHS grants DA00017 and DA01070.     

程度函数及其可靠性

通过实验及理论分析,验证并给出了：１．程度函数ａ的可靠性θ的取值范围为：１／ｍ≤θ≤１／ｍ∑ｂｋｙｊｐ。θ值的大小与可靠性呈反向变化：ａ的可靠性大,θ值小,ａ的可靠性小θ值大,一般情况下的θ值,为θ的上界与下界之和的一半;２．采用集值统计模型和一般统计模型处理多级估量法多人次结果,所得到的程度函数平均数几乎相等,而且相关系数高达０．９７７．非常显著．分组数据计算、单一数据计算或全部数据统一计算的程度函数ａ及可靠性θ值也都相等．３．一般统计模型处理多级估量法多人次结果所得到的标准差σ,只反映各被试的程度函数之间的变异,与程度函数可靠性无关（相关系数０．００２４,非常不显著）．如果个人结果中按公式θ＝１／ｍ∑ｅｊ再计算可靠性θ值,然后再求其平均数及标准差,还可进一步了解可靠性θ值的变异情况．     

多级计分测验中基于残差统计量的被试拟合研究

本文提出一种多级计分项目下的个人拟合统计量R, 考察它在检测6种常见的异常作答模式(作弊、猜测、随机、粗心、创新作答、混合异常)下的表现, 并与标准化对数似然统计量l_zp进行比较。结果表明：(1) 在异常作答覆盖率较低并且异常作答类型为作弊和猜测时, R的检测率显著高于l_zp; (2) 随着测验长度和被试异常程度的增加, 两种统计量的检测率都会上升; (3) 在一些条件下, R与l_zp检测效果接近。实证数据分析进一步展示了R统计量的使用方法和过程, 结果也表明R统计量具有较好的应用前景。