Commentary on Coefficient Alpha: A Cautionary Tale

The general use of coefficient alpha to assess reliability should be discouraged on a number of grounds. The assumptions underlying coefficient alpha are unlikely to hold in practice, and violation of these assumptions can result in nontrivial negative or positive bias. Structural equation modeling was discussed as an informative process both to assess the assumptions underlying coefficient alpha and to estimate reliability     

托幼机构教育质量评价中评委偏差的多侧面Rasch分析

采用多侧面Rasch模型对28位评委在托幼机构教育质量评价中的评委偏差进行了分析。分析结果显示：28名评委评分宽严度差异显著;3名评委内部一致性较差,其余25名评委内部一致性较稳定;评委与评价班级的交互作用不显著,与评价项目的交互作用显著。研究结果表明MFRM可以对托幼机构教育质量评价的评委偏差进行个体层面的具体分析,从项目反应理论的视角为托幼机构教育质量评价的评委针对性培训、评估评委的合格性从而建立合格评委库等提供现代教育、心理测量学依据。     

D-optimal design for the Rasch counts model with multiple binary predictors

In this paper we derive optimal designs for the Rasch Poisson counts model and its extended version of the (generalized) negative binomial counts model incorporating several binary predictors for the difficulty parameter. To efficiently estimate the regression coefficients of the predictors, locally D-optimal designs are developed. After an introduction to the Rasch Poisson counts model and its extension, we will specify these models as particular generalized linear models. Based on this embedding, optimal designs for both models including several binary explanatory variables will be presented. Therefore, we will derive conditions on the effect sizes for certain designs to be locally D-optimal. Finally, it is pointed out that the results derived for the Rasch Poisson models can be applied for more general Poisson regression models which should receive more attention in future psychological research.     

The development of the Children's Empathic Attitudes Questionnaire using classical and Rasch analyses

Empathic responding is implicated in antisocial behaviors such as bullying, sexual offending, and violent crime. Identifying children and adolescents at risk for antisocial behavior and evaluating interventions designed to address problem behaviors require valid and reliable measures. Definitional controversies and limited measurement models have hindered measurement. This study describes the development and analysis of the Children's Empathic Attitudes Questionnaire (CEAQ) using both classical and modern techniques. Rasch analyses provided probabilistic results over large item and person groups, enabling meaningful inferences from patterns of responses at the construct level. Analyses of fifth through seventh graders' responses to the final version of the CEAQ provide support for its reliability, validity, and functionality. Four meaningful item clusters were identified, each reflecting more cognitively advanced empathic attitudes. These analyses suggest that the CEAQ provides a theoretically sound, hierarchically meaningful measure of empathic attitudes that will be useful in identification and intervention with children and adolescents at risk for antisocial behavior.     

A Note on “Constant Latent Odds-Ratios Models and the Mantel–Haenszel Null Hypothesis” Hessen, 2005

In a recent paper, Hessen (Psychometrika 70(3):497–516, 2005) introduces the class of constant latent odds-ratios models as an extension of the Rasch model for which the sum score is still the sufficient statistic for ability. In this paper the relation between both the general and the general parametric constant latent odds-ratios model and the Rasch model is considered.     

网络成瘾的潜在结构:连续的还是分类的?

变量的潜在结构是连续的还是分类的不应被随意指定,错误的设定可能导致不正确的结论。本研究的目的在于从实证的角度探索网络成瘾的潜在结构。研究采用了来自中国杭州的2511名初中生对Young网络成瘾量表的有效作答数据,比较了Rasch模型、潜在类别模型和混合Rasch模型与数据的拟合情况。结果表明:2个潜在类别的混合Rasch模型可以最好地反映网络成瘾的潜在结构,说明网络成瘾包含两个存在着质的差异的群体,并且每个群体内的个体间存在量的差异。研究进一步比较了基于混合Rasch模型的分类与传统划界分数分类的区别,结果表明:Young的网络成瘾标准可能具有很小的误判率和较高的漏判率;基于Young网络成瘾测验的修订需要考虑有针对性地增加部分题目。     

探测认知发展的非连续性:思路与方法

认知发展是连续性的还是非连续性的是发展心理学中一个重要理论问题.目前研究者逐渐认识到发展既有连续性的也有非连续性的,因而关键的问题是什么情况下是非连续性的,什么情况下是连续性的.该问题的回答与非连续性的检测标准有很大关系.本研究详细介绍了考察发展非连续性的三种思路和方法:量表图分析和多重任务法;Rasch模型分析和Saltus模型分析;突变理论的应用.最后,强调了影响发展非连续性问题探讨的一些因素.     

PUTTING PRESUPPOSITIONS ON THE TABLE: WHY THE FOUNDATIONS MATTER

Abstract. Over time scientists have developed an effective investigative process that includes the acceptance of particular basic presuppositions, methods, content, and theories. The deeply held presuppositions are the philosophical foundation of scientific thought and do much to define the field's worldview. These fundamental assumptions can be esoteric for many and can become a source of conflict when they are not commonly shared with other points of view. Such presuppositions affect the observations, the conclusions drawn, and the positions taken. Furthermore, in some cases presuppositions in science have undergone important shifts in meaning, causing an increasing dissonance. We argue that disputes in religion and science often are due to these very basic differences in philosophy that are held by members in the different communities. To better understand the nature of science and its differences with religious views, presuppositions rather than conclusions should be articulated and examined for validity and scope of application.     

Loss of Information in Estimating Item Parameters in Incomplete Designs

In this paper, the efficiency of conditional maximum likelihood (CML) and marginal maximum likelihood (MML) estimation of the item parameters of the Rasch model in incomplete designs is investigated. The use of the concept of F-information (Eggen, 2000) is generalized to incomplete testing designs. The scaled determinant of the F-information matrix is used as a scalar measure of information contained in a set of item parameters. In this paper, the relation between the normalization of the Rasch model and this determinant is clarified. It is shown that comparing estimation methods with the defined information efficiency is independent of the chosen normalization. The generalization of the method to other models than the Rasch model is discussed. In examples, information comparisons are conducted. It is found that for both CML and MML some information is lost in all incomplete designs compared to complete designs. A general result is that with increasing test booklet length the efficiency of an incomplete design, compared to a complete design, is increasing, as is the efficiency of CML compared to MML. The main difference between CML and MML is seen in the effect of the length of the test booklet. It will be demonstrated that with very small booklets, there is a substantial loss in information (about 35%) with CML estimation, while this loss is only about 10% in MML estimation. However, with increasing test length, the differences between CML and MML quickly disappear.     

Geometric representation of association between categories

Categories can be counted, rated, or ranked, but they cannot be measured. Likewise, persons or individuals can be counted, rated, or ranked, but they cannot be measured either. Nevertheless, psychology has realized early on that it can take an indirect road to measurement: What can be measured is the strength of association between categories in samples or populations, and what can be quantitatively compared are counts, ratings, or rankings made under different circumstances, or originating from different persons. The strong demand for quantitative analysis of categorical data has thus created a variety of statistical methods, with substantial contributions from psychometrics and sociometrics. What is the common basis of these methods dealing with categories? The basic element they share is that the sample space has a special geometry, in which categories (or persons) are point masses forming a simplex, while distributions of counts or profiles of ratings are centers of gravity, which are also point masses. Rankings form a discrete subset in the interior of the simplex, known as the permutation polytope, and paired comparisons form another subset on the edges of the simplex. Distances between point masses form the basic tool of analysis. The paper gives some history of major concepts, which naturally leads to a new concept: the shadow point. It is then shown how loglinear models, Luce and Rasch models, unfolding models, correspondence analysis and homogeneity analysis, forced classification and classification trees, as well as other models and methods, fit into this particular geometrical framework.