Rasch analysis and item reduction of the hypomanic personality scale

The aim of the current study was to reduce the number of items in the 48-item hypomanic personality scale (HPS) and determine whether a unidimensional scale of the hypomanic trait could be derived. Previously collected HPS data from University students (n = 318) were applied to the Rasch model (one-parameter item response theory). Overall scale and individual item fit statistics were used to judge fit to the model and item maps employed to determine coverage of the trait. Cronbach’s Alpha and correlations with other questionnaires pre- and post-item reduction were evaluated. Rasch analysis indicated that the original HPS was not unidimensional, had significant redundancy and differential item functioning by age and gender. An iterative process of item reduction produced a 20-item HPS (HPS-20) that retained the concepts of the original HPS and had excellent fit to the Rasch model (χ² p = 0.27). Unidimensionality of the HPS-20 was confirmed. The traditional psychometric properties of the HPS-20 and coverage of the underlying hypomanic construct were similar to the original. It was possible to derive a unidimensional measure of the hypomanic trait. Further use of the HPS-20 is encouraged as it may increase understanding of the risk factors for affective disorders.     

项目反应理论中模型-资料拟合检验常用统计量

项目反应理论(IRT)模型依据项目与被试的特征预测被试的作答表现, 是常用的心理测量模型。但IRT的有效运用依赖于所选用IRT模型与实际数据资料相符合的程度(即模型?资料拟合度, goodness of fit)。只有当所采用IRT分析模型与实际数据资料拟合较好时, IRT的优点和功能才能真正发挥出来(Orlando & Thissen, 2000)。而当所采用IRT模型与资料不拟合或选择了错误的模型, 则会导致如参数估计、测验等值及项目功能差异分析等具有较大误差(Kang, Cohen & Sung, 2009), 给实际工作带来不良影响。因此, 在使用IRT分析时, 应首先充分考察及检验所选用模型与实际数据是否相匹配/相拟合(McKinley & Mills, 1985)。IRT领域中常用模型?资料拟合检验统计量可从项目拟合、测验拟合两个角度进行阐述并比较, 这是心理、教育测量领域的重要主题, 也是测验分析过程中较易忽视的环节, 目前还未见此类公开发表的文章。未来的研究可以在各统计量的实证比较研究以及在认知诊断领域的拓展方面有所发展。     

Secondary distinctiveness effects: Orthographic distinctiveness and bizarreness effects make independent contributions to memory performance

The secondary distinctiveness effect means that items that are unusual compared to one's general knowledge stored in permanent memory are remembered better than common items. This research studied two forms of secondary‐distinctiveness‐based effects in conjunction: the bizarreness effect and the orthographic distinctiveness (OD) effect. More specifically, an experiment investigated in young adults a possible additive effect of bizarreness and OD effects in free recall performance. Results revealed that in young adults these two secondary‐distinctiveness‐based effects appear to be largely independent and can complement each other to enhance performance. Findings are discussed in light of current distinctiveness theory.     

Do Raven's Colored Progressive Matrices function in the same way in typical and clinical populations? Insights from the intellectual disability field

Standardized tests are used widely in comparative studies of clinical populations, either as dependent or control variables. Yet, one cannot always be sure that the test items measure the same constructs in the groups under study. In the present work, 460 participants with intellectual disability of undifferentiated etiology and 488 typical children were tested using Raven's Colored Progressive Matrices (RCPM). Data were analyzed using binomial logistic regression modeling designed to detect differential item functioning (DIF). Results showed that 12 items out of 36 function differentially between the two groups, but only 2 items exhibit at least moderate DIF. Thus, a very large majority of the items have identical discriminative power and difficulty levels across the two groups. It is concluded that RCPM can be used with confidence in studies comparing participants with and without intellectual disability. In addition, it is suggested that methods for investigating internal bias of tests used in cross-cultural, cross-linguistic or cross-gender comparisons should also be regularly employed in studies of clinical populations, particularly in the field of developmental disability, to show the absence of systematic measurement error (i.e. DIF) affecting item responses.     

Brief assessment of schizotypy: Developing short forms of the Wisconsin Schizotypy Scales

The Wisconsin Schizotypy Scales—the Perceptual Aberration, Magical Ideation, Physical Anhedonia, and Revised Social Anhedonia Scales—have been used extensively since their development in the 1970s and 1980s. Based on psychometric analyses using item response theory, the present work presents 15-item short forms of each scale. In addition to being briefer, the short forms omit items with high differential item functioning. Based on data from a sample of young adults (n = 1144), the short forms have strong internal consistency, and they mirror effects found for the longer scales. They thus appear to be a good option for researchers interested in the brief assessment of schizotypic traits. The items are listed in an Appendix A.     

Gleaning Information for Cognitive Operations from Don’t Know Responses in Cognitive and Noncognitive Assessments

The Don’t Know (DK) response – taking the form of an omitted response or not-reached at the end of a cognitive test, or explicitly presented as a response option in a social survey – contains important information that is often overlooked. Direct psychometric modeling efforts for DK responses are few and far between. In this article, the linear logistic test model (LLTM) is proposed for delineating the impacts of cognitive operations for a test that contains DK responses. We assume that the DK response is a valid response. The assumption is reasonable for many situations, including low-stakes cognitive tests and attitudinal assessments. By extracting information embedded in the DK response, the method shows how DK can inform the latent construct of interest and the cognitive operations underlying the response to stimuli. Using a proven recoding scheme, the LLTM could be implemented through commonly used programs such as PROC GLIMMIX. Two simulation experiments to evaluate how well the parameters can be recovered were conducted. In addition, two real data examples, from a noncognitive test of health belief assessment and a cognitive test of knowledge in diabetes, are also presented as case studies to illustrate the LLTM for DK response.     

Attractive memory: High destination memory for attractive faces

Research has demonstrated that destination memory (i.e., the ability to remember to whom information was previously told) can be influenced by characteristics (e.g., emotional expressions and age) of the destination. Building on this literature, we investigated whether destination memory can be influenced by the attractiveness of the destination. We invited participants to give information on attractive faces, unattractive faces, or neither‐attractive‐nor‐unattractive faces. On a recognition test, they were invited to decide to whom each piece of information had been previously told. Results demonstrated higher destination memory (1) for attractive faces than for neither‐attractive‐nor‐unattractive faces, and (2) for unattractive faces than for neither‐attractive‐nor‐unattractive faces. We attribute the higher destination memory for attractive and unattractive destinations to their distinctiveness compared with neutrally attractive destinations. We also provide some attentional explanations for the high memory for attractive and unattractive destinations.     

在GLMM框架下统一GT与IRT

本文首次提出使用广义线性混合模型(Generalized Linear Mixed Model, GLMM)对概化理论(GT)和项目反应理论(IRT)进行统合,即在一次统计中就能同时获得GT和IRT所需要的估计结果。模拟研究结果显示：相比于传统的GT方差分量估计方法——期望均值平方(Expected Mean Squares, EMS),GLMM可以获得更准确的方差分量、G系数和Φ系数,而且GLMM获得的题目难度参数估计精度优于传统Rasch模型。实证研究展示GLMM在实际心理测量数据分析中的应用。     

Incorporating Mobility in Growth Modeling for Multilevel and Longitudinal Item Response Data

Multilevel data often cannot be represented by the strict form of hierarchy typically assumed in multilevel modeling. A common example is the case in which subjects change their group membership in longitudinal studies (e.g., students transfer schools; employees transition between different departments). In this study, cross-classified and multiple membership models for multilevel and longitudinal item response data (CCMM-MLIRD) are developed to incorporate such mobility, focusing on students' school change in large-scale longitudinal studies. Furthermore, we investigate the effect of incorrectly modeling school membership in the analysis of multilevel and longitudinal item response data. Two types of school mobility are described, and corresponding models are specified. Results of the simulation studies suggested that appropriate modeling of the two types of school mobility using the CCMM-MLIRD yielded good recovery of the parameters and improvement over models that did not incorporate mobility properly. In addition, the consequences of incorrectly modeling the school effects on the variance estimates of the random effects and the standard errors of the fixed effects depended upon mobility patterns and model specifications. Two sets of large-scale longitudinal data are analyzed to illustrate applications of the CCMM-MLIRD for each type of school mobility.