Two-Stage maximum likelihood estimation in the misspecified restricted latent class model

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.     

No Relationship Between Joint Position Sense and Force Sense at the Shoulder

In practice, a single test is used to quantify an individual's proprioception. Previous studies have not found a correlation between joint position sense (JPS) and force sense (FS), which are submodalities of proprioception. The purpose of the present study is to determine if root mean square (RMS) error in JPS and FS are related at the shoulder, controlling for external load and elevation angle. Active shoulder angle and force reproduction protocols were performed. No correlation was found between JPS and FS (r = –.019, p = .941) nor were any individual angle and load combinations significant. The main effect for angle in JPS was significant (p < .001). Follow-up contrast demonstrated a significant (p < .001) decrease in RMS error with increased elevation. A significant load by angle interaction was found for FS (p = .014). Follow-up simple effects tests by angle demonstrated RMS error decreased with load at 50° and 70° but not at 90°. By load, RMS error only decreased for 120% between 50° and 90°. JPS and FS demonstrate different behavior with load and angle. This differing behavior is more likely responsible for the lack of correlation than angle and load differences in JPS and FS protocols.     

Polytomous multilevel testlet models for testlet‐based assessments with complex sampling designs

A pplications of standard item response theory models assume local independence of items and persons. This paper presents polytomous multilevel testlet models for dual dependence due to item and person clustering in testlet‐based assessments with clustered samples. Simulation and survey data were analysed with a multilevel partial credit testlet model. This model was compared with three alternative models – a testlet partial credit model (PCM), multilevel PCM, and PCM – in terms of model parameter estimation. The results indicated that the deviance information criterion was the fit index that always correctly identified the true multilevel testlet model based on the quantified evidence in model selection, while the Akaike and Bayesian information criteria could not identify the true model. In general, the estimation model and the magnitude of item and person clustering impacted the estimation accuracy of ability parameters, while only the estimation model and the magnitude of item clustering affected the item parameter estimation accuracy. Furthermore, ignoring item clustering effects produced higher total errors in item parameter estimates but did not have much impact on the accuracy of ability parameter estimates, while ignoring person clustering effects yielded higher total errors in ability parameter estimates but did not have much effect on the accuracy of item parameter estimates. When both clustering effects were ignored in the PCM, item and ability parameter estimation accuracy was reduced.     

Matrix training of preliteracy skills with preschoolers with autism

Matrix training is a generative approach to instruction in which words are arranged in a matrix so that some multiword phrases are taught and others emerge without direct teaching. We taught 4 preschoolers with autism to follow instructions to perform action-picture combinations (e.g., circle the pepper, underline the deer). Each matrix contained 6 actions on 1 axis and 6 pictures on the other axis. We used most-to-least prompting to train the instructions along the diagonal of each matrix and probed the untrained combinations. For 2 participants, untrained responding emerged after the minimum amount of training. The other 2 participants required further training before untrained combinations emerged. At the end of the study, 3 of the 4 participants performed the trained actions with previously known pictures, letters, and numbers. This study demonstrated that matrix training is an efficient approach to teaching language and literacy skills to children with autism.     

Estimation and visualization of confusability matrices from adaptive measurement data

We present a simple but effective method based on Luce’s choice axiom [Luce, R.D. (1959). Individual choice behavior: A theoretical analysis. New York: John Wiley & Sons] for consistent estimation of the pairwise confusabilities of items in a multiple-choice recognition task with arbitrarily chosen choice-sets. The method combines the exact (non-asymptotic) Bayesian way of assessing uncertainty with the unbiasedness emphasized in the classical frequentist approach.We apply the method to data collected using an adaptive computer game designed for prevention of reading disability. A player’s estimated confusability of phonemes (or more accurately, phoneme-grapheme connections) and larger units of language is visualized in an easily understood way with color cues and explicit indication of the accuracy of the estimates. Visualization of learning-related changes in the player’s performance is considered.The empirical validity of the choice axiom is evaluated using the game data itself. The axiom appears to hold reasonably well although a small systematic violation is observable for the smallest choice-set sizes.     

一种多级评分的广义认知诊断模型

认知诊断是近些年教育测量研究中的热点,大多数的认知诊断模型仅适用于0～1评分的情况.本文提出一种有多个潜变量多个滑动参数的多级评分认知诊断模型——GP-D1NA,只要由评分标准和知识状态能确定理想反应模式,就可以利用此方法进行认知诊断分析.在该方法中,我们给出项目滑动矩阵的概念,将被试的观测得分均看成由某个理想得分的滑动,并采用EM算法估计滑动矩阵.在模拟研究中,采用每掌握一个属性得1分的评分标准,结果表明线性型、收敛型、发散型、无结构型和独立型五种属性层级结构均有较高的判准率.     

认知诊断分类中心的确定

期望反应模式是认知诊断分类的类中心,确定了诊断范围中属性及其层级关系后,类中心的数目由测验Q矩阵决定.类中心应该是完备的,即理论上有多少个知识状态就应该有多少个类中心,这涉及评分方式、Q矩阵设计、Q矩阵评价与修改、认知诊断测验如何实施等问题的讨论.重点给出多级评分认知诊断测验不同属性层级对应的完备Q矩阵设计的例子和罗列了Wang等人（2013）的Q矩阵修改的方法.     

Intuitive Covariation Assessment of the Illusory Correlation

Most of the research concerned with the illusory correlation is modeled after the seminal work of D. L. Hamilton and R. K. Gifford (1976). However, S. A. Haslam and C. McGarty (1994) have voiced concerns over the dependent measures used within this paradigm. Therefore, in this study, the authors tested a new dependent variable that has high face validity. This measure was modeled after the work of J. R. McGahan and R. Wight (1989) and consisted of a set of propositional statements representing either the illusory correlation, the contingency opposite the illusory correlation, or the noncontingency. A second purpose of this study was to validate other studies that have used dependent measures modeled after the work of J. R. McGahan and R. Wight (1989). Demonstrating that this measure can be used to detect a well-documented phenomenon (i.e., the illusory correlation) would strengthen the results and conclusions from other studies. To this end, results from 2 experiments indicate that this measure does provide a valid alternative to those measures that are commonly used in illusory correlation studies. The results thereby give credence to other studies that have used similar dependent measures.     

Predictors of quality of life enjoyment and satisfaction in individuals living with HIV and AIDS in a resource-constrained setting in South Africa

The burden of HIV disease is concentrated in sub-Saharan Africa, particularly in South Africa (SA). Whilst there have been many studies conducted on the biomedical and socio-psychological aspects of HIV and AIDS, insufficient attention has been paid to the quality of life of those infected with the virus. The primary purpose of the study was to determine the predictors of quality of life enjoyment and satisfaction (Q-LES) among individuals infected with HIV. A battery of questionnaires, which included the Hospital Anxiety and Depression Scale (HADS) and the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q), were administered to 121 participants. Data was analysed using SPSS. Of the total sample (n = 121), 74% were females. The study found that a large proportion (49.5%) of the sample within the age group 25–49 years old experienced psychological distress. Those who were not on antiretroviral treatment (ART) were significantly affected (66%). Psychological distress was significantly prevalent among HIV positive individuals and also the strongest predictor of Q-LES among these individuals.