A general nonparametric classification method for multiple strategies in cognitive diagnostic assessment.

Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students' strengths and weaknesses in terms of cognitive skills learned and skills that need study. In practice, it is not uncommon that questions can often be solved using more than one strategy, which requires CDMs capable of accommodating multiple strategies. However, existing parametric multi-strategy CDMs need a large sample size to produce a reliable estimation of item parameters and examinees' proficiency class memberships, which obstructs their practical applications. This article proposes a general nonparametric multi-strategy classification method with promising classification accuracy in small samples for dichotomous response data. The method can accommodate different strategy selection approaches and different condensation rules. Simulation studies showed that the proposed method outperformed the parametric CDMs when sample sizes were small. A set of real data was analyzed as well to illustrate the application of the proposed method in practice.

Psychometric Properties and Measurement Invariance of the LIVES Daily Hassles Scale in Chinese Samples.

Daily hassles have a larger effect on our health and well-being than those major events in daily life. The present study aimed to evaluate the psychometric properties and measurement invariance of the LIVES Daily Hassles Scale (LIVES-DHS) in Chinese samples, which consisted of 815 people at work aged between 20 and 60 years old. The results of both Explanatory Factor Analysis and Confirmatory Factor Analysis showed that the five-factor model solution was better than other solutions, which supported the original structure of LIVES-DHS. The Cronbach's alpha coefficients of the five subdimensions varied between.721 and.818, with the entire scale of.920, and McDonald's ω values of the five subdimensions varied between.716 and.821, with the entire scale of.936. The results also showed the support for measurement invariance of the five-factor model across different groups, which is the first to offer evidence for configural, metric, scalar and strict invariance of LIVES-DHS across gender, age and educational groups.

A Generalized Diagnostic Classification Modeling Framework Integrating Differential Speediness: Advantages and Illustrations in Psychological and Educational Testing.

To advance the theoretical foundation of incorporating response times (RTs) into diagnostic classification models (DCMs), this study attempts to further derive, test and illustrate a generalized modeling framework (known as the JVRT-LCDM) that can simultaneously analyze response accuracy and differential speediness based on an existing method (Zhan et al., British Journal of Mathematical and Statistical Psychology, 71(2), 262-286, 2018). The JVRT-LCDM not only provides fine-grained diagnostic feedback without strict model constraints but also clarifies the specific speed trajectory of individuals. Moreover, some existing models from psychometric literatures are included in the JVRT-LCDM as special cases. The feasibility of the JVRT-LCDM is investigated via a Monte Carlo simulation study using a Bayesian estimation scheme, and two empirical datasets are then analyzed to illustrate the applicability of the JVRT-LCDM in practice. The results indicate that (1) as a generalized and flexible model, the JVRT-LCDM realizes high correct classification rates and accurate speed parameter recovery; (2) the JVRT-LCDM outperforms the existing models in terms of model-data fit, diagnostic consistency, and estimation of specific individuals in practical cognitive diagnosis assessments; and (3) the JVRT-LCDM provides reliable evidence for nonconstant speed modeling.