Cognitive diagnosis models (CDMs) have been developed to evaluate the mastery status of individuals with respect to a set of defined attributes or skills that are measured through testing. When individuals are repeatedly administered a cognitive diagnosis test, a new class of multilevel CDMs is required to assess the changes in their attributes and simultaneously estimate the model parameters from the different measurements. In this study, the most general CDM of the generalized deterministic input, noisy “and” gate (G‐DINA) model was extended to a multilevel higher order CDM by embedding a multilevel structure into higher order latent traits. A series of simulations based on diverse factors was conducted to assess the quality of the parameter estimation. The results demonstrate that the model parameters can be recovered fairly well and attribute mastery can be precisely estimated if the sample size is large and the test is sufficiently long. The range of the location parameters had opposing effects on the recovery of the item and person parameters. Ignoring the multilevel structure in the data by fitting a single‐level G‐DINA model decreased the attribute classification accuracy and the precision of latent trait estimation. The number of measurement occasions had a substantial impact on latent trait estimation. Satisfactory model and person parameter recoveries could be achieved even when assumptions of the measurement invariance of the model parameters over time were violated. A longitudinal basic ability assessment is outlined to demonstrate the application of the new models.

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Respondents are often requested to provide a response to Likert-type or rating-scale items during the assessment of attitude, interest, and personality to measure a variety of latent traits. Extreme response style (ERS), which is defined as a consistent and systematic tendency of a person to locate on a limited number of available rating-scale options, may distort the test validity. Several latent trait models have been proposed to address ERS, but all these models have limitations. Mixture random-effect item response theory (IRT) models for ERS are developed in this study to simultaneously identify the mixtures of latent classes from different ERS levels and detect the possible differential functioning items that result from different latent mixtures. The model parameters can be recovered fairly well in a series of simulations that use Bayesian estimation with the WinBUGS program. In addition, the model parameters in the developed models can be used to identify items that are likely to elicit ERS. The results show that a long test and large sample can improve the parameter estimation process; the precision of the parameter estimates increases with the number of response options, and the model parameter estimation outperforms the person parameter estimation. Ignoring the mixtures and ERS results in substantial rank-order changes in the target latent trait and a reduced classification accuracy of the response styles. An empirical survey of emotional intelligence in college students is presented to demonstrate the applications and implications of the new models.

The DINA (deterministic input, noisy, and gate) model has been widely used in cognitive diagnosis tests and in the process of test development. The outcomes known as slip and guess are included in the DINA model function representing the responses to the items. This study aimed to extend the DINA model by using the random-effect approach to allow examinees to have different probabilities of slipping and guessing. Two extensions of the DINA model were developed and tested to represent the random components of slipping and guessing. The first model assumed that a random variable can be incorporated in the slipping parameters to allow examinees to have different levels of caution. The second model assumed that the examinees' ability may increase the probability of a correct response if they have not mastered all of the required attributes of an item. The results of a series of simulations based on Markov chain Monte Carlo methods showed that the model parameters and attribute-mastery profiles can be recovered relatively accurately from the generating models and that neglect of the random effects produces biases in parameter estimation. Finally, a fraction subtraction test was used as an empirical example to demonstrate the application of the new models.

Comorbidity of chronic pain and major depression disorder (MDD) are common diseases. However, the mechanisms of electroacupuncture (EA) and the responses of N-methyl-D-aspartate receptors in the brain remain unclear. Three injections of complete Freund's adjuvant (CFA) were administered to induce chronic inflammatory pain (CIP). EA was then performed once every other day from days 14 to 28. Behavior tests of chronic pain and depression were evaluated to make sure of the successful induction of comorbidity. We used Western blotting to analyze brain tissue from the prefrontal cortex (PFC), hippocampus, and hypothalamus for levels of phosphorylated N-methyl-D-aspartate receptor subunit 1 (pNR1), NR1, pNR2B, NR2B, and calcium/calmodulin-dependent protein kinase type II alpha isoform (pCaMKIIα). The mechanical hyperalgesia, thermal hyperalgesia, and depression were observed in the CIP group. Furthermore, decreased levels of N-methyl-D-aspartate receptors (NMDARs) were also noted. Not Sham EA but EA reversed chronic pain and depression as well as the decreased levels of NMDA in the signaling pathway. The CFA injections successfully induced a significant comorbidity model. EA treated the comorbidity by upregulating the NMDA signaling pathway in the PFC, hippocampus, and hypothalamus. Our results indicated significant mechanisms of comorbidity of chronic pain and MDD and EA-analgesia that involves the regulation of the NMDAR signaling pathway. These findings may be relevant to the evaluation and treatment of comorbidity of chronic pain and MDD.