Multilevel multidimensional item response model with a multilevel latent covariate

2015 ◽  
Vol 68 (3) ◽  
pp. 410-433 ◽  
Author(s):  
Sun-Joo Cho ◽  
Brian Bottge
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongyu Huang ◽  
Zhengkun Hou ◽  
Xianhua Liu ◽  
Fengbin Liu ◽  
Yuefeng Wu

Objective. This study aims to offer a new approach for quantifying severity of traditional Chinese medicine pattern with multidimensional analysis methods. Methods. A scale and theoretical models were constructed based on the definition of liver stagnation spleen deficiency pattern. Clinical data of 344 IBS-D patients from a cross-sectional study was used for feature validation of the model. Confirmatory factor analysis was used for evaluating the models. Also, multidimensional item response model was used for assessing multidimensional psychometric properties of the scale. Results. Detecting two latent traits, the Cronbach’s alpha of the 9-item scale was 0.745. Multidimensional model was evaluated with significant goodness of fit indices while the unidimensional model was rejected. The multidimensional item response model showed all the items had adequate discrimination. Parameters presented adequate explanation regarding mental syndromes having high factor loading on the liver stagnation factor and abdominal discomfort syndromes highly related to the spleen deficiency factor. Test information function showed that scale demonstrated the highest discrimination power among patients with moderate to high level of severity. Conclusions. The application of the multidimensional analysis methods on the basis of theoretical model construction provides a useful and rational approach for quantifying the severity of traditional Chinese medicine patterns.


2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Yanyan Sheng ◽  
Todd C. Headrick

Current procedures for estimating compensatory multidimensional item response theory (MIRT) models using Markov chain Monte Carlo (MCMC) techniques are inadequate in that they do not directly model the interrelationship between latent traits. This limits the implementation of the model in various applications and further prevents the development of other types of IRT models that offer advantages not realized in existing models. In view of this, an MCMC algorithm is proposed for MIRT models so that the actual latent structure is directly modeled. It is demonstrated that the algorithm performs well in modeling parameters as well as intertrait correlations and that the MIRT model can be used to explore the relative importance of a latent trait in answering each test item.


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