mehdi molaei yasavoli; ali delavar; mohammad asgari; jalil Younesi; vahid rezaei tabar
Abstract
Efficiency and bias of parameter estimation is one of the most important psychometric issues in behavioral science measurements. The existence of various algorithms such as MHRM and their application in tests with missing data is one of the challenges in the field of item-response theory models. The ...
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Efficiency and bias of parameter estimation is one of the most important psychometric issues in behavioral science measurements. The existence of various algorithms such as MHRM and their application in tests with missing data is one of the challenges in the field of item-response theory models. The purpose of this study was to investigate the risk of MHRM algorithm in multidimensional models of item-response theory in multi-valued data by considering the mechanism and the amount of missing data. The research method was experimental using a multi-group post-test design. The study sample was created based on simulation studies under different conditions of independent variables in 27 cases with 100 replications for each. The model used was a multidimensional scaled response model and the studied parameters were the slope and threshold of the questions. R statistical software was used to generate and analyze the data. The results showed that MHRM algorithm has less estimated risk compared to EM and MCEM algorithms. The results also showed that there is a significant difference in the risk of slope and threshold parameters between three different mechanisms of missing data, but no significant difference was observed in relation to the independent variable of missing data. There was also a significant interaction between the type of algorithm and the missing mechanism, which indicated the optimal performance of the MHRM algorithm. Thus when this algorithm is used, the mean and variance of the MSE slope and threshold parameters in all three loss mechanisms also converge as they decrease. As a result, it can be said that the application of MHRM algorithm is essential in data with high data missing and types of missing. Therefore, researchers are advised to use the MHRM algorithm in data analysis with complex structure such as high data missing and various missing mechanisms
