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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139851

Title:	An Item Response Tree Model with Not-All-Distinct End Nodes for Non-Response Modeling
Authors:	張育瑋 Chang, Yu-Wei Hsu, Nan-Jung Tsai, Rung-Ching
Contributors:	統計系
Keywords:	Laplace-approximated maximum likelihood estimation;item response theory tree model;non-response
Date:	2021-11
Issue Date:	2022-04-12
Abstract:	The non-response model in Knott et al. (1991, Statistician, 40, 217) can be represented as a tree model with one branch for response/non-response and another branch for correct/incorrect response, and each branch probability is characterized by an item response theory model. In the model, it is assumed that there is only one source of non-responses. However, in questionnaires or educational tests, non-responses might come from different sources, such as test speededness, inability to answer, lack of motivation, and sensitive questions. To better accommodate such more realistic underlying mechanisms, we propose a a tree model with four end nodes, not all distinct, for non-response modelling. The Laplace-approximated maximum likelihood estimation for the proposed model is suggested. The validation of the proposed estimation procedure and the advantage of the proposed model over traditional methods are demonstrated in simulations. For illustration, the methodologies are applied to data from the 2012 Programme for International Student Assessment (PISA). The analysis shows that the proposed tree model has a better fit to PISA data than other existing models, providing a useful tool to distinguish the sources of non-responses.
Relation:	British Journal of Mathematical and Statistical Psychology, Vol.74, No.3, pp.487-512
Data Type:	article
DOI link:	https://doi.org/10.1111/bmsp.12236
DOI:	10.1111/bmsp.12236
Appears in Collections:	[Department of Statistics] Periodical Articles

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