Psychometric Models with Leaving Blanks in Vocabulary Levels Test

• Main Authors: Chen, Chien-Yu, 陳建宇
• Other Authors: 呂翠珊
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/74yg7r

碩士 === 國立臺灣師範大學 === 數學系 === 106 === Vocabulary Level Test (VLT) is an English test with ten clusters, each containing three items and six options, to check the students’ vocabulary level. A VLT-sequence model (VSM) has been proposed to handle the dependence among items within each cluster by considering the response order of the examinees in each cluster. However, blanks are treated as the same as the incorrect responses, ignoring the fact that, unlike incorrect responses, they do not make changes to the number of remaining set of items within a cluster. In this thesis, we try to distinguish leaving blanks from giving incorrect responses and propose the VLT-sequence model with leaving blanks (VSMB) by adding a blank parameter to each item. Through simulations, we verify the validity of using the marginal maximum likelihood estimation for the proposed VSMB model, as well as adopting the limited information method to check model fit. The simulation results suggest that the VSM model fit the VSMB data reasonably well with the acceptable estimation errors and the low proportions of rejection. While analyzing the 3000-level VLT data, we also find that the VSM model appears to provide the most parsimonious fit, in comparison to the proposed VSMB model and the two-parameter item response model. Moreover, the analysis of empirical data also reveals that the single blank parameter in the VSMB model does not seem to well capture the mechanism for leaving blanks while observing that some examinees with more than 70% of correct responses on the test leave at least one whole cluster with easier items blank. That is, other factors such as motivation might play a role too.