การวิเคราะห์ข้อคำถามในแบบวัดความวิตกกังวลในการสอบคณิตศาสตร์ : การเปรียบเทียบระหว่างไฮราซิคอลลิเนียร์โมเดล พาเซียลเครดิตโมเดลและเกรดเรสพอนส์โมเดล - ศิริรัตน์ สุคันธพฤกษ์ (จุฬาลงกรณ์มหาวิทยาลัย, 2010)
This research had three objectives: 1) to analyze items in Mathematics Test Anxiety Scale using Hierarchical Linear Model by the estimation of item parameters, person parameters and to examine the effects of person characteristic variables on the probability of item correct response 2) to compare and study correlation of the estimation of item and person parameters among Hierarchical Linear Model (HLM), Partial Credit Model (PCM) and Graded Response Model (GRM) 3) to compare the Differential Item Functioning (DIF) detecting among Hierarchical Linear Model, Partial Credit Model and Graded Response Model The samples were 1,715 Mathayomsuksa 6 students who were studying in Math-science classes in 2009 academic year. Samples were drawn from 29 schools under the jurisdiction of the Pranakhon Sri Ayutthaya Educational Service Area Office 1 and 2, the Angthong Educational Service Area Office, and the Nonthaburi Educational Service Area Office using cluster random sampling technique. Data were collected though 7 instruments: The Mathematics Test Anxiety Scale, The Perception of Parent Expectation Scale, The Social Supporting Scale, the Test Preparing Scale, The Strategy Testing Scale, The Attitude Scale towards Mathematics, and The Motivation Scale towards Mathematics. There were three steps of data analysis: 1) the estimation of item and person parameters, and the study of effects of person characteristics on the probability of correct response from HLM using HLM program 2) the estimation of item and person parameters from PCM and GRM using PARSCALE, then the comparison with HLM 3) the comparison of results of differential item functioning analysis among HLM using HLM program and PCM and GRM using PARSCALE. The major findings were:1. The item analysis by HLM using HLM program estimated difficulty parameters consistently. The mean of standard error of estimation was 0.074. This method also estimated person parameters in the residual file. The results of student’s characteristic study also found that gender, perception of parent expectation and attitude towards Mathematics significantly affected the averaged probability of correct response at .05 level. 2. The correlations between difficulty parameter estimation derived from HLM and PCM were .899 – .929, between HLM and GRM was .996 and between PCM and GRM was .908 - .918. The person parameter estimation using the three models yielded that person parameter correlation between HLM and PCM was .390, person parameter correlation between HLM and GRM was .437 and person parameter correlation between PCM and GRM was .798. 3. The results from differential item functioning (DIF) examination detected 7 common items from 39 items between HLM using HLM and PCM using PARSCALE (17.94%), 8 common items from 39 items between HLM using HLM and GRM using PARSCALE (23.07%) and 6 common items from 39 items between PCM and GRM using PARSCALE (15.38%)