Simulation study of unlimited multiple scoring
碩士 === 國立臺中教育大學 === 教育測驗統計研究所 === 102 === The purpose of this research is to compare binary structure and multiple scoring with unlimited multiple scoring. Traditional binary structure is a answer mode of second election, application convenient and clear. In this research, 4-points multiple scoring、...
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ndltd-TW-102NTCT06290412019-05-15T21:23:13Z http://ndltd.ncl.edu.tw/handle/3z93px Simulation study of unlimited multiple scoring 無限多點計分模擬研究 Ming-Shun Lee 李明勳 碩士 國立臺中教育大學 教育測驗統計研究所 102 The purpose of this research is to compare binary structure and multiple scoring with unlimited multiple scoring. Traditional binary structure is a answer mode of second election, application convenient and clear. In this research, 4-points multiple scoring、5-points multiple scoring and 6-points multiple scoring which more commonly used in the multivariate, and the newly developed unlimited multiple scoring was planted with the "brace" feature scoring. Use the Monte Carlo simulation to simulate the data sets of three different related levels, each relevance type of data set is subdivided into three different sizes of data samples, various of scoring estimation subjects ability true ability, and calculated Mean Square Error values. The results are as follows: 1. When the number of different samples, various methods obtain mean square error values are different from each other the difference. 2. When the number of different questions, can't change the comparison result between scoring. 3. When the number of different K-value in cross-validation method, and will not affect the result of the comparison between the scoring. 4. In data of different of correlation levels, compared to binary structure and multiple scoring, unlimited multiple scoring ability to get closer to the true value of subjects. Guey-Shya Chen 陳桂霞 2014 學位論文 ; thesis 83 zh-TW |
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碩士 === 國立臺中教育大學 === 教育測驗統計研究所 === 102 === The purpose of this research is to compare binary structure and multiple scoring with unlimited multiple scoring. Traditional binary structure is a answer mode of second election, application convenient and clear. In this research, 4-points multiple scoring、5-points multiple scoring and 6-points multiple scoring which more commonly used in the multivariate, and the newly developed unlimited multiple scoring was planted with the "brace" feature scoring. Use the Monte Carlo simulation to simulate the data sets of three different related levels, each relevance type of data set is subdivided into three different sizes of data samples, various of scoring estimation subjects ability true ability, and calculated Mean Square Error values.
The results are as follows:
1. When the number of different samples, various methods obtain mean square error values are different from each other the difference.
2. When the number of different questions, can't change the comparison result between scoring.
3. When the number of different K-value in cross-validation method, and will not affect the result of the comparison between the scoring.
4. In data of different of correlation levels, compared to binary structure and multiple scoring, unlimited multiple scoring ability to get closer to the true value of subjects.
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author2 |
Guey-Shya Chen |
author_facet |
Guey-Shya Chen Ming-Shun Lee 李明勳 |
author |
Ming-Shun Lee 李明勳 |
spellingShingle |
Ming-Shun Lee 李明勳 Simulation study of unlimited multiple scoring |
author_sort |
Ming-Shun Lee |
title |
Simulation study of unlimited multiple scoring |
title_short |
Simulation study of unlimited multiple scoring |
title_full |
Simulation study of unlimited multiple scoring |
title_fullStr |
Simulation study of unlimited multiple scoring |
title_full_unstemmed |
Simulation study of unlimited multiple scoring |
title_sort |
simulation study of unlimited multiple scoring |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/3z93px |
work_keys_str_mv |
AT mingshunlee simulationstudyofunlimitedmultiplescoring AT lǐmíngxūn simulationstudyofunlimitedmultiplescoring AT mingshunlee wúxiànduōdiǎnjìfēnmónǐyánjiū AT lǐmíngxūn wúxiànduōdiǎnjìfēnmónǐyánjiū |
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