臺灣博碩士論文加值系統

許多國際上的大型測驗，多採用可能值方法來進行群體能力參數的估計。而可能值的資料型態，亦可讓資料分析者進行統計特性的描述。此外，一般大型測驗所評量的範圍都涵蓋了不同的認知向及難度，無法由單一受試者於短期間內全部完成，測驗題目都會進行不同的等化設計以減輕受試者負擔並達成測驗的目的。

本研究係各以定錨不等組（non-equivalent groups with anchor test design, NEAT）及平衡不完全區塊（balanced incomplete block design, BIB）的垂直等化設計，並以可能值方法、納入背景變項的期望後驗法、期望後驗法及最大概度估計法等各種方法別進行個體能力及群體能力的平均數與標準差的估計，其主要的目的在於探討可能值方法及其它估計法在群體參數回復的效果。

本研究結果發現在各種不同的垂直等化設計下，不管是個體能力參數的估計，或是群體能力平均數與標準差的回復上，納入背景變項估計方法皆有較好的估計效果。尤其在群體能力標準差的回復上，可能值方法的估計結果遠優於各種估計方法。

The purpose of this paper is to explore the performance of plausible values method under BIB and NEAT designs for vertical equating based on simulated data. The major focus of large-scale assessments is always on the population statistics, such as means and standard deviations, and the plausible value method is usually used to estimate the population parameters. For large-scale assessments the spectrum of subject matter is usually wide, but the testing time is short. Therefore, in order to cover the proficiency domain sufficiently, multiple booklets are used. Balanced incomplete block design (BIB) and non-equivalent groups with anchor test design (NEAT) are two popular test equating methods for this condition. The experimental results show that the estimating method based on plausible values estimate better than that of other methods in vertical equating designs, and as the test length increase, population parameters (mean and standard deviation) are well estimated. In these experimental situations, the estimations of population parameters are not affected by sample size (16128 and 10920). Both linking designs, BIB and NEAT, can lead to more precision estimates by using plausible value method.

摘要 I
Abstract II
目錄 III
表目錄 V
圖目錄 VI
第一章　緒論 1
第一節　研究動機 1
第二節　研究目的與待答問題 3
第三節　名詞解釋 3
第二章　文獻探討 7
第一節　單向度試題反應理論 7
第二節　參數估計方法 8
第三節　可能值方法 13
第四節　測驗等化設計 16
第三章　研究方法 21
第一節　研究步驟 21
第二節　測驗等化設計 23
第三節　模擬條件與估計方法設定 25
第四節　研究工具 28
第五節　評估準則 29
第四章　研究結果與討論 31
第一節 NEAT等化設計估計結果 31
第二節 BIB等化設計估計結果 38
第三節 二種等化設計方法之比較 44
第五章 結論與建議 49
第一節 結論 49
第二節 建議 50
參考文獻 53
中文部分 53
英文部分 54
附錄一 NEAT設計個體能力值不同估計方法之RMSE 59
附錄二 BIB設計個體能力值不同估計方法之RMSE 63
附錄三 NEAT設計群體能力平均數不同估計方法之RMSE 66
附錄四 BIB設計群體能力平均數不同估計方法之RMSE 71
附錄五 NEAT設計群體能力標準差不同估計方法之RMSE 74
附錄六 BIB設計群體能力標準差不同估計方法之RMSE 78

中文部分
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王暄博（2006）。BIB與NEAT設計之水平及垂直等化效果比較。未出版之碩士論文，臺中教育大學教育測驗統計研究所，臺中市。
余民寧（2009），試題反應理論（IRT）及其應用（一版）。臺北市，心理出版社股份有限公司。
洪碧霞、林素微、林娟如（2006）。認知複雜度分析架構對TASA-MAT六年級線上測驗試題難度的解釋力。教育研究與發展期刊，2（4），69-86。
郭伯臣、曾建銘、吳慧珉主編(2012)。大型標準化測驗建置流程應用於TASA之研究。新北市：國家教育研究院。
曾玉琳、王暄博、郭伯臣、許天維（2006）。不同BIB設計對測驗等化的影響。測驗統計年刊，13（2），209-229。臺中市：國立臺中教育大學。


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