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研究生(外文):LU, BING-JING
論文名稱(外文):The Effects of Different Imputation Methods on Missing Data for Uniform DIF Analysis
指導教授(外文):SU, YA-HUI
外文關鍵詞:missing dataDIFmultiple imputationMantel-Haenszel statisticLord’s chi-square
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測驗公平性是測驗施測基礎,也是檢視測驗品質的重要指標,因此差異試題功能(differential item functioning, DIF)的檢測就顯得格外重要。當進行DIF檢測時,如果資料部分遺失,DIF檢測方法便會受到影響;如果遺失比率過高,甚至會使DIF檢測無法運作,因此如何處理遺失值以確保DIF檢測結果是可信賴的,這是研究者們所必須面對的重要課題;再者,由於不同遺失值處理方法能夠回復資料的狀態也不盡相同,後續也可能影響DIF檢測結果。Finch (2011)指出,採用多重插補法處理遺失資料具有優勢,然而該研究只考慮第一道試題遺失的情境,未考慮DIF試題比率、遺失比率等因素對於DIF檢測結果的影響,故本研究目的欲瞭解這些因素如何影響遺失值對於DIF檢測的結果,期能為資料處理時可能會遇到的問題提供較好的處理方法。本研究分為兩部分探討,研究一是探討不同遺失值處理方法在面對不同遺失比率、遺失機制、DIF比率和DIF量時資料回復的效果,研究二是探討經不同遺失處理方法處理後,比較不同DIF檢測方法的差異。研究結果顯示,通過觀察準確率可以發現,操弄遺失機制對資料回復的影響最大,其中又以完全隨機遺失機制在資料回復表現最好,隨機遺失與非隨機遺失機制表現較差,除此之外,遺失機制的不同也會造成遺失比率對資料回復有所影響,在遺失資料處理方法方面,分類與回歸樹、預測平均數匹配與多重插補法三種方法在資料回復的效果上表現最佳,並且在不同遺失機制上保有一定的資料回復能力;在DIF檢測方面,Lord法在DIF比率10%至20%時型一錯誤率符合模式預期,MH法則是在DIF比率10%以下時型一錯誤率符合模式預期,兩種方法皆會隨著DIF比率上升,而使統計檢定力下降且型一錯誤率上升,兩種方法差異僅在適用的DIF比率並不相同,但在統計檢定力上,Lord法表現較低,未來研究建議使用分類與回歸樹、預測平均數匹配與多重插補法三種方法,DIF比率10%以下時使用MH法,DIF比率10%至20%時使用Lord法。
The fairness of a test is the basis for administering a test, and also a critical indicator for examining test quality. Therefore, the detection of differential item functioning (DIF) is particularly crucial. Missing data may affect the DIF detection method. If the data missing rate is too high, the DIF detection cannot work. Therefore, how to handle missing values to ensure that the DIF detection results are reliable is a crucial topic for researchers. Because different methods of handling missing values result in different state of restored data, they may subsequently affect the DIF detection results. Finch (2011) reported that using multiple imputation to handle missing data is advantageous. However, that research only considered the scenario of first item missing, not considering the factors of DIF item ration or data missing ratio on DIF detection results. Therefore, this study aimed to understand how these factors affected missing values on the DIF detection results. The results of this study may provide a more favorable handling method for problems encountered when handling data. This study was divided into two parts. Part I investigated the data restore effects using various methods of handling missing values when facing different missing ratios, missing mechanisms, DIF ratios, and magnitudes of DIF. Part II investigated the differences in using different DIF detection methods after undergoing different missing handling methods. Observations of data accuracy revealed that manipulating missing data mechanisms had the greatest effect on data restoration. Among them, the missing completely at random mechanism demonstrated optimal performance in data restoration, superior to that of missing at random and not missing at random mechanisms. In addition, different missing mechanisms resulted in influenced on missing ratio on data restore. Regarding missing data handling methods, three methods, namely classification and regression tree, predictive-mean matching, and multiple imputation, demonstrated optimal performance in data restoration. These methods maintained certain data restoration functions under different missing mechanisms. Regarding DIF detection, Lord method met the model expectation of type I error when the DIF ratio was 10%–20%. The MH method met the model expectation of type I error when the DIF ratio was 10% or lower. As DIF ratio increased, the statistical power of both methods decreased, and their type I error rates increased. These two methods only differed in the DIF ratio to which they were suitable. Regarding statistical power, Lord method was inferior. Future studies are advised to use classification and regression trees, predictive-mean matching, and multiple imputation. If the DIF ratio is 10% or lower, the MH method should be used; if it is 10%–20%, the Lord method should be applied.
摘  要
第一章 緒論
第一節 研究動機與目的
第二節 研究問題
第二章 文獻探討
第一節 遺失機制
第二節 遺失資料處理方法
第三節 DIF檢測方法
第四節 影響DIF檢測因素
第三章 研究方法
第一節 操弄變項
第二節 研究程序
第四章 研究結果與討論
第五章 結論與建議
第一節 結論
第二節 研究限制與建議

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