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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/49599
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/49599

    Title: 葛特曼量表之拒答插補研究
    Authors: 左宗光
    Contributors: 江振東
    Keywords: 拒答
    Date: 2008
    Issue Date: 2010-12-08 14:52:55 (UTC+8)
    Abstract: 在抽樣調查的資料中,可能因為題意不清、關係到個人隱私,或是議題太過於敏感而導致受訪者「拒答」。透過存在拒答的樣本資料來做分析探討時,很可能會造成偏誤的研究結果,因此如何處理無反應的資料常常是一項研究結果是否可信的重要關鍵之一。常見的處理方式通常是設法對這些拒答資料進行插補。然而插補的好壞一直沒有一個判定準則,分析結果亦常因此受到質疑。
    In a questionnaire survey、respondents may refuse to answer certain items since the questions themselves are unclear、sensitive、or relating to personal privacy. An analysis result using a data set containing refusal responses might be biased、how to deal with survey refusals have thus drawn much attention of late. One popular approach is through the use of imputation. However、lacking a criterion to evaluate its performances、there exist debates concerning the usefulness of this approach.
    In this study、we compare Simple imputation Method、Multiple Imputation Method、and Nearest Neighbor Method to deal with refusals in a set of survey items forming a Gittman scale in terms of imputation accuracy. Data are taken from the 2002 Taiwan Social Change Survey (TSCS)、and the items of interest are about sexual attitude. The parts of data that satisfy perfect Guttman scale are treat as 「Gold Standard」、and refusals are generated according to the original refusal pattern appear in the data.
    The result shows that the accuracy associated with Simple Imputation can actually be derived theoretically. No matter which version of Simple Imputation is applied、the accuracy is no more than 32%. Multiple Imputations performs slightly better than Simple Imputation、the accuracy is about 33%. However、it is less efficient in terms of computer time. Although Nearest Neighbor Method has the best performance the three、and its accuracy can reach as 47%、it requires much more computer time than the other two methods、and the accuracy would decrease as the refusal rate goes up.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0096354013
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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