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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/67790

    Title: 樣本代表性檢定與最小差異加權:以2001年台灣選舉與民主化調查為例
    Other Titles: On Minimum-Discrimination-Information (MDI) Method of Weighting: an Application to the 2001 Taiwan's Election and Democratization Study(TEDS)
    Authors: 黃紀;張佑宗
    Huang, Chi
    Contributors: 政治系
    Keywords: 樣本代表性檢定;事後分層加權;反覆多重加權;最小差異加權
    goodness-of-fit tests;raking;post-stratification weighting;minimum discrimination information weighting;TEDS;cross-entropy
    Date: 2003
    Issue Date: 2014-07-29 10:38:43 (UTC+8)
    Abstract: 樣本代表性檢定的目的,在發覺樣本是否被過度扭曲,而影響對母群特質的推估。大多數的民意調查計畫,包括電訪與面訪,如果發現樣本結構與母群不符,所採取的補救措施,不外乎「事後分層加權」或是「反覆多重加權」這兩種方式。然而,調查研究需要檢定的變數,通常超過一個以上,而「事後分層加權」所需之母群多變數聯合分佈值,往往為未知。因此,目前最常採用的是「反覆多重加權」的方式,但「反覆多重加權」實際執行時,最大的問題在於其檢定方式是透過卡方檢定,通常只要其檢定P值大於0.5,就認定樣本與母群一致,而未考慮一個最佳化的加權值。本文旨在提出第三種事後加權的處理方式,也就是「最小差異加權」的方法,它能同時考慮數個變項,而找出最佳的加權值。我們以「2001年台灣選舉與民主化調查(TEDS 2001)資料為例,分別進行「反覆多重加權」與「最小差異加權」,並與2000年戶口普查之母群資料比較,發現就性別、年齡、教育與地理區堿四個變數的聯合分佈估計值,整體而言,最小差異加權的估計值,有將近七成比「反覆多重加權」的估計值更接近母群的聯合分佈值,應值得後續研究進一步探討。
    Goodness-of-fit tests allow us to examine if the sample at hand is representative enough of the population to ensure accurate statistical inferences of parameters. When the sample fails the tests, survey researchers often appeal to reweighting as a remedy. Post-stratification and raking are perhaps the two most popular weighting methods. However, post-stratification requires the knowledge of multivariate joint distribution of the population when more than one post-stratifying variable is considered. Without such detailed information, raking comes as a rescue since it requires only the knowledge of marginal distributions of selected variables. Popular as it may be, raking takes no account of associations among post-stratifying variables. Furthermore, it relies heavily on Chi-squared tests and a pre-selected p-value (usually 0.5) as the stopping rule of iteration, an ad hoc rule justified only by convenience. This article proposes a third way of Weighting, which we call it the minimum-discrimination-information (MDI) method. MDI approach finds optimal (in terms of minimum cross-entropy) relative weights by treating sample joint distribution as prior and known population marginal distributions as constraints. We first explain the rationale behind this proposed MDI method and then use TEDS 2001 survey data to compare the estimates of raking and MDI weights. We find that nearly 70 percent of the latter indeed replicate the Census 2000 population joint distribution better than the former. We thus conclude that MDI method is an approach worth further theoretical investigation.
    Relation: 選舉研究, 10(2), 1-33
    Data Type: article
    Appears in Collections:[政治學系] 期刊論文

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