政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/56400
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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/56400


    Title: 網路民意調查偏誤的評估及修正機制
    Other Titles: Exploring Adjustment for Web Survey$S Estimation Bias
    Authors: 俞振華
    Contributors: 行政院國家科學委員會
    國立政治大學選舉研究中心
    Keywords: 網路調查;估計偏誤;樣本代表性;非源於觀察此一動作所產生的誤差
    Web survey;Estimation bias;Sample representation;Error of nonobservation;Propensity score adjustment
    Date: 2011
    Issue Date: 2012-12-05 11:07:55 (UTC+8)
    Abstract: 過去數年來，網路在台灣的普及率快速增加，各式各樣的網路調查在我們生活周遭時常出現。但一般學術界對於網路調查的結果多半不會認真看待，因為網路調查多半以非機率樣本的方式選擇受訪者，所得出的結果無助於我們推論母體特徵。本研究計畫企圖從建構機率樣本的網路調查名單開始，進一步探討利用網路調查方式時，因「非源於觀察此一動作所產生的誤差」所造成的估計偏誤。主要的研究問題有以下兩個：首先，網路調查所造成的偏誤究竟有多嚴重？過去台灣並沒有系統性的探討究竟網路調查因樣本代表性不足所造成的偏誤為何。本研究希望在這個問題上做出貢獻。在方法上，當我們確認因樣本代表性不足所造成的偏誤之後，我們應透過何種方式來校正網路調查結果？矯正此類偏誤的方式不外乎是在事前抽樣時即進行校正，或是事後採用各式加權處理。本研究的第二個探討主題是如何採用不同的方式來調整網路調查結果的偏誤，並將重點置於如何利用樣本比對的方式（分別來自於網路訪問及電話訪問），透過相同變項進行配對，再以入選機率調整法（Propensity score adjustment）修正網路調查結果的偏誤。
    Taiwan’s internet coverage has been dramatically increasing over the past year. It is quite often to observe various web surveys in our day-to-day lives. Yet, scholars rarely consider their results seriously. It is due to the fact that these survey results are products of op-in internet panels and can not be used to draw inference about the target population. This project aims to build a web survey panel using probability-based recruitment and to explore possible estimation bias due to error of nonobservation. The main subject of this project is twofold: first, it focuses on how serious the estimation bias could be in a given web survey. So far there exists no systematical analysis using Taiwan’s empirical web survey data to examine the extent to which nonrepresentative sample data can lead to estimation bias. Second, given the estimation bias of web survey, the next question would be how we can adjust it. The conventional wisdom suggests two stages to make any possible adjustment—that is, either at the sample design stage or at the post-survey weighting stage. This project will address both ways to adjust estimation bias. Specifically, it will pay additional attention on using propensity score adjustment to overcome web survey’s noncoverage and nonresponse errors.
    Relation: 基礎研究
    學術補助
    研究期間:10008~ 10107
    研究經費:458仟元
    行政院國家科學委員會
    計畫編號NSC100-2410-H004-114
    Data Type: report
    Appears in Collections:[Election Study Center] National Sci-Tech Programs

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