在調查研究中常會面臨項目無反應的問題，亦即某些受訪者雖然接受了訪問，但卻在部份問題上沒有提供研究者有效的分析答案，而回答不知道、無意見、很難說或甚至拒答。然而，僅以有反應的樣本來推估母體參數，可能會造成母體參數估計的偏誤。本研究藉助選樣偏誤模型，試著解答究竟無反應問題在甚麼樣的情況下會造成估計的偏誤，以及如何校正估計的偏誤。本研究以「台灣選舉與民主化2004年總統選舉」的調查資料為分析的基礎，以投票對象、政治功效意識與統獨立場三個研究主題說明無反應的問題與選樣偏誤模型的應用。研究發現顯示：在投票對象的模型中，由於泛藍認同者有較多偏向無反應，且政黨認同又決定性地影響投票對象，因此，無反應問題導致的估計偏誤相當嚴重。同時，在政治功效意識的模型中，泛綠認同者有較多偏向無反應，且他們又傾向有比較高的政治功效意識，因此無反應問題也會造成估計值的偏誤。但是在統獨立場的模型中，影響受訪者反應與否的因素並沒有同時影響受訪者的偏統或偏獨，因此並沒有造成估計值的偏誤。此一研究結論說明：無反應問題是否造成估計偏誤，與無反應受訪者的比例多寡無關，而是決定於影響受訪者無反應的因素，是否同時影響受訪者在研究主題的答案方向；如果是，則將會造成估計的偏誤，此時，以選樣偏誤模型來校正估計值的偏誤可以發揮相當不錯的效果。

Survey researchers inevitably face the problem of item non-response; that is, some respondents provide valid answers to some questions, but provide invalid answers to others, such as "don't know," "no opinion," or "hard to say." However, including only those respondents who provide valid answers for each question may bias the estimates, because non-response does not occur randomly. This study applies a selection bias model to the problem of item non-response and tries to answer the following questions: in what situations might item non-response cause estimation bias, and in which ways might we correct for this bias? By employing survey data collected by Taiwan's Elections and Democratization Studies Project on the 2004 Presidential Election (TEDS 2004P), this research applies selection bias models to three topics of interest in the survey-vote choice, political efficacy, and position on the independence-unification issue. The research findings show that the vote choice model estimates may suffer from serious selection bias because non-respondents tended to be pan-blue voters and to vote for the pan-blue candidate. In the political efficacy model, non-respondents tended to be pan-green voters and also to have higher levels of political efficacy. Estimates for that model may therefore also suffer from selection bias. In the independence-unification issue model, by contrast, the factors affecting rates of item non-response were not systematically related to the respondents' positions on the issue. Therefore, the model estimates do not suffer from selection bias. The research findings demonstrate that the seriousness of selection bias is not determined by the non-response rate per se, but rather by the degree to which response rates are systematically related to particular answers to a given survey question. The selection bias model can provide a satisfactory way to correct for estimation bias introduced by item non-response.