Survey research as a method of collecting sample data is supposed to produce sample statistics which can estimate the corresponding population parameters if the sampling design is appropriate. However, for reasons such as unit non-response, survey data is usually weighted by the institutes that collect the data or by researchers who analyse the data in order to correct or diminish the discrepancies between sample and population. Sample statistics based on weighted data are more representative of the population parameters than unweighted data in terms of some demographic characteristics.Therefore, to some extent, it seems legitimate to weight data and this manipulation has become a routine when dealing with survey data.

It is true that to weight data could be helpful, but this manipulation needs justifications. This paper therefore tries to argue that to weight data is no panacea and should not be taken for granted when considering the examples in Taiwan’s Election and Democratization Studies (TEDS) surveys. The first section discusses why weighted data is not necessarily representative of the population. As the TEDS surveys show, the turnout, the vote shares of parties, and marital status become more deviant from the population parameters after weighting the data.

If the focus is the relationships between variables, the correlations may be changed by weighting the data in bivariate or multivariate analysis. However, it is not clear whether we manufacture relationships which do not exist or if weighting the data actually helps us approximate the relationships that already exist in the population. Besides, it should be noted that to weight data set as a whole only deals with the problem of unit non-response, but does not solve the problem of item non-response.

The third section discusses why most efforts should be devoted to examining and improving questionnaires, sampling designs, and interviewerm straining and supervision, instead of simply appealing to post-weighting. If everything necessary has been tried, weighting data may be the last resort to improve the estimates. But the justifications for the selection of auxiliary variables and the methods of calculating weight factors should be provided rather than doing it without any explicit considerations. It is also important to consider whether the consequence of weighting is positive or negative.

經由抽樣設計恰當的調查研究所收集到的樣本資料應該能夠準確估計母體參數。但是因為單位無反應的問題，執行調查的單位或分析資料的學者通常會以加權的方式來 減少樣本統計量與母體參數之間的差距。加權後的資料在人口學變項上比未加權資料較為接近母體參數，因此加權似乎是一個合理處理樣本資料的做法。

然而，即使加權是可行的解決方法，也絕非萬靈丹。在加權前也必需提出事後操弄資料的理由，而不是將加權視為理所當然。本文以台灣選舉與民主化調查為例，首 先說明加權後的資料不必然較接近母體參數的原因。投票率、各政黨得票率、與婚姻狀況在加權後反而和母體參數有較大的差距。

除了單一變數分析之外，當討論的主題是變數間的關係時，加權可能增加也可能減少相關性的強度。雖然加權似乎會影響相關性，但其影響究竟是更接近真實的關 係，抑或是扭曲真正的相關性則不得而知。此外，通常對整筆資料作加權只處理了單元無反應的問題，但仍然沒有解決多變量分析一定會遇到的項目無反應問題。

不論是單一變數分析或是多變量分析，在加權之前應該先嘗試其他增加樣本代表性與提高資料品質的方法。如果沒有先投入更多時間與心力在問卷設計、抽樣設計、 訪員訓練與監督上，加權只是低成本的取巧做法。最後，假使一定要加權，必須說明與討論為什麼要加權、以哪些變數加權、如何加權、以及加權所產生的影響，而 非不加思考地將加權當作例行公事。

本研究利用台灣地區社會變遷基本調查四期一次卷一資料，從認知歷程、回答動機與社會情境三個理論途徑，分析總統選舉投票意向調查訪問中，影響兩種無效回答（不記得與不願意回答）產生的可能原因。經兩階層多類別邏輯迴歸分析法實證「不記得」與「拒答」不是隨機產生的。相對於有回答投票對象，「不記得」的產生受到與認知與動機相關的受訪者年齡、教育與政治態度的影響；拒答則是源自受訪者動機與社會情境相關變項（如：受訪者政治態度、訪員性別）。本研究印證了Cannell, Miller與Oksenberg（1981）雙軌回答決定歷程。也證實項目無反應的分析中不可缺少訪員效應。本文最後從調查實務與方法研究兩方面，提供多項建議。