未表態選民是選舉預測過程中非常重要的一環。本文嘗試先從人口變項、議題立場及政治態度等面向對已表態選民作區辨分析。結果發現，單從這些面向對已表態者的區辨能力來看，政治態度的區辨能力最高，議題立場次之，而人口變項最差。而即使把三個面向的變項共同建構一個模型，其區辨能力也並不令人滿意。如果將其用來預測未表態者，其正確度也令人懷疑。作者並嘗試以情感溫度計來區隔表態選民並預測未表態選民，經過與實際得票率比對後，發現情感温度計的區辨能力比上述的模型都要好，而其预測結果也最接近實際投票結果。另外作者也發現從各候選人的支持群眾分布來看，陳水扁和連戰的支持者平均距離是最遠的，宋楚瑜和陳水扁支持者的距離次之，而連戰和宋楚瑜的支持者的距離最接近。 In election forecast, the ability to predict the intentions of undecided voters plays an important role in determining its accuracy. This article uses discriminant analysis with demographic variables, issue positions, and attitudinal variables, respectively, to classify voters who stated their voting preferences. We found that comparatively speaking, attitudinal variables can correctly classify voters with the highest percentage, followed by issue positions and demographic variables. However, none of the above models displayed satisfying results, not even the combined model including all three types of variables. Therefore, there are reasonable doubts employing these variables to construct models predicting the vote intention of undecided voters. The author then attempts to use feeling thermometer scores to classify voters who expressed their vote intention sand to predict the inclinations of undecided voters. After contrasting the results with the actual election outcome, we found that feeling thermometer scores have better discriminant power than previous models. The author also found that among the supporters of the three major candidates, ideologically speaking, Chen Shui-Bian's and Lian Chan's supporters are furthest away from each other on their average positions, while James Soong's supporters' average ideological position is closest to that of Lien Chan's supporters.