September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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推估偏頗：一般問題
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這很顯然的是衝突的結果，強烈的讓我們感受到使用調查資料的兩個缺點，推估的偏頗
與回報的偏頗。能夠用來對付資料缺點的策略是很有限的，但意識到它們的存在並且盡
可能的採取預防措施，將能夠讓你的研究更好並且擁有更可靠的結果。
本章剩餘的部分將會描述這兩個問題—推估錯誤以及回報錯誤—也是在你使用的資料中
所可能會遇到的問題。我也會探討四個其他的偏頗可能來源—超過上限、接縫偏頗、代
理回答以及樣本選擇。總結來說，我要告訴你的就是要小心你所使用的資料，並且瞭解
它是如何被搜集，以及調查的建立（或是實施調查的機構）是如何讓你的資料變得偏
頗。要探討這些偏頗的來源也許需要一些時間以及精力，但獲得的東西絕對是值得的。
推估偏頗：一般問題
高度推估比例的資料讓調查資料的使用變得複雜。推估是發生在，當調查受訪者無法回
答特定問題時，而實施調查的機構可能會置換記錄的值。舉例來說，儘管某個受訪者可
能很高興的給你姓名、年紀、性別、教育程度以及種族資料，但她可能不想跟你分享
她去年賺了多少錢或是去年的工作時數。這些機構有許多的方法能夠推估這些遺失的
值；舉例來說， Census Bureau 使用不同的方法，最常見的就是熱卡插補法（hot-deck
imputation）。這個方法會在相同的資料集合中，尋找擁有完整記錄的相似受訪者，然後
以該人的值當成這個遺失的值。
4
在 Survey of Income and Program Participation（SIPP，
由 Census Bureau
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.