September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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偏頗的其他來源
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儘管要決定你正在使用的資料集合是否包含回報錯誤（以及這些錯誤是否有影響）是困
難的，小心這類的資料缺點將能夠讓你的研究更長久，更重要的是，讓結論的正當性更
強固。因此，基本的策略就是小心並且詢問關鍵的問題：
1. 在你的資料中的回報錯誤是否很有可能不是隨機的？若不是隨機的話，那麼你的結
果將會有所偏頗。
2. 你認為回報錯誤較可能發生在某些變數上嗎？
3. 你能夠使用其他的變數來找到可能的偏頗來源嗎？舉例來說，每個每年工作時數為
2,000 小時的人是否都擁有去掉尾數的工資數量？（也許該調查只允許受訪者勾選去
過尾數的數量，而不是詢問確切的數量。）
4. 調查的結構本身改變了回報錯誤的可能嗎？舉例來說，調查內容的問題順序是否會
影響人們作答的內容呢？或是人們是否會因為某種原因而在訪問的某個地方回答較
不可靠的答案？
總結而言，回報錯誤是很難被偵測的，但小心對待你的資料的優勢與劣勢，並且根據這
些來調整你的模型或是研究問題，都是很重要的。
偏頗的其他來源
儘管推估偏頗與回報錯誤是兩個在調查資料中最大的偏頗來源，但仍有一些其他的問題
會影響資料的正當性以及可靠性。在本節中，我會簡短的討論四種其他的偏頗來源：
超過上限
／
超過下限
（
Topcoding/bottomcoding
）
為了保護受訪者的資料，有些訪問會將極端高與極端低的值使用單一值來取代。
接縫偏頗
（
Seam bias
）
在跨越了兩次訪問之間的「接縫」的受訪者行為中的變化，跟單一訪問中的變化不
同的縱向（或是追蹤調查）訪問。也就是說，在兩次的訪問中 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.