Adjusting Survey Response Distributions Using Multiple Imputation: A Simulation with External Validation

應用多重插補法評估選民政黨傾向的可能性：以電話調查中不表態中間選民為例

劉正山(Frank C. S. Liu) ； 蘇毓淞(Yu-Sung Su)

《調查研究-方法與應用》 34期特刊 (2015/10) Pp. 7-32

摘要

電話調查中政黨傾向的「項目無反應」是選舉研究中值得正視的現象。愈來愈多台灣民眾在接受調查訪問時，不願表露本身政黨傾向，或選擇「中立」來回避作答，造成研究人員無法從調查數據中正確地估計選民政黨傾向的分佈，進而誤判選舉結果。多重插補法是解決這類資料缺失問題的統計方法之一。嚴格來說，學者尚無法確定什麼情況下可以放心的使用多重插補法來估計不表態民眾的政黨傾向分佈；原因一是政黨傾向並非隨機遺漏；第二是研究人員尚無法掌握檢驗數據遺漏機制。我們使用2013年收集的全國性的電訪資料，展示多重插補法如何有效地解決這個問題。本文首先使用模擬遺漏機制的方式，比較多重插補前後數據的差異，指出控制與政黨傾向相關的變數會使政黨傾向的遺漏接近隨機；其次，我們比對插補後的資料及電訪追訪結果，並對中間選民進行深度訪談後，發現經過遺漏值分布和數據遺漏機制檢驗的政黨傾向插補數據具有高度的外部有效性。此方法和檢驗程序亦適用於其他非隨機遺漏的「項目無反應」研究。

關鍵字

多重插補 ； 項目無反應 ； 遺漏值 ； 外部有效性檢證

並列摘要

Item non-response is endemic to most survey studies, and hinders the researcher in making sensible inferences. One plausible solution to this problem, multiple imputation (MI), is becoming a widely used approach in dealing with the problem of missing data thanks to the development of various software packages. Nonetheless, MI is not a panacea. Imputing missing data using MI without checking it may further induce biases. This oblivious use of MI arises partly from the conviction that some MI assumptions are simply mathematically unverifiable. Hence, the goal of the paper is twofold: first, it demonstrates how various post-MI diagnostics can be performed with a telephone survey dataset collected in Taiwan in early 2013; secondly, it places greater emphasis on the external validity of MI with a follow up survey, and compares imputed values to the real ones. This paper concludes that, with a sensible application of MI and accompanying diagnostics, we are able to adjust survey response distribution and, at the same time, elaborate on the inferences in our studies.

並列關鍵字

multiple imputation ； item-non-response ； missing values ； external validation

參考文獻

Allison, Paul D.(2001).Missing Data.Thousand Oaks, CA:Sage.

Barzi, F.(2004).Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies.American Journal of Epidemiology.160(1),34-45.

Bernaards, Coen A.,Farmer, Melissa M.,Qi, Karen,Dulai, Gareth S.,Ganz, Patricia A.,Kahn, Katherine L.(2003).Comparison of Two Multiple Imputation Procedures in a Cancer Screening Survey.Journal of Data Science.1(3),293-312.

Bernhagen, Patrick,Marsh, Michael(2007).The Partisan Effects of Low Turnout: Analyzing Vote Abstention as a Missing Data Problem.Electoral Studies.26(3),548-560.

Buuren, Stef van,Groothuis-Oudshoorn, Karin(2011).Mice: Multivariate Imputation by Chained Equations in R.Journal of Statistical Software.45(3),1-67.