監測訪問時間的調查品質管制程序

Monitoring Interview Duration for Survey Quality Control

10.7014/SRMA.2020040001

謝淑惠(Shu-Hui Hsieh)；李名鏞(Ming-Yung Lee)

訪問時間 ； 調查品質管制 ； 管制圖 ； 台灣社會變遷基本調查 ； 主成分分析法 ； interview duration ； survey quality control ； control chart ； Taiwan Social Change Survey ； principle components analysis

調查研究-方法與應用

44期（2020 / 04 / 01）

7 - 39

繁體中文

社會科學研究常使用問卷進行資料收集，但資料品質與抽樣誤差、調查過程產生的誤差等有關。Biemer和Lyberg（2003）建議使用資料收集過程來監控資料品質。Japec（2005）建議監測訪談的持續時間，以提供有關資料品質的訊息。有好的過程品質，才能得到好品質的資料。以調查周邊資料和時間標記作為資料品質管理，不僅能獲得更高品質的資料且能同時改善操作效率和降低成本。本研究以2015年「台灣社會變遷基本調查計畫」工作與生活組為例，提出一個以訪問時間為基礎的調查品質管制程序，並將管制圖應用於調查資料品質管制。我們提出的程序包含三個部分：⑴訪員的篩選程序，利用主成分分析法依據訪問時間將訪員分類；⑵決定檢查比例的程序，依據訪員分類配置不同的檢查比例；⑶訪員個人監測程序，運用個人中位數管製圖選取檢查樣本。此研究目標不是簡單的監測，而是提供持續品質改善的程序，且能更有效的掌握檢查作業成本，以兼具品質改善與成本控管。

To collect data in social sciences, it is common to use questionnaire survey methods. Data quality is essentially a function of the magnitude of error in data. If data have sufficient accuracy, it is said that the data quality is high. On the other hand, if the magnitude of error in data is large, the data are said to have poor quality. These criteria for data quality can be applied when using data to estimate population parameters. There are two forms of error in estimating parameters. The first, known as sampling error, is the error as a result of drawing a probability sample rather than conducting a complete enumeration. The second, known as nonsampling error, refers to all other forms of error that can occur during data collection and processing procedures. Therefore, efforts to improve data quality are directed at nonsampling error. Biemer and Lyberg (2003) provide an extensive discussion of methods for data quality improvement. They suggest focusing on steps to improve the data collection process. Often a good-quality process implies good quality of both the data sampled and the resulting output. Using paradata and time stamps to manage survey data collection can lead to a more efficient operation that produces higher quality data at lower cost. In this study, we explore the effect of interview duration on data quality in the Taiwan Social Change Survey (TSCS). The TSCS, the first nationally representative survey in Taiwan, was established in 1985 and is conducted through face-to-face interviews. In it, interviewers are instructed to perform their tasks according to the key principles of standardized interviewing. If the interviewer complies with standardized procedures, an abnormal interview duration is less likely. Conversely, if the interviewer deviates from the standardized procedures, there is higher probability of an abnormal interview duration. Interview duration is very important for the interviewer with regard to planning fieldwork activities, and is a factor which influences his or her cost and benefit analysis. Short and well-paid interviews are financially and organizationally more attractive. Japec (2005) suggested monitoring interview duration, since it can tell us something about data quality. This paper addresses the problem of quality control in a survey based on interview duration data, and applies control charts to the monitoring process. We conducted this process in three parts: (1) classification of interviewers based on interview duration data using principal components analysis. The aim was to improve the efficiency of monitoring the data collection process. (2) detection of the optimal rate among different interviewers' classifications, and avoidance of continually collecting data by using non-standard interview procedures which affect the quality of survey data. (3) use of a median control chart to monitor the potential causes of variation between individual interviewers to find out why some interviewers' interview times exceed the control limit. To assess the applicability of the proposed method, we used the 2015 Work and Life module of the TSCS to illustrate the proposed process for survey quality control. The research goal of this article was not simply to monitor, but also to improve the quality of the process over time. Based on the proposed process, it is notable that the inspection operation cost can be more effectively controlled.

1. Bassili, John(1996).The How and Why of Response Latency Measurement in Telephone Surveys.Answering Questions: Methodology for Determining Cognitive and Communicative Processes in Survey Research,San Francisco, CA:
2. Biemer, Paul P.,Lyberg, Lars. E.(2003).Introduction to Survey Quality.Hoboken, NJ:John Wiley & Sons.
3. Bushery, John,McGovern, Pamela,Marck, Paul S.,Hogan, Howard(2006).Quality Audits at the U.S. Census Bureau.European Conference on Quality in Survey Statistics,Newport, America:
4. Cannell, Charles F.,Marquis, Kent H,Laurent, Andre(1977).A Summary of Research Studies of Interviewing Methodology, 1959-1970.Washington, DC:U.S. Government Printing Office.
5. Cannell, Charles F.,Miller, Peter,Oksenberg, Lois(1981).Research on Interviewing Technique.Sociological Methodology,12,389-437.
6. Couper, Mick,Lyberg, Lars(2005).The Use of Paradata in Survey Research.the 55th Sections of the International Statistical Institute,Sydney, Australia:
7. Groves, Robert M.,Heeringa, Steven G.(2006).Responsive Design for Household Surveys: Tools for Actively Controlling Survey Errors and Costs.Journal of the Royal Statistical Society: Series A,169(3),439-457.
8. Hughes, Arther,Chromy, James,Giacoletti, Katherine,Odom, Dawn(2002).Impact of Inter-viewer Experience on Respondent Reports of Substance Use.Redesigning an Ongoing National Household Survey,Rockville, MD:
9. Jans, Matt,Sirkis, Robyn,Morgan, David(2013).Managing Data Quality Indicators with Paradata Based Statistical Quality Control Tools: The Key to Survey Performance.Improve Surveys with Paradata: Analytic Uses of Process Information,Hoboken, NJ:
10. Japec, Lilli(2005).Stockholm,Department of Statistics, University of Stockholm.
11. Kreuter, Frauke,Couper, Mick,Lyberg, Lars(2010).The Use of Paradata to Monitor and Manage Survey Data Collection.Section on Survey Research Methods, Joint Statistical Meetings,Vancouver, Canada:
12. Loosveldt, Geert,Beullens, Koen(2013).‘How Long Will It Take?’ An Analysis of Inter-view Length in the Fifth Round of the European Social Survey.Survey Research Meth-ods,7,69-78.
13. Lyberg, Lars(1981).Stockholm,Department of Statistics, University of Stockholm.
14. Mudryk, Walter, Bob Bougie, and Hansheng Xie, 2002, Some Guidelines for Data Analysis in Quality Control. Statistics Canada Technical Report. Ottowa, Ontario: Statistics Canada.
15. Nix, Brian(2014).The Effect of Interview Length on Data Quality in the Consumer Expenditure Interview Survey.2014 Joint Statistical Meetings,Boston, America:
16. Olson, Kristen,Peytchev, Andy(2007).Effect of Interviewer Experience on Interviewer Pace and Interviewer Attitude.Public Opinion Quarterly,72,273-286.
17. Robyn, Sirkis,Jans, Matt(2011).Using Statistical Process Control to Understand Variation in Computer Assisted Personal Interviewing Data.2011 Joint Statistical Meetings,Florida, America:
18. Ryan, Thomas P.(2000).Statistical Methods for Quality Improvement.New York:Wiley & Sons.
19. Vandenplas, Caroline,Loosveldt, Gaart,Beullens, Koen(2017).Fieldwork Monitoring for the European Social Survey: An Illustration with Belgium and the Czech Republic in Round 7.Journal of Official Statistic,33,659-686.
20. Weisberg, Herbert F.(2005).The Total Survey Error Approach: A Guide to the New Science of Survey Research.Chicago:University of Chicago Press.
21. Wheeler, Donald J.(2004).Advanced Topics in Statistical Process Control: The Power of Shewhart’s Charts.Knoxville, TN:SPC Press.

1. 莊文忠,洪永泰,林美榕(2022)。不同抽樣底冊之選民母體與投票母體的輪廓分析：以2016年總統選舉民調為例。選舉研究，29(1)，69-117。