Inferential Methods to Identify Possible Interviewer Fraud Using Leading Digit Preference Patterns and Design Effect Matrices

摘要

Interviewer fraud can damage the data quality severely. How can we detect it. Turner et al. (2002) used response patterns to detect falsification. They reported that suspected falsifiers could be noticeable by an unexpectedly high yield of interviews per assigned sample address, and/or unusual response rates for specific reported variables on behaviors. Turner et al. also discussed the systematic differences between suspected falsifiers and other interviewers in providing the verification means, such as telephone numbers of the respondents. Biemer and Stokes (1989) proposed a statistical model for describing dishonest interviewer behavior, which was applied to a general quality control sample design and several associated cost models. A 1982 U.S. Bureau of the Census study indicated a higher degree of cheating in urban areas (Biemer & Stokes). The study also shows a substantial and highly significant tendency for relatively inexperienced interviewers to cheat more frequently for the two largest demographic surveys, the Current Population Survey and the National Crime Survey (Biemer and Stokes). We used the leading digits to detect curbstoning in this paper. The effect of the sampling design, such as stratification and clustering, on standard Pearson chi-squared test statistics for goodness of fit is investigated. Statistical methods for analyzing cross-classified categorical data has been extensively developed under the assumption of multinomial sampling.