Proceedings of the American Statistical Association.

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**DOI:** Not available at this time.

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**Abstract:** ABSTRACT: 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 &
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 & 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. However,
most of the commonly used survey designs
involve clustering and stratification and hence the
multinomial assumption is violated (Rao & Scott,
The views expressed in this paper are those of the authors
and do not necessarily reflect the policies of the U.S. Bureau
of Labor Statistics.
1981). Literature has shown that clustering can have
a substantial effect on the distribution of the standard
Pearson chi-squared test statistic, χ^{2} and that
some adjustment to χ^{2} may be necessary, without
which one can get misleading results in practice (Rao
& Scott, 1981). Rao & Scott developed a simple correction
to χ^{2} which requires only the knowledge of
deffs (or variance estimates) for individual cells in
the goodness of fit problem (Rao & Scott, 1981).
The original Rao & Scott papers considered inference
for one vector of proportions, based on (essentially)
one sample.
In this paper, we are considering inference for a
large number of proportion vectors p_{i}, i = 1, ..., I,
where I is the total number of interviewers. Only
a small portion of an interviewer’s workload can be
verified because of the limited resources. Therefore,
we addressed the optimum allocation of resources
such as re-interview time using the optimal decision
rule

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**Reference Type:** Journal Article

**Subject Area(s):** Statistics