With measurement error modeled as the random variables
a general measurement error model is presented. It decomposes the errors into variance and measurement bias. The variance can be further decomposed into measurement variance and sampling variance. The measurement variance is composed with simple measurement variance and correlated measurement variance. The calculation formula and estimation method of the above errors are obtained. According to the most common interviewer effects in survey practice
combined with investigator assignments
it is proposed that the low correlation coefficient of measurement results in repeated surveys is the key to control the correlation measurement variance
and that increasing the sub-sample interviewers can effectively reduce investigator variance.
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references
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