Measurement error
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Measurement Error. e. M. Measurement Error. Survey record differs from its true value Sampling error: arise from the random sampling variation when n of units measured instead of N units. Measurement error (Systematic error, nonsampling error).

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Measurement error1



Measurement Error

  • Survey record differs from its true value

    • Sampling error: arise from the random sampling variation when n of units measured instead of N units.

    • Measurement error

      (Systematic error, nonsampling error)

Simplified model
Simplified Model

Normal Dist’n


  • In presence of measurement error with mean zero, the estimator of mean and total remain unbiased or consistent.

  • For more complex parameters, the nice feature may not hold.

  • Fuller (1995) points out that the usual estimators for dist’n func., quantiles, regression coef. are biased.

  • If estimates of the measurement error variances are available, it is possible to obtain bias-adjusted estimators.

  • Repeated measurement of sub-samples

    • Allocate resources at design stage to make repeated observations on a sub-sample.

    • Hartley and Rao (1978) and Hartley and Biemer (1978) provided interview and coder assignment conditions that permit the estimation of sampling and measurement error.

  • Measurement error variance model.

Interviewer effect
Interviewer Effect available, it is possible to obtain bias-adjusted estimators.

  • Earliest examination of measurement error in the survey focuses on evaluating the impact of interviewers on the data.

  • There is correlation among measured values collected by the same interviewer.

  • Hansen, Hurwitz and Bershad model shows

References variability.

  • Full, W.A. Estimation in the presence of measurement error.

  • Scott, A. and Davis, P.. Estimating interviewer effects fro survey responses.

  • Hartley, H.O. and Biemer, P.. The estimation of nonsampling variances in current surveys.

  • Hartley, H.O. and Rao, J.N.K. The estimation of nonsampling variance components in sample surveys.

  • Measurement errors in surveys. Paul Biermer, et al.

Binary data and interviewer effects

Binary Data and Interviewer Effects variability.

An Example

Medical questionnaires
Medical Questionnaires variability.

  • Often use binary variables

  • Interested in proportion parameters

  • Very specialized studies

    • Few reviewers (highly skilled)

    • Very expensive to train

    • Large case loads

  • Interviewer variability is usually ignored because it affects binary data less than continuous data

New zealand quality of health care study
New Zealand Quality of Health Care Study variability.

  • Studying ‘adverse events’ in New Zealand hospitals

  • 2-stage design

    • PPS sample of hospitals

    • Systematic sample of 575 medical records drawn from each hospital for 1998 admissions

  • Average case load per reviewer is 300 (problem!)

  • Typical of such studies

  • Interest in proportion of hospital admissions associated with an adverse event

Model variability.

  • Random Effects Model

  • Hospital Effect

  • Interviewer Effect

  • Respondent Effect

  • Assume an underlying continuous variable initially and then extend to the binary case

Assumptions variability.

  • : Normally Distributed

  • : Normally Distributed

  • : Logisticly Distributed

  • All these effects are assumed to be uncorrelated with all other effects

Design effect
Design Effect variability.

  • Represents the inflation in variance due to the interviewer and cluster effects (i.e. inflation when there are not independent observations)

  • This is assuming small interviewer and PSU effects

Results variability.

  • = 0.04

  • = 0.002

  • = 687

  • Design Effect = 29.0

  • So the variability is increased by a multiplicative factor of 29!

Conclusions variability.

  • With high case loads, even small interviewer variability can have high impact on estimates of population means and proportions

  • Binary data poses special challenges and more research needs to be done when the PSU and interviewer correlations are not small