In panel surveys, there are two sources of error that threaten to make panel data invalid and unreliable. First, nonresponse among specific respondents, and second errors in measurement of the topic of interest using survey questions. Survey methodologists worry that errors due to nonresponse and measurement interact. Some reasons for nonresponse might at the same time also be a reason for reporting with more measurement error. Lower cognitive abilities, complex income compositions, or language difficulties are among the reasons for both nonresponse and measurement error.
In this presentation, a general method for investigating trade-offs and common causes for both nonresponse error and measurement error is outlined. Statistical modeling is used to separate the two types of error; we normally have no information on measurement error for those people that do not participate. After this, common causes of the two error sources can be linked to the model to identify which respondent characteristics are responsible for common causes of nonresponse and measurement error. Understanding the trade-off better will enable researchers to compare the nature and size of both errors, and make better informed decisions in trying to limit survey errors. This in turn may make future panel studies more cost-effective and provide higher data quality. In this presentation, data from the British Household Panel Survey is used to illustrate the method, and possible implications for survey practice.
Presented by:
Peter Lugtig (ISER)
Date & time:
June 19, 2013 12:00 pm
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