Older People and Data Quality in Surveys. Does Measurement Error Increase with Age and Deterioration of Cognitive Abilities?
This paper aims to evaluate the quality of the survey data in the Survey for Health and Retirement in Europe (SHARE). We draw on Lynn and Lugtig (2016)’ theoretical framework to assess the effect of aging, and in particular, of changes in respondent cognitive functions, on a number of indicators of measurement error, including heaping and don’t know answers. Our working hypothesis is that aging is associated with poor reporting. Controlling for age and educational level, we expect to find a positive relationship between a deterioration in cognitive functioning (measured with two indicators of short term and long term memory and mathematical skills) and the occurrence of heaping and don’t know answers.
We use Wave 1, 2, 4 and 5 of SHARE data. We intend to employ a set of multilevel models and, in particular, to use growth curve models (GCM) which are appropriate statistical techniques to model change in a dynamic framework. Results from a preliminary analysis have shown that the relationship between aging, cognitive functions and survey data quality and quite complex. E.g. For heaping, cognitive skills do not seem to play a role whereas for don’t answers they seem to have a negative impact.