As a survey methodologist I get paid to develop survey methods that generaly minimize survey errors, and advise people on how to field surveys in a specific setting. A question that has been bugging me for a long time is what survey error we should worry about most. The Total Survey Error (TSE) framework is very helpful for thinking which type of survey error may impact survey estimates
But which error source is generally larger?
Last week, I wrote about the fact that respondents in panel surveys are now using tablets and smartphones to complete web surveys . We found that in the LISS panel, respondents who use tablets and smartphones are much more likely to switch devices over time and not participate in some months.
The question we actually wanted to answer was a different one: do respondents who complete surveys on their smartphone or mobile give worse answers?
A follow up on last month’s post . Respondents do seem to be less compliant in the waves before they drop out from a panel survey. This may however not neccesarily lead to worse data. So, what else do we see before attrition takes place? Let have a look at missing data:
First, we look at missing data in a sensitive question on income amounts. Earlier studies ( here , here, here ) have already found that item nonresponse on sensitive questions predicts later attrition.
I am working on a paper that aims to link measurement errors to attrition error in a panel survey. For this, I am using the British Household Panel Survey. In an earlier post I already argued that attrition can occur for many reasons, which I summarized in 5 categories.
3. Inability (due to old age, infirmity) as judged by the interviewer, also called ‘other non-interview’.
4. Ineligibibility (due to death, or move into institution or abroad).
I am spending time at the Institute for Social and Economic Research in Colchester, UK where I will work on a research project that investigates whether there is a tradeoff between nonresponse and measurement errors in panel surveys.
Survey methodologists have long believed that multiple survey errors have a common cause. For example, when a respondent is less motivated, this may result in nonresponse (in a panel study attrition), or in reduced cognitive effort during the interview, which in turn leads to measurement errors.