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Removing respondents that fail attention checks is likely to introduce a demographic bias. Rather, “failing” an attention check question should be used as one of many data quality metrics to be evaluated after data collection has completed. However, our research scientists in the Qualtrics Methodology Lab recently conducted a careful review of emerging research on this topic and found that much of it advises against eliminating these respondents from most datasets (Anduiza and Galais 2016 Berinsky, Margolis, and Sances 2014 2016 Hauser et al.
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Start building surveys today with our free Qualtrics survey account The Problem with Attention Check Strategies Qualtrics has in the past recommended for using attention check questions and eliminating those respondents that fail them from the data, because the attention-check technique seemed intuitively reasonable and pragmatic, and is often used across the research industry. Since the assumption is that respondents that fail the attention check are not paying attention, there is a common belief that these “bad” respondents should simply be eliminated from the dataset, the sooner the better. For the purposes of this post, we will refer to all of the strategies listed above as “attention checks.” While many different factors could result in the pattern of results that these techniques produce, the most common explanation is that the offending respondents are not paying sufficient attention to the survey. These strategies vary somewhat in how they are implemented, but they all share an interest in “catching” respondents who appear to not follow instructions in the survey.
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These strategies for identifying “bad” respondents are known by a variety of different names, including: While the original research indicated that filtering these respondents might increase experimental efficiency, other researchers quickly began using this method not only to identify respondents who do not read instructions but also as a proxy for other low-effort response strategies listed above. No researcher or organization wants low-quality data in their results, and since at least 2009 there has been a widespread trend toward attempting to identify survey respondents that are not carefully reading instructions (Oppenheimer, Meyvis, and Davidenko 2009). Other behaviors include skipping some necessary questions, speeding through the survey by giving low-effort responses, not fully answering open-text questions, or engaging in a variety of other behaviors that negatively impact response quality. Several of these strategies are grouped together under the term ‘satisficing’ – including acquiescence, straight-lining, choosing the first reasonable response, or saying ‘don’t know’ or ‘no opinion’ (Krosnick 1999 Vannette and Krosnick 2014). When the cognitive demands of a survey begin to exceed the motivation or ability of respondents, they often employ a set of response strategies that allow them to reduce the effort that they have to expend without leaving the survey altogether. In this article, we highlight how new findings from our Qualtrics Methodology Lab are helping us to revisit and refine advice that is commonly given to survey researchers, namely the use of attention check questions to ensure data quality.
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Thinking of checking up on respondent attention mid-survey to make sure that you’re getting good data? Think again.