Using Markov Chains to Detect Careless Responding in Survey Research

Abstract

Careless responses by survey participants threaten data quality and lead to misleading substantive conclusions that result in theory and practice derailments. Prior research developed valuable precautionary and post-hoc approaches to detect certain types of careless responding. However, existing approaches fail to detect certain repeated response patterns, such as diagonal-lining and alternating responses. Moreover, some existing approaches risk falsely flagging careful response patterns. To address these challenges, we developed a methodological advancement based on first-order Markov chains called Lazy Respondents (Laz.R) that relies on predicting careless responses based on prior responses. We analyzed two large datasets and conducted an experimental study to compare careless responding indices to Laz.R and provide evidence that its use improves validity. To facilitate the use of Laz.R, we describe a procedure for establishing sample-specific cutoff values for careless respondents using the “kneedle algorithm” and make an R Shiny application available to produce all calculations. We expect that using Laz.R in combination with other approaches will help mitigate the threat of careless responses and improve the accuracy of substantive conclusions in future research.