Exploring acquiescence response bias using digital footprints

Research output: Contribution to conferenceAbstract

Abstract

Acquiescence response bias is the tendency to agree to personality questionnaires irrespective of item content or direction. Acquiescence is problematic for both researchers and clinicians (Rammstedt & Farmer, 2013), and yet is mostly ignored or treated as meaningless error. While acquiescence is known to be shaped by both age and education (Costello, 2015), little more is known about the behavioural implications of acquiescence. Using a sample of 20899 US adults from the myPersonality project who completed the 100-item IPIP inventory, the current study explored the extent to which social media behaviours (Facebook likes) could predict acquiescence using machine learning. Acquiescence scores were calculated using matched item pairs (Hofstee, ten Berge & Hendriks, 1998). Using singular value decomposition (SVD), linear regression, and cross validation, the optimal prediction accuracy with 27 SVD dimensions was .31. This suggests that acquiescence is multidimensional, can be predicted using digital behaviours, and therefore likely plays a role in everyday behaviour. Implications for both researchers and clinicians are explored.
Original languageEnglish
Number of pages1
Publication statusPublished - 2017
EventAustralian Conference on Personality and Individual Differences 2017 - Sydney, Australia
Duration: 1 Dec 20172 Dec 2017

Conference

ConferenceAustralian Conference on Personality and Individual Differences 2017
Abbreviated titleACPID 2017
CountryAustralia
CitySydney
Period1/12/172/12/17

Keywords

  • Personality
  • Machine learning
  • Big data
  • Acquiescence

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