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Title: Detecting semi-plausible response patterns
Author: Terzi, Tayfun
ISNI:       0000 0004 6349 532X
Awarding Body: London School of Economics and Political Science (LSE)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2017
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New challenges concerning bias from measurement error have arisen due to the increasing use of paid participants: semi-plausible response patterns (SpRPs). SpRPs result when participants only superficially process the information of (online) experiments/questionnaires and attempt only to respond in a plausible way. This is due to the fact that participants who are paid are generally motivated by fast cash, and try to efficiently overcome objective plausibility checks and process other items only superficially, if at all. Thus, those participants produce not only useless but detrimental data, because they attempt to conceal their malpractice. The potential consequences are biased estimation and misleading statistical inference. The statistical nature of specific invalid response strategies and applications are discussed, effectually deriving a meta-theory of response strategy, process, and plausibility. A new test measure to detect SpRPs was developed to accommodate data of survey type, without the need of a priori implemented mechanisms. Under a latent class latent variable framework, the effectiveness of the test measure was empirically and theoretically evaluated. The empirical evaluation is based on an experimental and online questionnaire study. These studies operate under a very well established psychological framework on five stable personality traits. The measure was theoretically evaluated through simulations. It was concluded that the measure is successfully discriminating between valid responders and invalid responders under certain conditions. Indicators for optimal settings of high discriminatory power were identified and limitations discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: HA Statistics