Response Process Validation Protocol using Neurophenomenological Gamma Asymmetry

Authors

  • Ronald J Bonnstetter University of Nebraska-Lincoln Target Training International
  • Eric Gehrig Target Training International
  • Dustin Hebets Target Training International

DOI:

https://doi.org/10.15540/nr.5.3.93

Keywords:

gamma asymmetry, response process validity, industrial/organizational psychology, organizational neuroscience, sLORETA, approach-avoidance, data triangulation

Abstract

In the process of designing a continuous improvement process for a set of personal attribute  self-reporting assessments, an opportunity was seen to combine assessment analytics with brain activity to capture decision-making pathways while responding to assessment items. This pilot triangulation process is designed to address response process validation, as described by The American Psychological Association to begin to better understand whether the responder is following the intended purpose of the assessment.  Method: The protocol involves collecting electroencephalographic (EEG) data, using standardized low-resolution brain electromagnetic tomography (sLORETA) to analyze and view voxel images of real-time brain activity collected while a participant responds to assessment items. This analysis examines gamma asymmetry in the frontal lobes, as well as opening the door to further wave comparisons in the future. Conclusions: The protocols used to expose the mindset of assessment responders will be shared, as well as pilot insights gained as a result of this imaging process. By collecting images from the moment of stimulus exposure to the moment that the respondent selects a likert scale answer, insights are gathered that include: how final answers compare to brain processing data, brain decision-making pathways when exposed to reverse or double negative assessment  items, exposure of brain processing when faced with socially loaded statements, resulting brain processing of neutrally scored  stimulus, and insights gained when all of this data is cross-walked against quantitative item analysis of population data such as inter-item correlations and item factor loading based on exploratory factor analysis.

References

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Published

2018-09-29

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Section

Technical Notes