Date of Award
Master of Arts (MA)
Dr. Joseph DiRenzo III
The presented research work examined the ways in which six verbal cues and fourteen non-verbal cues could be utilized as a means of detecting deception in individual-level cases. The focus of this research was the development of a model that would be useful for law enforcement and intelligence personnel who must detect deception on a timely, constant basis without reliance on technology. Through this research, a novel model “Determining and Evaluating Truth through Explicit Cue Testing (DETECT)” was developed. The work utilized a mixed-methods approach to analysis; thus, an analysis of the thirty participant interviews, in addition to a further non-parametric statistical analysis was conducted. Accordingly, it was recognized that the DETECT model was able to correctly identify deception/truthfulness in twenty-eight of the thirty cases. The research received explicit approval from the Institutional Review Board (IRB) prior to the commencement of the research. The work further proved that the model is statistically acceptable at a 5% significance level. Therefore, there is a 95% confidence interval for this model. This means the power of this model is 95%. The work concluded with the determination that whilst the model can be used without a background in the detection of deception, the undergoing of a six-week training course may help to raise the statistical probabilities of correctly determining the truthfulness of an individual’s statements.
de Silva, Eugenie, "The Development of the Determining and Evaluating Truth through Explicit Cue Testing (DETECT) Model to Detect Deception: Verbal and Non-Verbal Cues" (2014). Master's Capstone Theses. 2.
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