Optimizing CBCA and RM research: recommendations for analyzing and reporting data on content cues to deception

For more than a century, verbal content cues to deception have been investigated to assess the credibility of statements in judicial contexts. Among the many cues investigated, Criteria-based Content Analysis (CBCA) and criteria based on the reality monitoring (RM) approach have been most prominent....

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Bibliographische Detailangaben
1. VerfasserIn: Sporer, Siegfried Ludwig (VerfasserIn)
Beteiligte: Manzanero Puebla, Antonio Lucas ; Masip, Jaume
Medienart: Elektronisch Aufsatz
Sprache:Englisch
Veröffentlicht: 2021
In: Psychology, crime & law
Jahr: 2021, Band: 27, Heft: 1, Seiten: 1-39
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Schlagwörter:
CBCA
Criteria-based Content Analysis
Deception detection
Reality Monitoring
deception cues
Beschreibung
Zusammenfassung:For more than a century, verbal content cues to deception have been investigated to assess the credibility of statements in judicial contexts. Among the many cues investigated, Criteria-based Content Analysis (CBCA) and criteria based on the reality monitoring (RM) approach have been most prominent. However, research with these cues used as ‘tools’ has not fully exploited their potential. We critically discuss statistical approaches used in past research and recommend a series of 12 principles or guidelines researchers should follow to design, analyze and report future studies on detecting deception with verbal content cues. To illustrate some of these points, we present analyses from two separate studies: A quasi-experiment in a field setting conducted with adults with intellectual disabilities who truthfully or deceptively described a negative autobiographical event to an interviewer, and a large-scale simulation study where adults wrote an account of either an experienced or an invented significant life event. Accounts in both studies were rated with CBCA and RM criteria, as well as by ‘naive’ raters. The guidelines should help to increase the quality and transparency of research in this area.
ISSN:1477-2744
DOI:10.1080/1068316X.2020.1757097