Validity of content‐based techniques for credibility assessment—How telling is an extended meta‐analysis taking research bias into account?

Saved in:

Bibliographic Details
Title:	Validity of content‐based techniques for credibility assessment—How telling is an extended meta‐analysis taking research bias into account?
Authors:	Oberlader, Verena A. (AUTHOR), Quinten, Laura (AUTHOR), Banse, Rainer (AUTHOR), Volbert, Renate (AUTHOR), Schmidt, Alexander F. (AUTHOR), Schönbrodt, Felix D. (AUTHOR)
Source:	Applied Cognitive Psychology. Mar2021, Vol. 35 Issue 2, p393-410. 18p. 1 Diagram, 5 Charts, 2 Graphs.
Subjects:	Research bias, Fix-point estimation, Content analysis
Abstract:	Summary: Content‐based techniques for credibility assessment (Criteria‐Based Content Analysis [CBCA], Reality Monitoring [RM]) have been shown to distinguish between experience‐based and fabricated statements in previous meta‐analyses. New simulations raised the question whether these results are reliable revealing that using meta‐analytic methods on biased datasets lead to false‐positive rates of up to 100%. By assessing the performance of and applying different bias‐correcting meta‐analytic methods on a set of 71 studies we aimed for more precise effect size estimates. According to the sole bias‐correcting meta‐analytic method that performed well under a priori specified boundary conditions, CBCA and RM distinguished between experience‐based and fabricated statements. However, great heterogeneity limited precise point estimation (i.e., moderate to large effects). In contrast, Scientific Content Analysis (SCAN)—another content‐based technique tested—failed to discriminate between truth and lies. It is discussed how the gap between research on and forensic application of content‐based credibility assessment may be narrowed. [ABSTRACT FROM AUTHOR]
	Copyright of Applied Cognitive Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Psychology and Behavioral Sciences Collection

Description
Abstract:	Summary: Content‐based techniques for credibility assessment (Criteria‐Based Content Analysis [CBCA], Reality Monitoring [RM]) have been shown to distinguish between experience‐based and fabricated statements in previous meta‐analyses. New simulations raised the question whether these results are reliable revealing that using meta‐analytic methods on biased datasets lead to false‐positive rates of up to 100%. By assessing the performance of and applying different bias‐correcting meta‐analytic methods on a set of 71 studies we aimed for more precise effect size estimates. According to the sole bias‐correcting meta‐analytic method that performed well under a priori specified boundary conditions, CBCA and RM distinguished between experience‐based and fabricated statements. However, great heterogeneity limited precise point estimation (i.e., moderate to large effects). In contrast, Scientific Content Analysis (SCAN)—another content‐based technique tested—failed to discriminate between truth and lies. It is discussed how the gap between research on and forensic application of content‐based credibility assessment may be narrowed. [ABSTRACT FROM AUTHOR]
ISSN:	08884080
DOI:	10.1002/acp.3776