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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Reply to Mac Giolla and Ly (2019)
T2 - On the reporting of Bayes Factors in Deception Research
AU - McLatchie, Neil
AU - Warmelink, Lara
AU - Tkacheva, Daria
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Bayes factors help researchers by providing a continuous measure of evidence for one hypothesis (e.g., the null, H0) relative to another (e.g., the alternative, H1). Warmelink, Subramanian, Tkacheva and McLatchie (2019) reported Bayes factors alongside p-values to draw inferences about whether the order of expected versus unexpected questions influenced the amount of details interviewees provided during an interview. Mac Giolla & Ly (2019) provided several recommendations to improve the reporting of Bayesian analyses, and used Warmelink et al (2019) as a concrete example. These included (I) not to over-rely on cut-offs when interpreting Bayes factors; (II) to rely less on Bayes factors, and switch to “nominal support”; and (III) to report the posterior distribution. This paper elaborates on their recommendations and provides two further suggestions for improvement. First, we recommend deception researchers report Robustness Regions to demonstrate the sensitivity of their conclusions. Second, we encourage deception researchers to estimate a priori the sample size likely to be required to produce conclusive results.
AB - Bayes factors help researchers by providing a continuous measure of evidence for one hypothesis (e.g., the null, H0) relative to another (e.g., the alternative, H1). Warmelink, Subramanian, Tkacheva and McLatchie (2019) reported Bayes factors alongside p-values to draw inferences about whether the order of expected versus unexpected questions influenced the amount of details interviewees provided during an interview. Mac Giolla & Ly (2019) provided several recommendations to improve the reporting of Bayesian analyses, and used Warmelink et al (2019) as a concrete example. These included (I) not to over-rely on cut-offs when interpreting Bayes factors; (II) to rely less on Bayes factors, and switch to “nominal support”; and (III) to report the posterior distribution. This paper elaborates on their recommendations and provides two further suggestions for improvement. First, we recommend deception researchers report Robustness Regions to demonstrate the sensitivity of their conclusions. Second, we encourage deception researchers to estimate a priori the sample size likely to be required to produce conclusive results.
KW - Deception
KW - Bayes factors
U2 - 10.1111/lcrp.12177
DO - 10.1111/lcrp.12177
M3 - Journal article
VL - 25
SP - 72
EP - 79
JO - Legal and Criminological Psychology
JF - Legal and Criminological Psychology
SN - 1355-3259
IS - 2
ER -