Coretta, S;
Casillas, JV;
Roessig, S;
Franke, M;
Ahn, B;
Al-Hoorie, AH;
Al-Tamimi, J;
... Lima, RM; + view all
(2023)
Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses.
Advances in Methods and Practices in Psychological Science
, 6
(3)
pp. 1-29.
10.1177/25152459231162567.
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Abstract
Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions.
| Type: | Article |
|---|---|
| Title: | Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/25152459231162567 |
| Publisher version: | https://doi.org/10.1177/25152459231162567 |
| Language: | English |
| Additional information: | This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/), which permits noncommercial use, reproduction, and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
| Keywords: | Crowdsourcing science, data analysis, scientific transparency, speech, acoustic analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Speech, Hearing and Phonetic Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174970 |
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