Using meta-analysis for evidence synthesis: The case of incomplete neutralization in German. (September 2018)

• Record Type: Journal Article
• Authors: Nicenboim, Bruno; Roettger, Timo B.; Vasishth, Shravan
• Is Part Of: Journal of phonetics. Volume 70(2018)
• Page Start: 39
• Page End: 55
• Publication Date: 2018-09
• Subjects: Meta-analysis -- Incomplete neutralization -- Final devoicing -- German -- Bayesian data analysis; Phonetics -- Periodicals; Phonetics -- Periodicals; Phonétique -- Périodiques; Phonetics; Periodicals; Electronic journals; 414.05
• DOI: 10.1016/j.wocn.2018.06.001
• Languages: English
• ISSNs: 0095-4470

Abstract

Highlights: A systematic literature review of incomplete neutralization in German. A discussion of common statistical misconceptions in the phonetic sciences. A Bayesian random-effects meta-analysis of incomplete neutralization. Suggestions for improving the quality of future research in phonetics. Abstract: Within quantitative phonetics, it is common practice to draw conclusions based on statistical significance alone. Using incomplete neutralization of final devoicing in German as a case study, we illustrate the problems with this approach. If researchers find a significant acoustic difference between voiceless and devoiced obstruents, they conclude that neutralization is incomplete; and if they find no significant difference, they conclude that neutralization is complete. However, such strong claims regarding the existence or absence of an effect based on significant results alone can be misleading. Instead, the totality of available evidence should be brought to bear on the question. Towards this end, we synthesize the evidence from 14 studies on incomplete neutralization in German using a Bayesian random-effects meta-analysis. Our meta-analysis provides evidence in favor of incomplete neutralization. We conclude with some suggestions for improving the quality of future research on phonetic phenomena: ensure that sample sizes allow for high-precision estimates of the effect; avoid the temptation to deploy researcher degrees of freedom when analyzing data; focus on estimates of the parameter of interest and the uncertainty about that parameter; attempt to replicate effects found; and, whenever possible, make both the data and analysis available publicly.