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Doing L2 Speech Research Online

Why and How to Collect Online Ratings Data

Bibliographic Data

ID21506345
AuthorsChristopher L Nagle (0000-0003-2712-2705, Iowa State University, corresponding author), Ivana Rehman (0000-0002-8590-5401, Iowa State University)
Year2021
Volume43
Issue4
Pages916-939
Publication date2021-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueStudies in Second Language Acquisition (JOURNAL)
Journal identifiersISSN: 0272-2631 • E-ISSN: 1470-1545
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s0272263121000292
OpenAlexW3172685660
LanguageEN
Citations received14
References cited36

Listener-based ratings have become a prominent means of defining second language (L2) users’ global speaking ability. In most cases, local listeners are recruited to evaluate speech samples in person. However, in many teaching and research contexts, recruiting local listeners may not be possible or advisable. The goal of this study was to hone a reliable method of recruiting listeners to evaluate L2 speech samples online through Amazon Mechanical Turk (AMT) using a blocked rating design. Three groups of listeners were recruited: local laboratory raters and two AMT groups, one inclusive of the dialects to which L2 speakers had been exposed and another inclusive of a variety of dialects. Reliability was assessed using intraclass correlation coefficients, Rasch models, and mixed-effects models. Results indicate that online ratings can be highly reliable as long as appropriate quality control measures are adopted. The method and results can guide future work with online samples

Developmental psychology · Intraclass correlation · Psychometrics · Rasch model · Rating scale · Applied Psychology · Computer Science · Linguistic Variation and Morphology · Phonetics and Phonology Research · Psychology · Speech and dialogue systems · Artificial Intelligence

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Unique citing works14
Citations per year4,67
Citation span2023 - 2026 (4)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 14
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