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EFFECT SIZE–DRIVEN SAMPLE-SIZE PLANNING, RANDOMIZATION, AND MULTISITE USE IN L2 INSTRUCTED VOCABULARY ACQUISITION EXPERIMENTAL SAMPLES

Published online by Cambridge University Press:  06 September 2021

Joseph P. Vitta Open the ORCID record for Joseph P. Vitta [Opens in a new window] ,
Christopher Nicklin Open the ORCID record for Christopher Nicklin [Opens in a new window]  and
Stuart McLean Open the ORCID record for Stuart McLean [Opens in a new window]
Joseph P. Vitta*
Affiliation:
Kyushu University
Christopher Nicklin
Affiliation:
Rikkyo University
Stuart McLean
Affiliation:
Momoyama Gakuin University
*
*Correspondence concerning this article should be addressed to Joseph P. Vitta, Kyushu University, Faculty of Languages and Cultures, Fukuoka, Japan. E-mail: [email protected] and [email protected]
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Abstract

In this focused methodological synthesis, the sample construction procedures of 110 second language (L2) instructed vocabulary interventions were assessed in relation to effect size–driven sample-size planning, randomization, and multisite usage. These three areas were investigated because inferential testing makes better generalizations when researchers consider them during the sample construction process. Only nine reports used effect sizes to plan or justify sample sizes in any fashion, with only one engaging in an a priori power procedure referencing vocabulary-centric effect sizes from previous research. Randomized assignment was observed in 56% of the reports while no report involved randomized sampling. Approximately 15% of the samples observed were constructed from multiple sites and none of these empirically investigated the effect of site clustering. Leveraging the synthesized findings, we conclude by offering suggestions for future L2 instructed vocabulary researchers to consider a priori effect size–driven sample planning processes, randomization, and multisite usage when constructing samples.

Type
Methods Forum
Information
Studies in Second Language Acquisition , Volume 44 , Issue 5 , December 2022 , pp. 1424 - 1448
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© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

The research synthesis analyses in this article earned Open Materials and Open Data badges for transparent practices. The materials and data are available at https://www.iris-database.org/iris/app/home/detail?id=york%3A939467&ref=search

The authors wish to acknowledge Joy Egbert, Luke Plonsky, Ali H. Al-Hoorie, Phil Hiver, and Dayna Jost for the expert reviews regarding the report pool creation process and for their external consultations throughout the project. The authors also wish to thank the SSLA editors and the two external reviewers who provided useful and constructive feedback on multiple versions of this manuscript.

†This article has been updated since its original publication. See https://doi.org/10.1017/S0272263121000814

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