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An overview of mixed-effects Statistical models for Second language researchers
Second Language Research
Vol. 28, No. 3 (July 2012), pp. 369-382
Published by: Sage Publications, Ltd.
Stable URL: http://www.jstor.org/stable/43103901
Page Count: 14
You can always find the topics here!Topics: Statistical models, Modeling, Syntactic models, Sentences, Statistical variance, Language, Research methods, Multilevel models, Data models, P values
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As in any field of scientific inquiry, advancements in the field of second language acquisition (SLA) rely in part on the interpretation and generalizability of study findings using quantitative data analysis and inferential statistics. While statistical techniques such as ANOVA and t-tests are widely used in second language research, this review article provides a review of a class of newer statistical models that have not yet been widely adopted in the field, but have garnered interest in other fields of language research. The class of statistical models called mixed-effects models are introduced, and the potential benefits of these models for the second language researcher are discussed. A simple example of mixed-effects data analysis using the statistical software package R (R Development Core Team, 2011) is provided as an introduction to the use of these statistical techniques, and to exemplify how such analyses can be reported in research articles. It is concluded that mixed-effects models provide the second language researcher with a powerful tool for the analysis of a variety of types of second language acquisition data.
Second Language Research © 2012 Sage Publications, Ltd.