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29 - Statistical Modeling in L3/Ln Acquisition

from Part VI - Research Methods in L3/Ln

Published online by Cambridge University Press:  13 July 2023

Jennifer Cabrelli
Affiliation:
University of Illinois, Chicago
Adel Chaouch-Orozco
Affiliation:
The Hong Kong Polytechnic University
Jorge González Alonso
Affiliation:
Universidad Nebrija, Spain and UiT, Arctic University of Norway
Sergio Miguel Pereira Soares
Affiliation:
Max Planck Institute for Psycholinguistics
Eloi Puig-Mayenco
Affiliation:
King's College London
Jason Rothman
Affiliation:
UiT, Arctic University of Norway and Universidad Nebrija, Spain
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Summary

This chapter introduces Bayesian data analysis and shows how such an approach can better deal with the intricacies of Ln acquisition data. The data analyzed is simulated based on Rothman’s (2010) study to demonstrate how we can use Bayesian models to estimate variables of interest in R. The chapter discusses (1) how to tackle smaller sample sizes and (2) how to incorporate theoretical principles and assumptions into our statistical analysis. As will be shown, in addition to its general advantages, Bayesian data analysis provides an effective toolset to address both (1) and (2). It also offers a much more nuanced and comprehensive approach to meet the methodological needs in the field.

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Publisher: Cambridge University Press
Print publication year: 2023

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