Advancing Quantitative Methods in Second Language Research is the first hands-on guide to conducting advanced research methods in the fields of applied linguistics and second language studies. The text is bookended by discussions of these advanced procedures in the larger context of second language studies, with the main chapters serving as how-to sections on a wide variety of advanced research methods. By offering much-needed coverage on advanced statistical concepts and procedures, with an eye toward real-world implementation, Advancing Quantitative Methods in Second Language Research enhances the methodological repertoire of graduate students and researchers in applied linguistics and second language studies.

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Luke Plonsky (PhD, Michigan State University) is a faculty member in the Applied Linguistics program at Northern Arizona University. His interests include SLA and research methods, and his publications in these and other areas have appeared in Annual Review of Applied Linguistics, Applied Linguistics, Language Learning, Modern Language Journal, and Studies in Second Language Acquisition, among other major journals and outlets. He is also Associated Editor of Studies in Second Language Acquisition and Managing Editor of Foreign Language Annals.

Part I: Introduction 1. Introduction Luke Plonsky 2. The place and value of advanced quantitative methods in L2 research JD Brown Part II: Enhancing Existing Quantitative Methods 3. Statistical power, p values, descriptive statistics, and effect sizes: A "back-to-basics" approach to advancing quantitative methods in L2 research Luke Plonsky 4. A practical guide to bootstrapping descriptive statistics, correlations, t tests, and ANOVAs Geoffrey T. LaFlair, Jesse Egbert, & Luke Plonsky 5. Presenting quantitative data visually Thom Hudson 6. Meta-analysis Luke Plonsky & Frederick L. Oswald Part III: Advanced and Multivariate Methods 7. Multiple Regression Eun Hee Jeon 8. Mixed effects modeling and longitudinal data analysis Ian Cunnings & Ian Finlayson 9. Principal components analysis & factor analysis Shawn Loewen & Talip Gonulal 10. Structural equation modeling Rob Schoonen 11. Cluster analysis Douglas Biber & Shelley Staples 12. Rasch analysis Ute Knoch & Tim McNamara 13. Discriminant function analysis John M. Norris 14. Bayesian data analysis Beth Mackey & Steven Ross