Full Description
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. While a number of texts discuss basic quantitative research methodology, none focus exclusively on providing coverage of alternative advanced statistical procedures in second language studies from a practical approach. The text is bookended by discussions of these advanced procedures in the larger context of second language studies, debating their strengths, weaknesses, and potential for further research; the remaining chapters are how-to sections, each chapter following the same organization, 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.
For additional content, visit: https://lukeplonsky.wordpress.com/aqmslr/
Contents
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
