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Download Advancing Quantitative Methods in Second Language Research by Luke Plonsky PDF

By Luke Plonsky

Advancing Quantitative equipment in moment Language Research is the 1st hands-on advisor to carrying out complex learn tools within the fields of utilized linguistics and moment language stories. whereas a couple of texts talk about easy quantitative learn technique, none concentration completely on supplying insurance of different complex statistical tactics in moment language reviews from a pragmatic procedure. The textual content is bookended through discussions of those complicated approaches within the greater context of moment language stories, debating their strengths, weaknesses, and capability for additional learn; the remainder chapters are how-to sections, every one bankruptcy following an identical association, on a wide selection of complicated learn equipment. via supplying much-needed insurance on complex statistical innovations and systems, with an eye fixed towards real-world implementation, Advancing Quantitative tools in moment Language examine enhances the methodological repertoire of graduate scholars and researchers in utilized linguistics and moment language studies.

For extra content material, stopover at: http://oak.ucc.nau.edu/ldp3/AQMSLR.html

 

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B. (2006). Structural Equation Modeling: Reviewing the basics and moving forward. Journal of Personality Assessment, 87(1), 35–50. PART€II Enhancing Existing Quantitative Methods This page intentionally left blank 3 STATISTICAL POWER, P VALUES, DESCRIPTIVE STATISTICS, AND EFFECT€SIZES A “BACK-TO-BASICS” APPROACH TO ADVANCING QUANTITATIVE METHODS IN L2 RESEARCH Luke Plonsky Introduction Methodologically speaking, a great deal of quantitative L2 research has been misguided. All too often we have been asking the wrong questions of our data.

If not, then defending the scores as an interval scale may not be justified. One solution to that problem is to use an advanced statistical technique called Rasch analysis. This form of analysis can help you analyze and improve any raw-score interval scales you use, but also as a byproduct, you can use Rasch analyses to convert those raw scores into logit scores which arguably form a true interval scale. There are a number of other reasons why you might want to use Rasch analysis to better understand your scales and how precisely you are measuring the variables in your studies (Knoch€& McNamara, Chapter€12 in this volume).

Another option for categorical data is a simple percentage. Though not traditionally regarded as an effect size, percentages certainly comply with our earlier definition and, more importantly, they are very easy both to calculate and to interpret. A€final effect size commonly used with categorical data is the odds ratio.

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