Shawn Loewen, Luke Plonsky's An A-Z of applied linguistics research methods PDF

By Shawn Loewen, Luke Plonsky

"A short and available advent to the strategies and strategies utilized in utilized linguistics learn, to be able to be illustrated utilizing real-life examples. The ebook covers either qualitative and quantitative learn layout, sampling methods, instrumentation and analyses present in utilized linguistics research"--

summary: "A short and available creation to the ideas and methods utilized in utilized linguistics learn, with a purpose to be illustrated utilizing real-life examples. The e-book covers either qualitative and quantitative study layout, sampling approaches, instrumentation and analyses present in utilized linguistics examine"

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I dislike it when I am corrected in class. Strongly Disagree 1 2 3 4 5 Strongly Agree Brown, J. D. (2014). Classical theory reliability. In A. J. ), The companion to language assessment. Oxford: Wiley Blackwell. Douglas, D. (2001). Performance consistency in second language acquisition and language testing: A conceptual gap. Second Language Research, 17, 442–456. , & Chen, X. (2009). Second language learners’ beliefs about grammar instruction and error correction. Modern Language Journal, 93, 91–104.

Examples of common categorical variables in applied linguistics are L1 background and L 2 target language, each containing potential categories such as English, Chinese, Spanish, and so forth. Figure 6, based on data from Loewen et al. (2014), shows the responses to a categorical variable where participants had to select which academic field of study they most identified with. Thus, the category with the most frequent responses was SLA, with Applied Linguistics being the next closest category. SLA Applied Linguistics TESOL Linguistics Foreign Languages 0 Figure 6 C 20 40 60 80 100 120 140 Categorical data [Participants’ academic field of study] It is not possible to perform many types of statistical procedures on categorical variables because they do not have mathematical properties such as a mean or standard deviation.

Cohen’s d An effect size used to express the magnitude of difference between two mean scores. Because of the frequency of means-based comparisons in applied linguistics such as t-tests and ANOVAs, this effect size is appropriate in a wide variety of contexts of quantitative research. As shown in the formula below, Cohen’s d is calculated by dividing the difference between two mean scores by their (pooled) standard deviation (SD). ) The SD in the denominator of this formula acts as a kind of standardizer.

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