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Research Designs

Research Designs. Sebastian M. Rasinger Quantitative Research in Linguistics. 2e 2013 London: Bloomsbury. The research process. Research Area. Research Question. Quantitative data. can be put into numbers can be used for statistic analysis NOT necessarily ‘much data’ Deductive:

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Research Designs

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  1. Research Designs Sebastian M. Rasinger Quantitative Research in Linguistics. 2e 2013 London: Bloomsbury S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  2. The research process Research Area Research Question S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  3. Quantitative data • can be put into numbers • can be used for statistic analysis • NOT necessarily ‘much data’ • Deductive: theory  hypotheses  data  confirm/reject H • Examples of quantitative variables • people’s age • test scores S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  4. Qualitative data • ‘words’, ‘text’, ‘qualities’, ‘patterns’ • how is something, rather than howmuch • interpretative • inductive: theory is derived from the research results S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  5. S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  6. S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  7. Research Designs • Longitudinal vs. cross-sectional • Change vs. status quo • Experimental vs. natural • Deliberate manipulation of variable/s? • (vs. quasi-experimental) S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  8. Data – some practicalities • How much data? • As much as you need to make a coherent argument… • No need to prove beyond reasonable doubt • Does your data ‘make sense’ given the topic and previous theory S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  9. Data – some practicalities (2) • Whatever method you use: pilot it!! • What’s going well? • What’s going horribly wrong? • Why have things gone wrong? Is it your instrument? Is it you? Is it both? • Adjust accordingly and, ideally, pilot again S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  10. Causality • A causes B • Notoriously difficult to prove!! • Variables A and B must correlate with each other, that is, their values must co-occur in a particular pattern: for example, the older a speaker, the more dialect features you find in their speech. • There must be a temporal relationship between the two variables A and B, that is, B must occur after A. • The relationship between A and B must not disappear when controlled for a third variable. S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  11. Reliability and Validity • Reliability: Does you method repeatedly measure what it’s supposed to measure? I.e. if you use the method under exactly the same circumstances, do you get the same result? • Split-half • Test-retest • Validity • Does you method measure what it’s supposed to measure? S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

  12. Samples and Sampling • Part of population (P). Population = all people who show certain characteristics (e.g. year 2 undergraduate students at British universities) • Reflection of P, i.e. characteristics equal those of P • Ideally: random sample. Every member of P has equal chance to be member of sample • Realistically: opportunistic sample. Take what you can get. But check that it’s not completely off P  use personal judgement S.M.Rasinger. 2013. Quantitative Research in Linguistics. 2e. Bloomsbury.

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