Abstract
Comparing the scores of groups is one of the most basic and common analyses in quantitative research in applied linguistics. T-tests answer a very basic research question ‘Is there a difference between two groups of data?’ This chapter looks at writing about different types of t-tests. T-tests are often used in conjunction with other analyses to find out whether there are differences between the groups of participants in a study. This chapter considers the following:
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Technical information
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Independent samples t-test
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Paired samples t-tests
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Single sample t-test
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Assumptions of t-tests
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Reporting effect sizes
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Examples
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Calculating effect sizes
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Assumption testing t-test
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Independent samples t-test
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Paired samples t-test
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Effect sizes for t-tests
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One sample t-test
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Post hoc t-test
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Further reading
Field, A. (2013). Discovering statistics using SPSS (4th ed.). London: Sage.
Pallant, J. (2010). SPSS survival manual. Maidenhead: Open University.
Sources of examples
Gardner, R., Masgoret, A.-M., & Tennant, J. (2004). Integrative motivation: Changes during a year-long intermediate-level language course. Language Learning, 54(1), 1–34. doi: 10.1111/j.1467-9922.2004.00247.x.
Taylor, C., & Lafayette, R. (2010). Academic achievement through FLES: A case for promoting greater access to foreign language study among young learners. Modem Language Journal, 94(1), 22–42. doi: 10.1111/j.1540-4781.2009.00981.x.
Webb, S., Newton, J., & Cheng, A. (2013). Incidental learning of collocation. Language Learning, 62(1), 91–120.
Author information
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Copyright information
© 2014 Lindy Woodrow
About this chapter
Cite this chapter
Woodrow, L. (2014). Writing about T-tests. In: Writing about Quantitative Research in Applied Linguistics. Palgrave Macmillan, London. https://doi.org/10.1057/9780230369955_6
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DOI: https://doi.org/10.1057/9780230369955_6
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-0-230-36997-9
Online ISBN: 978-0-230-36995-5
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