Skip to main content
SpringerLink
Log in

Correlation and Simple Linear Regression in Applied Linguistics

  • Chapter
The Palgrave Handbook of Applied Linguistics Research Methodology

Abstract

This chapter provides an applied description of two key methods to evaluate the association between two research variables. First, we provide a conceptual view of the notion of non-directional linear correlation. Using small datasets, we discuss the various behaviors of the correlation statistic, Pearson’s r, under different scenarios. Then, we turn our attention to a neighboring but practically different concept to evaluate the directional association between two research variables: the simple linear regression. Particularly, we shed light on one of the most useful purposes of simple linear regression and prediction. By end of the chapter, we present a conceptually overarching view that links the regression methods to all other methods that applied linguists often use to find important patterns in their data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Cohen, J. (1968). Multiple regression as a general data-analytic system. Psychological Bulletin, 70, 426–443.

    Article  Google Scholar 

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. New York, NY: Routledge.

    Google Scholar 

  • DeKeyser, R. (2000). The robustness of critical period effects in second language acquisition. Studies in Second Language Acquisition, 22, 499–533.

    Google Scholar 

  • De Winter, J. C., Gosling, S. D., & Potter, J. (2016). Comparing the Pearson and spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological methods, 21, 273–290.

    Article  Google Scholar 

  • Draper, N. R., & Smith, H. (1998). Applied regression analysis. New York, NY: Wiley.

    Google Scholar 

  • Egbert, J., & Plonsky, L. (2015). Success in the abstract: Exploring linguistic and stylistic predictors of conference abstract ratings. Corpora, 10, 291–313.

    Article  Google Scholar 

  • Field, A. (2013). Discovering statistics using IBM SPSS statistics. Thousand Oaks, CA: SAGE.

    Google Scholar 

  • Graybill, F. A., & Iyer, H. K. (1994). Regression analysis. New York, NY: Duxbury Press.

    Google Scholar 

  • Howell, D. C. (2013). Statistical methods for psychology. Belmont, CA: Cengage Learning.

    Google Scholar 

  • Johnson, J. S., & Newport, E. L. (1989). Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21(1), 60–99.

    Article  Google Scholar 

  • Kline, R. B. (2015). Principles and practice of structural equation modeling. New York, NY: Guilford.

    Google Scholar 

  • Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models. New York, NY: McGraw-Hill.

    Google Scholar 

  • Norouzian, R., & Plonsky, L. (2018). Eta- and partial eta-squared in L2 research: A cautionary review and guide to more appropriate usage. Second Language Research, 34, 257–271.

    Article  Google Scholar 

  • Norris, J. M. (2015). Statistical significance testing in second language research: Basic problems and suggestions for reform. Language Learning, 65(Supp. 1), 97–126.

    Article  Google Scholar 

  • Norris, J. M., Ross, S., & Schoonen, R. (2015). Improving second language quantitative research. Language Learning, 65(Supp. 1), 1–8.

    Article  Google Scholar 

  • Pearson, K. (1896). Mathematical contributions to the theory of evolution. III. Regression, heredity, and panmixia. Philosophical Transactions A, 373, 253–318.

    Article  Google Scholar 

  • Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for the social sciences: Analyses with SAS and IBM’s SPSS. New York, NY: Routledge.

    Google Scholar 

  • Plonsky, L. (2013). Study quality in SLA: An assessment of designs, analyses, and reporting practices in quantitative L2 research. Studies in Second Language Acquisition, 35, 655–687.

    Article  Google Scholar 

  • Plonsky, L. (Ed.). (2015a). Advancing quantitative methods in second language research. New York, NY: Routledge.

    Google Scholar 

  • Plonsky, L. (2015b). Statistical power, p values, descriptive statistics, and effect sizes: A “back-to-basics” approach to advancing quantitative methods in L2 research. In L. Plonsky (Ed.), Advancing quantitative methods in second language research (pp. 23–45). New York, NY: Routledge.

    Chapter  Google Scholar 

  • Plonsky, L., & Derrick, D. J. (2016). A Meta-Analysis of Reliability Coefficients in Second Language Research. Modern Language Journal, 100, 538–553.

    Article  Google Scholar 

  • Plonsky, L., & Ghanbar, H. (in press). Multiple regression in L2 research: A methodological synthesis and guide to interpreting R values. Modern Language Journal.

    Google Scholar 

  • Plonsky, L., & Oswald, F. L. (2014). How big is ‘big’? Interpreting effect sizes in L2 research. Language Learning, 64, 878–912.

    Article  Google Scholar 

  • Plonsky, L., & Oswald, F. L. (2017). Multiple regression as a flexible alternative to ANOVA in L2 research. Studies in Second Language Acquisition, 39, 579–592.

    Article  Google Scholar 

  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: SAGE.

    Google Scholar 

  • Roever, C., & Phakiti, A. (2017). Quantitative methods for second language research: A problem-solving approach. New York, NY: Routledge.

    Book  Google Scholar 

  • Rosnow, R. L., & Rosenthal, R. (2008). Essentials of behavioral research: Methods and data analysis. New York, NY: McGraw-Hill.

    Google Scholar 

  • Schoonen, R. (2015). Structural equation modeling in L2 research. In L. Plonsky (Ed.), Advancing quantitative methods in second language research (pp. 213–242). New York, NY: Routledge.

    Chapter  Google Scholar 

  • Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, DC: American Psychological Association.

    Book  Google Scholar 

  • Thompson, B. (Ed.). (2003). Score reliability: Contemporary thinking on reliability issues. Newbury Park, CA: SAGE.

    Google Scholar 

  • Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Wilcox, R. (2016). Understanding and applying basic statistical methods using R. New York, NY: Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Texas A&M University, College Station, Texas, USA

    Reza Norouzian

  2. Northern Arizona University, Flagstaff, AZ, USA

    Luke Plonsky

Authors
  1. Reza Norouzian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luke Plonsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luke Plonsky .

Editor information

Editors and Affiliations

  1. Sydney School of Education and Social Work, University of Sydney, Sydney, NSW, Australia

    Aek Phakiti

  2. Department of Linguistics, Germanic, Slavic, Asian and African Languages, Michigan State University, East Lansing, MI, USA

    Peter De Costa

  3. Applied Linguistics, Northern Arizona University, Flagstaff, AZ, USA

    Luke Plonsky

  4. School of Education, UNSW Sydney, Sydney, NSW, Australia

    Sue Starfield

Copyright information

© 2018 The Author(s)

About this chapter

Cite this chapter

Norouzian, R., Plonsky, L. (2018). Correlation and Simple Linear Regression in Applied Linguistics. In: Phakiti, A., De Costa, P., Plonsky, L., Starfield, S. (eds) The Palgrave Handbook of Applied Linguistics Research Methodology. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-59900-1_19

Download citation

  • DOI: https://doi.org/10.1057/978-1-137-59900-1_19

  • Publisher Name: Palgrave Macmillan, London

  • Print ISBN: 978-1-137-59899-8

  • Online ISBN: 978-1-137-59900-1

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics

Search

Navigation