Correlation and Simple Linear Regression in Applied Linguistics

Reza Norouzian, Luke Plonsky

Research output: Chapter in Book/Report/Conference proceedingChapter

10 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationThe Palgrave Handbook of Applied Linguistics Research Methodology
PublisherPalgrave Macmillan
Pages395-421
Number of pages27
ISBN (Electronic)9781137599001
ISBN (Print)9781137598998
DOIs
StatePublished - Jan 1 2018

Keywords

  • Correlation
  • Quantitative methods
  • Regression
  • Statistics

ASJC Scopus subject areas

  • Arts and Humanities(all)
  • Social Sciences(all)

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