Correlation and Simple Linear Regression in Applied Linguistics

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.