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Book Chapter

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Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Publication Details

“Correlation and Regression”, in Approaches to Quantitative Research – A Guide for Dissertation Students, Ed, Chen, H, Oak Tree Press. 2012

Abstract

A correlation is a measure of the linear relationship between two variables. It is used when a
researcher wishes to describe the strength and direction of the relationship between two
normally continuous variables. The statistic obtained is Pearson’s product-moment
correlation (r), and SPSS also provides the statistical significance of r. In addition, if the
researcher needs to explore the relationship between two variables while statistically
controlling for a third variable, partial correlation can be used. This is useful when it is
suspected that the relationship between two variables may be influenced, or confounded, byA correlation is a measure of the linear relationship between two variables. It is used when a
researcher wishes to describe the strength and direction of the relationship between two
normally continuous variables. The statistic obtained is Pearson’s product-moment
correlation (r), and SPSS also provides the statistical significance of r. In addition, if the
researcher needs to explore the relationship between two variables while statistically
controlling for a third variable, partial correlation can be used. This is useful when it is
suspected that the relationship between two variables may be influenced, or confounded, by the impact of a third variable. Correlations are a very useful research tool but they do not address the predictive
power of variables. This task is left to regression. Regression is based on the idea that the
researcher must first have some valid reasons for believing that there is a causal relationship
between two or more variables. A well known example is the consumer demand for products
and the level of income of consumers. If income increases then demand for normal goods
such as cars, foreign travel will increase. In regression analysis, a predictive model needs to
fit to both the data and the model. And then we can use the result to predict values of the
dependent variable (DV) from one or more independent variables (IVs). In straight forward 2
terms, simple regression seeks to predict an outcome from a single predictor; whereas2
terms, simple regression seeks to predict an outcome from a single predictor; whereas multiple regression seeks to predict an outcome from several predictors.multiple regression seeks to predict an outcome from several predictors.

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