From Volume 5, Issue 4, October 2012 | Pages 113-117
There is an increasing volume of research undertaken within orthodontics and with this comes a need to evaluate what is available. This short series aims to help the orthodontist revise basic concepts of critical appraisal and pertinent statistics.
Correlation is concerned with the strength of linear association between two variables measured on the same individual. By convention, the variables are plotted on a scatterplot so that the independent (explanatory) variable is on the x-axis and the dependent (response) variable is on the y-axis (Figure 1).
A correlation coefficient can be calculated to describe the magnitude and direction of any linear association, but it cannot suggest whether the relationship is causal. Hypothesis tests for correlation can be undertaken and a p-value may be displayed in the results: the null hypothesis presumes there is no linear association whatsoever. Both parametric and non-parametric versions of correlation coefficient exist:
Regression models can be used to describe the relationship between two or more variables: x (the independent variable/s) and y (the dependent variable) via an equation (Box 1).
The independent explanatory variables may be numeric, categoric or a mixture. The appropriate regression model to use depends on the type of the outcome variable:
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