Using Statistical Regression Methods in Education Research
2.1 Overview
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Regression analysis refers to a set of techniques for predicting or explaining an outcome variable using one or more explanatory variables. It is essentially about creating a model for estimating one variable based on the values of others. Simple linear regression is regression analysis in its most basic form - it is used to predict a continuous (scale) outcome variable from one continuous explanatory variable. Simple Linear regression can be conceived as the process of drawing a line to represent an association between two variables on a scatterplot and using that line as a linear model for predicting the value of one variable (outcome) from the value of the other (explanatory variable). Don’t worry if this is somewhat baffling at this stage, it will become much clearer later when we start displaying bivariate data (data about two variables) using scatterplots! Correlation is excellent for showing association between two variables. Simple Linear regression takes correlation's ability to show the strength and direction of an association a step further by allowing the researcher to use the pattern of previously collected data to build a predictive model. Here are some examples of how this can be applied:
It is important to point out that there are limitations to regression. We can't always use it to analyse association. We'll start this module by looking at association more generally.
We're keen on training you with real world data so that you may be able to apply regression analysis to your own research. For this reason all of the examples we use come from the LSYPE and we provide an adapted version of the LYSPE dataset for you to practice with and to test your new found skills from. We recommend that you run through the examples we provide so that you can get a feel for the techniques and for SPSS/PASW in preparation for tackling the exercises. |
