ABSTRACT
In this chapter, you will learn how to compute several associational statistics. First, you will learn how to make scatterplots and how to interpret them. An assumption of the Pearson product moment correlation is that the variables are related in a linear (straight line) way so we will examine the scatterplots to see if that assumption is reasonable. Second, the Pearson correlation, and the Spearman rho will be computed. The Pearson correlation is used when you have two variables that are normal/scale, and the Spearman is used when the two variables are ordinal. Second, you will compute a correlation matrix indicating the associations among all the pairs of three or more variables. Fourth, we will show you how to compute Cronbach’s alpha, the most common measure of reliability, which is based on a correlation matrix. Fifth, you will compute simple or bivariate regression, which is used when one wants to predict scores on a normal/scale dependent variable from one normal or scale dependent variable. Last, we will provide an introduction to a complex associational statistic, multiple regression, which is used to predict a scale/normal dependent variable from two or more independent variables.
