Enter pairs of scores in SPSS using the data editor. Enter each If you only had two variable, enter one variable SPSS Correlation Pulldown Menus. This tutorial will show you how to use SPSS version to perform bivariate Bivariate correlation can be used to determine if two variables are linearly related . A (Pearson) correlation is a number between -1 and +1 that indicates to what a correlation of 0 means there's no linear relation between 2 variables whatsoever. . The figure below shows the most basic format recommended by the APA for.
Then click on the arrow button to move the variable into the Variables pane.
Instructions for Using SPSS to Calculate Pearson's r | Educational Research Basics by Del Siegle
Click on the other variable that you want to correlate in the left hand pane and move it into the Variables pane by clicking on the arrow button: Specify whether the test of significance should be one-tailed or two-tailed. We won't get to this topic for quite a while. For now, select the one-tailed test by clicking on the circle to the left of "one-tailed".
You can click on the Options button to have some descriptive statistics calculated. The Options dialog box will appear: From the Options dialog box, click on "Means and standard deviations" to get some common descriptive statistics. Click on the Continue button in the Options dialog box.
Click on OK in the Bivariate Correlations dialog box. This is what the Bivariate Correlations output looks like: The Descriptive Statistics section gives the mean, standard deviation, and number of observations N for each of the variables that you specified.
Using SPSS for Correlation
For example, the mean of the extravert variable is 2. The Correlations section gives the values of the specified correlation tests, in this case, Pearson's r. Each row of the table corresponds to one of the variables.
Each column also corresponds to one of the variables. In this example, the cell at the bottom row of the right column represents the correlation of extravert with extravert.
Whilst there are a number of ways to check whether a linear relationship exists between your two variables, we suggest creating a scatterplot using SPSS Statistics, where you can plot the one variable against the other variable, and then visually inspect the scatterplot to check for linearity.
Your scatterplot may look something like one of the following: In our enhanced guides, we show you how to: Pearson's correlation determines the degree to which a relationship is linear. Put another way, it determines whether there is a linear component of association between two continuous variables.
As such, linearity is not actually an assumption of Pearson's correlation. However, you would not normally want to pursue a Pearson's correlation to determine the strength and direction of a linear relationship when you already know the relationship between your two variables is not linear.
Instead, the relationship between your two variables might be better described by another statistical measure. For this reason, it is not uncommon to view the relationship between your two variables in a scatterplot to see if running a Pearson's correlation is the best choice as a measure of association or whether another measure would be better.
There should be no significant outliers.
Pearson's Product-Moment Correlation using SPSS Statistics
Outliers are simply single data points within your data that do not follow the usual pattern e. The following scatterplots highlight the potential impact of outliers: Therefore, in some cases, including outliers in your analysis can lead to misleading results.
Therefore, it is best if there are no outliers or they are kept to a minimum. Your variables should be approximately normally distributed. In order to assess the statistical significance of the Pearson correlation, you need to have bivariate normality, but this assumption is difficult to assess, so a simpler method is more commonly used.