The purpose of this guide is to show you how to create a simple scatterplot using SPSS Statistics. A simple scatterplot could also be used to determine if there is a linear relationship between the distance women can run in 30 minutes and their VO 2max, which is a measure of fitness (i.e., your dependent variable would be "distance run" and your independent variable would be "VO 2max"). Note: If you are analysing your data using an ANCOVA (analysis of covariance) or two-way ANOVA, for example, you will need to consider a grouped scatterplot instead (N.B., if you need help creating a grouped scatterplot using SPSS Statistics, we show you how in our enhanced content).įor example, a simple scatterplot could be used to determine if there is a linear relationship between lawyers' salaries and the number of years they have practiced law (i.e., your dependent variable would be "salary" and your independent variable would be "years practicing law"). For example, determining whether a relationship is linear (or not) is an important assumption if you are analysing your data using Pearson's product-moment correlation, Spearman's rank-order correlation, simple linear regression, multiple regression, amongst other statistical tests. You can assign different colors or markers to the levels of these variables.A Simple Scatterplot using SPSS Statistics IntroductionĪ simple scatterplot can be used to (a) determine whether a relationship is linear, (b) detect outliers and (c) graphically present a relationship between two continuous variables. You can use categorical or nominal variables to customize a scatter plot. Either way, you are simply naming the different groups of data.
SCATTER PLOT RELATIONSHIPS CODE
You can use the country abbreviation, or you can use numbers to code the country name. Country of residence is an example of a nominal variable. For example, in a survey where you are asked to give your opinion on a scale from “Strongly Disagree” to “Strongly Agree,” your responses are categorical.įor nominal data, the sample is also divided into groups but there is no particular order. With categorical data, the sample is divided into groups and the responses might have a defined order. Scatter plots are not a good option for categorical or nominal data, since these data are measured on a scale with specific values. Some examples of continuous data are:Ĭategorical or nominal data: use bar charts Scatter plots make sense for continuous data since these data are measured on a scale with many possible values. Scatter plots and types of data Continuous data: appropriate for scatter plots Annotations explaining the colors and markers could further enhance the matrix.įor your data, you can use a scatter plot matrix to explore many variables at the same time. The colors reveal that all these points are from cars made in the US, while the markers reveal that the cars are either sporty, medium, or large. There are several points outside the ellipse at the right side of the scatter plot. From the density ellipse for the Displacement by Horsepower scatter plot, the reason for the possible outliers appear in the histogram for Displacement. In the Displacement by Horsepower plot, this point is highlighted in the middle of the density ellipse.īy deselecting the point, all points will appear with the same brightness, as shown in Figure 17. This point is also an outlier in some of the other scatter plots but not all of them. In Figure 16, the single blue circle that is an outlier in the Weight by Turning Circle scatter plot has been selected. It's possible to explore the points outside the circles to see if they are multivariate outliers. The red circles contain about 95% of the data. The scatter plot matrix in Figure 16 shows density ellipses in each individual scatter plot.
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