Correlation analyses are used to assess the relationship between two variables. Graphically, the relationship between the two can be plotted (one as the independent, or the predictor variable, and the other as the dependent, or the predicted variable). In this case, the slant of the line represents the degree of correlation: the steeper the line is, the more highly correlated the two variables are. Otherwise, the correlation is represented in a table format as "r," in which case, the greater the absolute value of "r," the higher the correlation.
1) Normality: Assume that the population distribution is normal. Check for normality by creating a histogram.
2) Independent observations: The observations within each variable must be independent.
1) Click on "Correlate" under the "Analyze" menu.
2) Select "Bivariate" from this menu.
3) As seen in Figure 3.1, select the variables you wish to correlate and press "OK."You may put in as many values as you wish.
Figure 3.1 Bivariate Correlations
To obtain a correlation graph, see Creating a Scatterplot under Creating Graphs and Charts.
SPSS will give you a table with all the variables across the top and left. Look for where the columns and rows for your two variables intersect to find information about the r-value and p-value. Significant correlations will be starred, as shown in Figure 3.2.
Figure 3.2 Output from Pearson Correlation
Be sure to include: the r-value, the p-value, and the direction of the effect. You may also choose to include the degrees of freedom (N-2).