A One-Way Between Subjects ANOVA compares the means between more than two independent groups, such as comparing the difference between groups A, B and C. If your data only has two groups such as Male/Female or Present/Absent you should consider the Independent-Samples t-Test.
It is considered a parametric test and is only suitable for parametric data. To check if your data is parametric, please check out the dedicated guide: Parametric or Not Guide (PDF)
If your data is non-parametric you should consider using a Kruskal-Wallis Test
Step 1: Click Analyze > Compare Means > One-Way ANOVA
Within the "One-Way ANOVA" Window, select the test variable or dependent variable you are analysing and move it to the "Dependent List" box. Then move your independent/grouping variable into the “Factor” box.
Click "Options". Within the "One-Way ANOVA: Options" window select "Descriptive", "Fixed and random effects","Homogeneity of variance test", and "Means plot"
Click "Post Hoc". Within the "One-Way ANOVA: Post Hoc Multiple Comparison" window select "Bonferroni"
Click "Continue" then "OK".
SPSS will generate multiple tables, to correctly report this test we need three, the Descriptives, the ANOVA, and the ANOVA Effect Sizes:
This table shows a selection of descriptive statistics: the sample size of each group (N), the mean of each group (Mean), and the standard deviation of each group (Std. Deviation), best practice is to report them all.
This table shows the specific test results including the F-statistic (F), the degrees of freedom (df) the two-tailed significance or p-value (Sig).
This table shows the eta-squared effect size of the ANOVA and its 95% confidence interval.
Test scores of three groups (A, B, and C) were compared. A One-Way Between-Subjects ANOVA indicated there was a significant effect for test score, F (2,70) = 4.39, p = .016, η2 = .112.
In addition, if your ANOVA is significant you must also report your post-hoc results:
On average, Group A (M = 49.67, SD = 6.70) scored higher than Group B (M = 48.12, SD = 5.21), but lower than Group C (M = 54.14, SD = 8.44). Post hoc comparisons were conducted using the Bonferroni correction. The difference between Group A and Group B, 1.25 95% CI [-3.45,5.97], was not statistically significant (p = .999), The difference between Group A and Group C, 4.47 95% CI [-.36,9.30], was not statistically significant (p = .079). However, th difference between Group B and Group C 5.79 95% CI [.76,10.98] was statistically significant (p = .018).