However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a one-way ANOVA to give you a valid result. Since the one-way ANOVA is often followed up with a post hoc test, we also show you how to carry out a post hoc test using SPSS Statistics. This 'quick start' guide shows you how to carry out a one-way ANOVA using SPSS Statistics, as well as interpret and report the results from this test. Alternatively, if your dependent variable is the time until an event happens, you might need to run a Kaplan-Meier analysis. To learn more, see our SPSS Statistics guide on ANCOVA.

Note: If your study design not only involves one dependent variable and one independent variable, but also a third variable (known as a 'covariate') that you want to 'statistically control', you may need to perform an ANCOVA (analysis of covariance), which can be thought of as an extension of the one-way ANOVA. You can do this using a post hoc test (N.B., we discuss post hoc tests later in this guide). Since you may have three, four, five or more groups in your study design, determining which of these groups differ from each other is important. Also, it is important to realize that the one-way ANOVA is an omnibus test statistic and cannot tell you which specific groups were statistically significantly different from each other it only tells you that at least two groups were different. For example, you could use a one-way ANOVA to understand whether exam performance differed based on test anxiety levels amongst students, dividing students into three independent groups (e.g., low, medium and high-stressed students). The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups (although you tend to only see it used when there are a minimum of three, rather than two groups). One-way ANOVA in SPSS Statistics Introduction

Spss 20 tutorial pdf