One way ANOVA
ANOVA (analysis of variance) is a widely used statistical method to compare the means of two or more groups. When there are only two means to be compared, the calculation of ANOVA is simplified to t tests. ANOVA tests normally return a value called the omnibus F. Therefore, ANOVA tests are also called “F tests” (Lazar, 2017). One way ANOVA is used to compare means for between-group design.
The assumptions for using one way ANOVA are:
- There is independence of observations. This is mostly a study design issue and, as such, you will need to determine whether you believe it is possible that your observations are not independent based on your study design.
- There is homogeneity of variances. This means that the population variances in each group are equal.
- There should be no significant outliers.
- The dependent variable is normally distributed in each group that is being compared in the one-way ANOVA.
For further reading about one way ANOVA using SPSS, you may read this:
or watch these videos:
Lazar, J. , Feng, J. H., Hochheiser, H. (2017), Research methods in human-computer interaction: Morgan Kaufmann, 2017.
Rosenthal, R., Rosnow, R., 2008. Essentials of Behavioral Research: Methods and Data Analysis, third ed. McGraw Hill, Boston, MA.
Sauro, J., and Lewis, J. R., (2016). Quantifying the user experience: Practical statistics for user research: Morgan Kaufmann.
Dix, A. (2020). Statistics for HCI: Making Sense of Quantitative Data. Morgan & Claypool Publishers.
Robertson, J., & Kaptein, M. (2016). An introduction to modern statistical methods in HCI (pp. 1-14). Springer International Publishing.
Larson-Hall, J. (2015). A guide to doing statistics in second language research using SPSS and R. Routledge.
Aldrich, J. O. (2018). Using IBM SPSS statistics: An interactive hands-on approach. Sage Publications.
Salcedo, J., & McCormick, K. (2020). SPSS statistics for dummies. John Wiley & Sons.