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Introduction to ANOVA statistics
Introduction to ANOVA statistics
2nd February 2023
Related topic: Quantitative research
Author: Ady Hameme N. A.
ANOVA, or Analysis of Variance, is a statistical technique used to determine whether there is a significant difference between the means of two or more groups. It was first introduced by statistician Ronald A. Fisher in 1925 as a way to test hypotheses about the mean of a population.
Since its inception, ANOVA has been widely used in a variety of fields, including psychology, education, and business, to compare groups and determine the statistical significance of their differences. In 1981, statistician W.L. Hays expanded upon Fisher's work and introduced the concept of nested ANOVA, which allowed for the analysis of data from groups within groups.
In recent years, ANOVA has been further refined and expanded upon by statisticians such as Keppel and Wickens (2004), Myers and Well (2003), and Tabachnick and Fidell (2013). These advancements have allowed for a deeper understanding of the statistical relationships between groups and have made ANOVA a valuable tool for researchers.
ANOVA is based on the premise that the variance within a group is smaller than the variance between groups. By comparing the variance within each group to the variance between groups, ANOVA allows researchers to determine whether the differences between groups are statistically significant.
There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and factorial ANOVA, which allow researchers to compare groups at different levels of complexity. For example, one-way ANOVA is used to compare the means of two or more groups, while two-way ANOVA allows for the comparison of two or more groups while taking into account the effect of two or more variables.
ANOVA is a powerful tool for researchers and has the ability to provide important insights into the relationships between groups. It is important, however, for researchers to carefully consider the assumptions of ANOVA and to ensure that their data meet these assumptions in order to accurately interpret their results.
Cite this article: Ady Hameme, N. A. (2023, February 2). Introduction to ANOVA statistics. Retrieved <insert month> <insert date>, <insert year>, from https://www.myadvrc.com/publications/article-14
References
Fisher, R.A. (1925). Statistical methods for research workers. London: Oliver and Boyd.
Hays, W.L. (1981). Statistics (3rd ed.). New York: Holt, Rinehart, and Winston.
Keppel, G., & Wickens, T.D. (2004). Design and analysis: A researcher's handbook (4th ed.). Upper Saddle River, NJ: Pearson Education.
Myers, J.L., & Well, A.D. (2003). Research design and statistical analysis (3rd ed.). Upper Saddle River, NJ: Pearson Education.
Tabachnick, B.G., & Fidell, L.S. (2013). Using multivariate statistics (6th ed.). Boston, MA: Pearson Education.
Header photo by Zukiman Mohamad. For illustration purposes only.
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