Introduction to ANOVA
Also referred to as ANOVA, in the field of psychology, the study of variance is a mathematical method used to measure the variation between two or more ways. It is often used to figure out whether the observations of experimental and observational evidence are significant. ANOVA allows an observer to determine, in basic words, whether to consider or deny a hypothesis. Through the variance analysis, we test groups to assess if there is a disparity between them or if one is greater than the other. Grove and Cipher note that there is a one-way ANOVA and a two-way ANOVA where one factor is being studied and two factors are experimented on respectively (2016).
Importance of ANOVA
The ANOVA test is the first step while looking at the factors that are affecting a given data set (Kass, Eden, & Brown, 2014). For instance, taking a group of students from different colleges to take the same exam just to determine the result if students from a particular college will perform better than the others. Therefore, while experimenting with the students, one can decide to either random samples or convenience samples. For example, conduct a study of males versus females or one college versus the other. Since the two combinations are different, it is either the gender or the college that will be the most informative source of variation in the study whether it is a null or an alternative hypothesis. With ANOVA, we can test a group of subjects and determine if there exists a difference between them (Hahn & Salmaso, 2017).
Comparison of Multiple Groups
In essence, ANOVA is a technique that can be used to examine the potential differences in the means of three or more groups. Moreover, the analysis allows a comparison of more than two groups all at the same time to conclude whether a relationship exists between them or not.
References
Grove, S. K., & Cipher, D. J. (2016). Statistics for Nursing Research: A Workbook for Evidence-Based Practice (2nd ed.). St. Louis, MO: Saunders Elsevier.
Hahn, S., & Salmaso, L. (2017). Analysis Of Variance (ANOVA) is used to compare the Means of Various Samples. Parametric ANOVA Approaches Assume Normally Distributed Error Terms Within Subsamples. Permutation Tests Like Synchronized Permutation Tests Are Computationally Intensive and Distribution Free Procedures. Hence, They Overcome The Limitations Of Parametric Methods. Unbalanced Designs With Differing Subsample Sizes Are Quite...Statistical Papers,58(1), 123-146.
Kass, R. E., Eden, U. T., & Brown, E. N. (2014). Analysis of variance. In Analysis of Neural Data (pp. 361-389). Springer New York.
