QUESTION-Compare the various types of ANOVA by discussing when each is most appropriate for use. Include specific examples to illustrate the appropriate use of each test and how interaction is assessed using ANOVA.

QUESTION-Compare the various types of ANOVA by discussing when each is most appropriate for use. Include specific examples to illustrate the appropriate use of each test and how interaction is assessed using ANOVA.

Solution

ANOVA Test

Catherine, your post is quite informative. I agree with you that ANOVA, also known as analysis of variance, is a statistical process applied to test if the outcomes of a survey are essential or not. It is one of the most often used statistical approaches in medical research (Kim, 2017). This type of test help individuals comprehends how different groups respond, with a null hypothesis for a test that means different groups are equivalent. If there’s a statistically significant result, then it implies that the two populations are not equal. I support your point that ‘one-way’ ANOVA and ‘two-way ANOVA are some of the ANOVA types. One way ANOVA compares means of 2 or more independent groups defines whether statistical evidence that the related population means are meaningfully different.

On the other hand, Two-way ANOVA determines the effect of 2 insignificant predictor variables on constant outcome variables and analyzes the impact of independent variables on the anticipated outcome and their relationship to the outcome itself. There are some assumptions concerning these two types of ANOVA. For one, the results of one-way ANOVA may be well-thought-out as reliable provided that the following assumptions are met: that response variable residuals are generally distributed and that the variances of populations are equivalent (Sureiman & Mangera, 2020). In two-way ANOVA, the assumptions are that populations from which samples are attained should be generally distributed, observations for between and within groups should be independent, variances between populations should be equal, and besides, data are nominal and interval. Generally, care providers and marketers find it convenient to use the ANOVA test as it has the advantages of affording the overall equality test of group means,  control the overall rate of type I error and besides, it’s a parametric test, so it’s more influential if normality assumptions hold.

References

Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean Journal of anesthesiology, 70(1), 22. DOI: 10.4097/kjae.2017.70.1.22