Question

Compare the various types of ANOVA by discussing when each is most appropriate for use and which types of research questions each best answer. Include specific examples to illustrate the appropriate use of each test and how interaction is assessed using ANOVA.

Answer 1

An ANOVA test is a way to find out if survey or experiment results are significant. In other words, they help you to figure out if you need to reject the null hypothesis or accept the alternate hypothesis.

There are mainly two types of anova

1.one way anova

2.two way anova

1. A one-way ANOVA is used when assessing for differences in one continuous variable between ONE grouping variable. For example, a one-way ANOVA would be appropriate if the goal of research is to assess for differences in job satisfaction levels between ethnicities.

In a one-way ANOVA there are two possible hypotheses.

• The null hypothesis (H0) is that there is no difference between the groups and equality between means.
• The alternative hypothesis (H1) is that there is a difference between the means and groups.

2.A two-way ANOVA is, like a one-way ANOVA, a hypothesis-based test. However, in the two-way ANOVA each sample is defined in two ways, and resultingly put into two categorical groups.Two-way ANOVA can be used to examine the interaction between the two independent variables. Interactions indicate that differences are not uniform across all categories of the independent variables.

For example, females may have higher IQ scores overall compared to males, but this difference could be greater in European countries compared to North American countries. Two-way ANOVAs are also called factorial ANOVAs.

Interaction effects represent the combined effects of factors on the dependent measure. When an interaction effect is present, the impact of one factor depends on the level of the other factor.Interaction effects occur when the effect of one variable depends on the value of another variable. Interaction effects are common in regression analysis, ANOVA, and designed experiments.Given the specifics of the example, an interaction effect would not be surprising. If someone asks you, “Do you prefer ketchup or chocolate sauce on your food?” Undoubtedly, you will respond, “It depends on the type of food!” That’s the “it depends” nature of an interaction effect. You cannot answer the question without knowing more information about the other variable in the interaction term—which is the type of food in our example!