What is Two-Way ANOVA?

What is Two-way ANOVA?

Two-Way ANOVA (Analysis of Variance) is a statistical analysis method used to test the differences in means across groups that have two or more factors. It considers the main effects of each factor and the interaction effects between these factors. This analysis is useful for examining how each factor affects the dependent variable and whether there is an interaction effect between the factors.

When to Use Two-Way ANOVA

Two-Way ANOVA is used in situations where there are multiple groups being measured with two factors influencing the dependent variable. For example:

• Studying the impact of teaching methods (traditional and online) and gender (male and female) on academic performance.

• Analyzing the effect of diet type (vegetarian and non-vegetarian) and exercise duration (less than 1 hour and more than 1 hour) on blood sugar levels.

Example Research Hypotheses

Suppose a study aims to examine the impact of teaching methods (traditional and online) and gender (male and female) on academic performance. The research hypotheses could include:

• Hypothesis 1 (Main Effect of Teaching Method): Teaching methods have an effect on academic performance.

• Hypothesis 2 (Main Effect of Gender): Gender has an effect on academic performance.

• Hypothesis 3 (Interaction Effect): There is an interaction effect between teaching methods and gender on academic performance.

Characteristics of Variables

• Dependent Variable: Must be a quantitative variable that can be measured, such as test scores, blood sugar levels, or satisfaction levels.

• Independent Variables: Must be categorical variables that can be divided into groups, such as teaching methods (traditional and online) and gender (male and female).

Two-Way ANOVA is a valuable tool for analyzing the effects of two factors on a dependent variable and for investigating the interaction effects between these factors. Using Two-Way ANOVA helps us understand the complexity of how different factors influence the dependent variable. Interpreting the results of the analysis helps us understand the significance and nature of the various effects on the research and business decision-making processes.

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