Post Your Answer

Anova test the study based on the variables used in the study. For instance: if there is more than one mean, then, you can use one-way ANOVA. If in your study, you are dealing with the height of stalks of three different types of seeds, then, One-way ANOVA is apt for your study. On the other hand, Two- Way ANOVA is done when you need to compare the population depending on the various characteristics. Just imagine if you have three different kinds of seeds which might have used four different types of fertilizer. In this case, you will need to do a comparison between them, then, you should use Two-Way ANOVA. Multivariate analysis of variance (MANOVA): Its concept is simply based on ANOVA with several dependent variables. MANOVA deals in testing the difference in two or more vectors of means.

One-Way has one dependent and one independent variable. Independent will have more than 2 average mean value. Two-Way has one dependent, one independent variable. The dependent will have more than two average mean value, whereas, independent may have one or more average mean value.  MNOVA have two independent and two dependent variables.

one-way ANOVA: If there is more than one mean, then, you should use it. Since there would be only one factor that would affect the variable.  Two-way ANOVA: If there are numerous types of variables used in a study, then, this method is used

If the hypothesis test is letting you test the equality of more than two means using the variance, then, One-Way ANOVA is used. If the test can study the relationship between the factors and variables influencing it, It is then very easily visible that Two-Way ANOVA is used.

All the three types of ANOVA are based on “Analysis of Variance” (ANOVA). One-Way ANOVA: It is used to compare the levels of a single factor based on a single continuous response variable. Two-Way ANOVA: It compares the levels of two or more factors based on a single continuous response variable. MANOVA: In MANOVA, M stands for Multivariate. It is based on ANOVA only but has more than one continuous response variables.