Advantages and Disadvantages of the MANOVA

ANOVA and MANOVA are two of the most extensively used statistical measures that are helpful for analysing relationship between the variables in a data set. There are several differences between the ANOVA and MANOV test for which the researchers try to sort the gathered data and choose the best method for conducting the statistical tests and issues. ANOVA mainly checks the differences between the means of two samples/ populations while MANOVA checks for the differences between multiple sample/populations. MANOVA uses covariance-variance relationship of considering more than one dependent variable. Where the ANOVA test can be conducted, where there is only on dependent variables, but the numbers of independent variables may vary as per the data set. The MANOVA test is helpful where there is more than one dependent variables, and the researchers can analyse and assess the differences but the independent variables an analyse its impacts on dependent variables in the data set.

Hence, ANOVA concerns about two variables, while MANOVA concerns the differences in multiple variables simultaneously. ANOVA hereby helps to compare two means at the same time, but can only include one dependent variable in the analysis. On the other hand, MANOVA can determine the relationship between multiple variables concurrently. ANOVA decomposes the total sum of squares T into different group sum of squares B ad within the Group sum of squares W, so that T= B + W. it calculates F= B/ W ration, which main follows the F distribution. The result indicates that; vary small P value of F statistics leads to rejection of Null hypothesis. on the other hand, MANOVA decomposes the total scatter matrix T into between group scatter matrix B and within group scatter matrix W, so that T= B + W. if the P value of the matrix insignificant, the null hypothesis will be rejected and alternative hypothesis being accepted. As per the data set, the researchers can chose the statistical measure to evaluate the significance of the data and analyse variance and internal means.

It is very crucial to know what makes a system advantageous and disadvantageous. ANOVA is a very popular software that is being used for testing purposes. Talking about ANOVA then it is considered to be very useful for all the possible experimental designs that involve pairing, interactions between the effect of two factors. However, the disadvantage of the software ANOVA is that it does not tell related to the specific groups that are significantly different from each other. Or the need for follow-up analyses in place to identify the differences that exist in the two sections.

Knowing about the advantages and disadvantages of software helps in knowing how effective it is in several aspects. The best advantage of the MANOVA is that it is a helpful way to evaluate multiple dependent variables simultaneously. Along with that it also provides a mixture that helps separate independent variable groups. The disadvantage is that it requires large sample sizes to learn about our editorial. It also assumes a need for homogeneity of variance. All these points make the working and system clear for the ones who use it. make sure to overcome the disadvantages to get the expected results.

Through this article, it is possible to demonstrate the ANOVA and MANOVA test, where the researchers must identify the dependent and independent variables in the data set after data sorting and management. If there is one dependent variable and two or more independent variables, the one or two way ANOVA test can be implemented for analysing the means of variance and valuate its significance. However, if there is more than one dependent variable with different independent variable, the researchers can choose MANOVA test for considering all the data sets of the independent variables and analyse different means and their variances for evaluating the impacts of the independent variables on the dependent variables. Hereby, ANOVA and MANOVA tests are different from each other as per the numbers of the dependent and independent variables for considering all the matrixes in the statistical analysis and analysing the significance of the data critically.