Main Title Study on the Analysis of Variance

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Main Title Study on the Analysis of Variance

Analysis of Variance (ANOVA)

Analysis of variance is carried out in the testing of hypothesis, either to accept the null hypothesis or the alternative hypothesis. Analysis of variance (ANOVA) is an extension of the z-test and the t-test after the invention by Ronald Fisher in 1918 (Henson, 2015). Since then the ANOVA has been used by most statisticians in hypothesis testing. ANOVA is used to split the overall variability into the data collected into either systematic or random factors. Systematic factors affect the data while the random does not harm the data.

Plan of the Study

The first step used in analyzing of factors affecting a particular set of data is the ANOVA test. The test enables the analyst to compare groups of data and tell if there is any relationship between them. For instance, One- way ANOVA is used to determine the relationship between more than two groups of data.

Independent Groups

The analyst uses ANOVA to determine how independent variables affect the dependent variable in a study to determine the relationship between the mean of two variables. In the case of the above study, Mary has to identify both the independent and dependent groups so that she can begin the analysis. For instance, the independent variable, in this case, is the independent variable is background noise level while the dependent variable is the Sound from the class work.

The Condition for the Study

There must be more than two variables for comparison. For instance, in the case of the above study, there are high and low noises and the teaching’s sound making a total of three set of data for comparison.

Measurement of the Dependent Variable

For one to get a continuous measurement, the Dependent variable is measured at a continuous interval, as they are ratio variables. The measurements are done severally to determine the consistency of the variable hence coming up with a continuous set of data

Advantages and Disadvantage of Repeated Measurement in ANOVA


Statistical power. Measurements done repeatedly are powerful in that it controls factors that cause variation in variables in the groups of study (Crowder, 2017).

Fast and Cheaper. This method of measurement the cheapest and fastest as there is only a few people need the training to collect the data.

Few Subjects. Due to its high statistical power, the method requires only a few subjects, to get the desired size of data. Sample size can be made smaller further considering that each took measurements repeatedly.

Time-Related Effects. Collection of data independently on the different group is of great significance bearing in mind that it is time-related. The effect helps in keeping the tracked over time especially during the subjects are fatigue.


Orders Effect. Since there are repeated measurements (Crowder, 2017), the different subjects handle each data differently; hence the order given don’t match with the order of the treatment. Order effect is very important, and the interference with it can make the whole analysis fail, as it is difficult to retrace the actual treatment of order.



Most Probable Choice

The best choice to make is to stick with repeated measurement of ANOVA (Crowder, 2017) because as much as the drawback is concerned, the advantages are more. Therefore, repeated measurement remains the top choice especially when the order effect is handled, by treating the order reversing.





Crowder, M. (2017). Analysis of repeated measures. Routledge.

Henson, R. N. (2015). Analysis of variance (ANOVA). Brain Mapping: an encyclopedic reference. Elsevier, 477-481.