When analyzing data in a research process, there are several techniques used. A t-test is a conventional method of measuring variance in research, especially when the investigation is measuring the differences between two groups: an experimental group and a control group. However, this test cannot be reliable when conducting tests between more than two variables as it can result in increased errors. In such an instance, researchers use an ANOVA test that is reliable when there are more than two populations to be examined, such as a dependent valuable and two independent valuables.
ANOVA or analysis of Variance is a tool or technique used when carrying out research to analyze variations between two or more groups and ascertain variance among populations ( Cleophas & Zwinderman, 2018) . The method can be applied when measuring the mean of more than two groups in a research population. As a result, researchers use the ANOVA test when testing a hypothesis that has more than three groups to determine any statistical differences between three or more groups of unrelated information known as a variance. After finding the statistical difference, then the researcher will examine where the gaps are found and map the results as data.
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Researchers use the ANOVA technique in a variety of ways depending on the research design. A one-way ANOVA technique is where there is a single independent variable and when the research deals with variations among different groups. It is the most comfortable type of ANOVA because researchers can only use one grouping to describe different groups. An example of an ANOVA test is when examining differences in IQs between particular populations. A one-way ANOVA is when measuring an independent variable IQ and an independent variable being gender.
On the other hand, a two-way ANOVA technique has two independent variables and is used when dealing with complicated groupings. This technique is applied when examining differences between the dependent variable and two independent variables. In most cases, a two-way ANOVA technique is used when discussing the interaction between two independent variables ( Khan, 2013) . Additionally, a researcher can use the ANOVA test when there are more than two independent variables in research. A two-way ANOVA test will entail examining differences in IQ tests as a dependent variable and the gender and age of the population as independent variables. This will show that females will have a higher IQ compared to male, but the difference will be more significant among a particular age difference. Other variables examined in ANOVA tests may entail IQ as the dependent variable and Gender, Ethnicity, Country and Age group as independent variables.
In an ANOVA test, the level of measurement of the variables and assumptions of the analysis play an important role. For example, the dependent variable must be continuous such as a ratio while the independent variables must be categorical such as nominal. Other assumptions in the study are that the data is normally distributed, and also that there is the homogeneity of variance, which means the variation among groups is supposed to be approximately equal. Additionally, another assumption is that observations are independent of each other, which means the results of ANOVA will be invalid if the independence assumption is violated ( Wiley & Pace, 2015) .
In conclusion, researchers use the ANOVA test when analyzing data between three or more variables. The method is preferred because it produces accurate results when compared to using a t-test and it minimizes the occurrence of type-one errors. As a result, the technique avoids presenting misleading data or information that is inappropriate.
References
Cleophas, T. J., & Zwinderman, A. H. (2018). Analysis of Variance. In Regression Analysis in Medical Research (pp. 147-153). Springer, Cham.
Khan, R. M. (2013). Analysis of variance. Problem Solving and Data Analysis using Minitab: A clear and accessible guide to Six Sigma methodology , 150-208.
Wiley, J. F., & Pace, L. A. (2015). Analysis of variance. In Beginning R (pp. 111-120). Apress, Berkeley, CA.
