Application and interpretation of Public Data-TOPIC 5 DQ 1-
Application and interpretation of Public Data-TOPIC 5 DQ 1-
QUESTION-TOPIC 5 DQ 1
Compare the various types of ANOVA by discussing when each is most appropriate for use. Include specific examples to illustrate the appropriate use of each test and how interaction is assessed using ANOVA.
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Solution
Analysis of Variances-ANOVA Testing
Analysis of Variance tool entails statistical data analysis techniques used in studying and comparing experimental and observational outcomes in research (Nowakowski, 2019). Thus, it qualifies as an effective approach for examining several groups of people by studying the effect of multiple factors on them: independent variables over multiple dependent variables. Analysis of Variances focuses on establishing any existing relationship among data sets. Therefore, the test method determines if there is any relation in the grouped data set in one way or another and the effects of variables on each other (Nowakowski, 2019).
Types of Analysis of Variances
Analysis of Variances uses a set of statistical techniques to examine the average value in population groups. For this tool to be effective in a study, two types can be used: the one-way and two-way analysis of variances. In one-way, the variance comparison is done within three or more categories in consideration of only one independent variable (Mackenzie, 2018). The analysis of variance is a hypothesis-based test. Generating of hypothesis entails developing data questions (Mackenzie, 2018). For example, a group of researchers studying the prevalence rate of babies having flu, the hypothesis question would be; does the number of babies having flu increase in winter or summer seasons, both winter and summer mean the same thing. Thus categorically grouped into three or more months to represent both summer and winter seasons (Mackenzie, 2018).
Two-way analysis of variances is more likely the same as one-way. The test is hypothesis-based, but the variance comparison is done in two ways, considering two independent variables (Mackenzie, 2018). For instance, in the case of babies having flu, it will be; does the number of babies having flu increase in winter or summer seasons and which gender in particular tends to be high. In this situation, there are two independent variables. That is, the seasons and the gender; thus, two independent variables in testing over the dependent variable are the number of babies (Mackenzie, 2018).
Application of Analysis of Variance Test
In the one-way analysis of variances, the comparison occurs categorically among three or more groups. For instance, the analysis is categorically done in February, March, September and October to show any differences among the observations, who are the babies. On the other hand, the two-way analysis of variances analyzes the effect of the two variables on another, that is, seasons and gender, and if they have a different effect on the number of babies. Thus, both types of analysis of variances tend to examine the dependent and independent variables to determine an outcome of a study in different groups of populations.
References
Mackenzie, R. J. (2018). One-way vs two-way ANOVA: Differences, assumptions and hypotheses. Technology Networks. https://www.technologynetworks.com/informatics/articles/one-way-vs-two-way-anova-definition-differences-assumptions-and-hypotheses-306553
Nowakowski, M. (2019). The ANOVA method as a popular research tool. Studia i Prace WNEiZ US, (55), 67-77. https://wnus.edu.pl/sip/file/article/view/16104.pdf
