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
Analysis of variance (ANOVA) has two different types: the tests can be one-way or two-way, and the integral approach’s choice depends on the expecting outcomes. Kim (2017) states that “ANOVA falls under the category of parametric analysis methods which perform the analysis after defining the distribution of the recruitment population in advance” (p. 22). The first variation is used for studying multiple groups of data to determine the relationship between dependent or independent pieces (Kim, 2017). For example, World Health Organization can apply public health data about the number of deadly cardiovascular disease cases in multiple countries to discover wherever one of them is close to epidemic conditions. One-way ANOVA is the suitable data analysis approach when there is a demand in identifying significant differences between the means.
While the described ANOVA testing approach includes only one variable to apply, the two-way type requires two categorical variables that can be independent and cause dependence when combined. For example, it can be used in medication testing when the effectiveness can be determined by checking the participants’ conditions before and after taking it. The two separate groups should perform various activities, such as two different treatment programs. If the collected data has dependent quantifiable and two categorical independent variables, it can be tested with the two-way ANOVA (Nibrad, 2019). Both types are suitable for various statistical studies of the public health data because they determine tendencies and discover causality between factors.
When analyzing public health data, describe the steps you would take to determine what statistical test to conduct. Discuss the impact of data type on test selection
Public health data can be presented in various types, and it is crucial to determine the right statistical test to get useful results. Choosing the right approach requires knowing the suppositions and the variable’s categories that are going to be utilized. Steps for selecting necessary testing are: identify the type of data, decide what are the expected outcomes, analyze how the information was gathered, and use the examination that suits the category (Beath & Jones, 2018). The assumptions that are commonly identified are the observations’ independence, the variance between the groups, and, for quantitative data, the normality of distribution, while variables basically divide into quantitative and categorical (Beath & Jones, 2018). Statistical tests can be divided into categorical and numeric, and the choice should depend on the chosen outcome measurement. Numbers will be the result for sample t-test, Pearson correlation, and ANOVA testing, while Pearson goodness of fit, GLMM, and logistic regression result in the categories (Beath & Jones, 2018). Public health statistics can be compared based on the groups’ dependent variables or analyzed to determine the testing’s causalities.
The type of data to analyze plays a prior role in selecting the statistical testing approach because different variables require specified measurements. The agreement as the concordance between two measures of one variable must be checked before selecting probations (Ranganathan et al., 2017). Moreover, categorical data cannot be tested by the numeric approaches, and the numbers would not provide a shred of clear evidence about the qualities.
Given the limited amount of statistical calculations required by some public health positions, explain why it is important for you to know how to utilize appropriate software to analyze public health data. Support your ideas with reasons, facts, and examples
Public health positions include a limited amount of calculations and require the ability to use specialized software and strategies for data analysis. Hayat et al. (2018) state that “the widespread availability in user-friendly statistical software packages has led to the use of more advanced statistical methods and analyses being used and reported in the health literature.” Indeed, counting data must be chosen carefully because the outcomes are essential for establishing healthcare policies and regulating the overall wellbeing.
The recent COVID-19 pandemic is an example that reveals the importance of medical professionals’ knowledge of how to analyze public health data. Information that was continuously gathered helped understand the disease symptoms, risk groups, and mortality rates. Cowling and Aiello (2020) state that appliance of measurements “decreased transmission, spread the epidemic over a longer period, reduced the height of the epidemic peak, and the number of infected persons” (p. 1750). If a medical professional knows how to test the COVID-19 statistical data, they are capable of timely determining relationships between groups or factors that might help prevent the disease from the spread. Moreover, the biostatistics software appliance provided significant evidence about the infection’s lifecycle based on the public health data (Hayat et al., 2018). That information is vital in such processes as vaccine development and requires the knowledge of statistical testing.
Discuss three strengths of the linear regression? Identify a peer-reviewed study that uses linear regression in its analysis. Explain why linear regression was used and discuss one challenge in interpreting the results. Include the permalink with your citation
Linear regression creates the relationship between two variables by establishing a linear equation for analyzing data, where one alternate is explanatory, and another is dependent. The three main strengths of that type of approach are its straightforwardness, broad variations of data to study with it, and the ability to predict the dependence tendency (Aggarwal & Ranganathan, 2017). These points make linear regression one of the most popular statistical measurements in different disciplines, including the public health data analysis.
Peer-reviewed studies apply linear regression to determine the dependence between two factors based on the public health statistics. Indeed, the study “Increased body weight affects academic performance in university students” conducted by Anderson and Good, uses the approach while comparing the two groups of students by their final grades and BMIs. Linear regression helped the scientists determine the correlation between healthy weight and better academic performance among the participants (Anderson & Good, 2017). However, there is a challenge in interpreting the results due to the technique’s straightforwardness. The regression can only analyze the two variables, while multiple factors can affect the students’ quality of studying, diet, and health conditions (Anderson & Good, 2017). It is crucial to apply various statistical testings and approaches to get accurate results for public health studies.
References
Aggarwal, R., & Ranganathan, P. (2017). Common pitfalls in statistical analysis: Linear regression analysis. Perspectives in Clinical Research, 8(2), 100. Web.
Anderson, A. S., & Good, D. J. (2017). Increased body weight affects academic performance in university students. Preventive Medicine Reports, 5, 220-223.
Beath, A., & Jones, M. P. (2018). Guided by the research design: choosing the right statistical test. The Medical Journal of Australia, 208(4), 163-165.
Cowling, B. J., & Aiello, A. E. (2020). Public health measures to slow community spread of coronavirus disease 2019. The Journal of Infectious Diseases, 221(11), 1749-1751.
Hayat, M. J., Powell, A., Johnson, T., and Caldwell, B. L. (2017). Statistical methods used in the public health literature and implications for training of public health professionals,” PLoS One, 12(6), e0179032.
Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean Journal of Anesthesiology, 70(1), 22.
Nibrad, G. M. (2019). Methodology and Application of Two-way ANOVA. International Journal of Marketing and Technology, 9(6), 1-8. Web.
Ranganathan, P., Pramesh, C. S., & Aggarwal, R. (2017). Common pitfalls in statistical analysis: Measures of agreement. Perspectives in Clinical Research, 8(4), 187. Web.