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PUB-550: Application and Interpretation of Public Health Data

Objectives:

Compare and contrast the types of ANOVA tests and their application.

Apply the results of an ANOVA to determine statistical difference between means and potential interactions.

The purpose of this assignment is to calculate and interpret an ANOVA table. For this assignment, use IBM SPSS Statistics.

Part 1:

Using the “Example Dataset,” assess this statement using ANOVA: “People with different levels of education exercise for different amounts of time during the week.”

Select and conduct the appropriate ANOVA test to assess the statement. Export the ANOVA table to a Word document.

Part 2:

In 250-500 words, discuss the following regarding the use of ANOVA.

Describe when the use of ANOVA is more appropriate than the use of a t-test.

Describe which ANOVA test you used and why.

Interpret the results by (a) stating the reason the study or test was done; (b) presenting the main results, including explaining the within and between subjects variation and the F-ratio from the ANOVA table; (c) explaining what the results mean, including discussing whether there is a statistically significant difference between education groups and amount of exercise; and (d) making suggestions for future research.

General Requirements

Submit one Word document for the Part 1 assignment content and a second Word document for Part 2 of the assignment. Submit both Word documents to the instructor.

APA style is not required, but solid academic writing is expected.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are not required to submit this assignment to LopesWrite.

Attachments

PUB-550-RS-Example Dataset.xlsx

Compare the various types of ANOVA by discussing when each is most appropriate for use and which types of research questions each best answers. Include specific examples to illustrate the appropriate use of each test and how interaction is assessed using ANOVA.

Topic 5 DQ 1

Analysis of Variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation. It is very useful for analyzing datasets. It is a way to find out if survey or experiment results are significant. In other words, they help you to figure out if you need to reject the null hypothesis or accept the Alternative hypothesis. It basically allows comparisons to be made between three or more groups of data. For example, when multiple medications that treat the same condition are being tested to see which one works better or which one has the most benefit (Glen, 2020).

There are two types of ANOVA that are commonly used, the One-Way ANOVA and the Two-Way ANOVA. A one-way ANOVA is a type of statistical test that compares the variance in the group means within a sample while considering only one independent variable or factor. It is a hypothesis-based test, meaning that it aims to evaluate multiple mutually exclusive theories about one data. A one-way ANOVA compares three or more than three categorical groups to establish whether there is a difference between them. Within each group, there should be three or more observations and the means of the samples are compared. For example, conducting research on whether people gain more weight during Thanksgiving, Christmas or Easter (Mackenzie, 2018). In a one-way ANOVA, there are two possible hypotheses. The null hypothesis (H0) is that there is no difference between the groups and equality between means. And the alternative hypothesis (H1) is that there is a difference between the means and groups (Mackenzie, 2018). In the two-way ANOVA, each sample is defined in two ways and resultingly put into two categorical groups. For example weight and gender. It actually compares the effect of multiple groups of two independent variables on a dependent variable and on each other (Mackenzie, 2018).

References:

Glen, S. (2020). ANOVA Test: Definition, Types, Examples. Retrieved from

https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/hypothesis-testing/anova/#top

Mackenzie, R. (2018). One-Way vs Two-Way ANOVA: Differences, Assumptions, and Hypotheses. Retrieved from

https://www.technologynetworks.com/informatics/articles/one-way-vs-two-way-anova-definition-differences-assumptions-and-hypotheses-306553

Different types of software can be used for data management. Compare Excel and SPSS and discuss specific SPSS software features that make it preferable to Excel for data management. Provide examples illustrating when electing to use SPSS could be preferable to Excel and vice versa.

Topic 5 DQ 2

SPSS is a powerful program which provides many ways to rapidly examine data and test scientific hunches. SPSS can produce basic descriptive statistics, such as averages and frequencies, as well as advanced tests such as time-series analysis and multivariate analysis (Zhang, 2015). The program also is capable of producing high-quality graphs and tables. Compared with Excel, SPSS’s statistical analysis, chart function and database connectivity function can be more powerful. According to Zhang (2015) SPSS software has the characteristics of high speed, no programming, convenient data interface and flexible function module to deal with the huge data which is affected by random factors. Compared with SPSS, Excel shows its more professional. It is worth mentioning that these two types of software documents can be introduced into each other. This greatly reduces the trouble of data entry. Users can use the same data file to be processed on different software. The main limitation of analyzing data in an Excel spreadsheet is the potential for errors by typing incorrect formulas (Neyeloff, Fuchs and Moreira, 2012). All sensitivity analysis is done manually, including and excluding each study of the effect summary calculations.

Neyeloff, J. L., Fuchs, S. C., & Moreira, L. B. (2012). Meta-analyses and Forest plots using a microsoft excel spreadsheet: step-by-step guide focusing on descriptive data analysis. BMC Res Notes (5) 52. https://doi.org/10.1186/1756-0500-5-52.

Zhang, K. (2015). Application of SPSS single factor analysis of variance in biostatistics.

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