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# Large-Scale Simultaneous Hypothesis Testing, Coursework Example

Pages: 1

Words: 287

Coursework

Reversing Type 2 diabetes among obese African American (40-50 years age group)

The goal of any data analysis process is adapting the most appropriate strategies, which ultimately will accurately reflect a true outcome of the investigation/research. In this particular project the goal pertaining to reversing Type 2 diabetes among obese African American (40-50 years age group)relates to interpreting data applying quantitative as well as qualitative methodologies.

In utilizing qualitative data an expository analysis aligning theory to the data collected is a major goal. However, when quantitative data analysis is applied to information retrieved from a study, statistical analysis is the main strategy utilized. Thegoal is to accurately measure behavior of data and represent findings that are both internally and externally valid. Consequently, the methodological concerns are establishing validity and reliability.

Univariate, bivariate and multivariate statistical techniques will be applied in the quantitative processing of data. Univariate is very simple and it allows the researcher to describe the behavior of single variables such as age in relation to the overall results. Bivariate explains two variables such as age and sex in relation to the results observed. Multivariate takes into account a number of variables applicable to the study. They could be obesity, diet, complications and many more as they appear in the data collection process (Babbie, 2009).

Each hypothesis will be tested using the chi-square test of significance whereby the null hypothesis will be verified for consistency. A statistical model must be adapted to test the null hypothesis. This hypothesis is considered true until statistically proven to be false when it is rejected (Efron, 2004).

Reference

Babbie, E. (2009). The Practice of Social Research”, (12th ed ). Wadsworth Publishing,

Efron, B. (2004). Large-Scale Simultaneous Hypothesis Testing. Journal of the American Statistical Association 99 (465): 96

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