Cross-tabulation of Healthcare Outcomes in Diabetes
In this week, we take a look at cross-tabulation or contingency tables, which deals with analysis of tabular data. This implies analysis of categorical/nominal variables. Moreover, chi-square and other measures of association are discussed, too.
Crosstabulation or contingency tables deals with analysis of tabular data, which implies analysis of categorical/nominal variables. In this module, chi-square and other measures of association are discussed.
Upon successful completion of this module, you will be able to:
- Develop research questions and testable hypotheses linked to existing theory or research.
- Develop hypotheses, choose appropriate statistics to test them, and describe the results correctly in a short research paper.
- Develop research questions and testable hypotheses linked to existing theory or research.
- Perform descriptive and inferential statistical analysis of public administrative datasets using IBM SPSS software.
- Interpret results from descriptive and inferential statistical analysis of public administrative datasets and place results in APA formatted text, tables, and figures.
Requirements: 3_4 | .doc file
Answer preview
From the simple statistics adduced in the previous paragraph, one may conclude that the risk of diabetes increases with age as adults comprise a significant portion of the American population with diabetes. Therefore, the cross-tabulation method would be a valuable tool in assessing healthcare trends concerning diabetes. It will help demonstrate the gross risks of age as children make for less than 1% of the entire American population with diabetes. While the disease is distributed across the spectrum of adulthood from eighteen years and beyond, the demographic aged sixty-five years and older form a disproportionately large segment of the population with diabetes. Crunching down the data exposes the linear correlation between diabetes and age, and such simplifications are essential in pinpointing the magnitude of risk that different communities face. In this regard, it is crucial to appreciate the significance of statistical analysis tools and methods that help institutions and individuals to make sense of enormous data volumes and identify important trends that shape the subsequent interventional approaches.
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Cross-tabulation of Healthcare Outcomes in Diabetes
