For this assessment, you will determine the relevant statistical tests to apply to the analysis of a data set, and then write a 3–4 page interpretation of the results of your analysis. This assessment will ask you to select, apply, and interpret the results of a variety of statistical tests on a health care data set. This may include tests you have learned about or applied previously in the course, or the new nonparametic -Test which is presented in the resources for this assessment. The challenge is using what you have learned to determine the best course of action to complete the interpretative tasks the assessment lays out for you. This attempts to mirror real-world situations where the data or statistical analysis could be approached in a variety of different ways. To decide which statistical test to use for the various dependent variables to be analyzed, one must first know more about the data type (measurement level) within those variables. Public health researchers are often involved in collaborating in the design, development, and analysis of community initiatives of varying complexity. While this course alone will not provide sufficient training for you to act as a statistical consultant, it does offer a broad and practice-based analytic foundation that can position you to better understand and more fully contribute to real-world project teams. Building on the basic statistical concepts and analytical techniques of the previous units, this assessment is an opportunity to use your cumulative quantitative-analysis skills to address a broad set of real-world research questions. By successfully completing this assessment you will address the following scoring guide criteria, which align to the indicated course competencies. Complete the following for this two-part assessment. The following statistical analysis software is required to complete your assessments in this course: You have access to the more robust IBM SPSS Statistics GradPack. Please refer to the page on Campus for general information on SPSS software, including the most recent version made available to Capella learners.

Introduction

In this assessment, we will be examining a health care data set and applying appropriate statistical tests to analyze the data. To determine the best statistical tests to use, we need to consider the measurement level of the variables within the data. Public health researchers often collaborate in the design, development, and analysis of community initiatives, and having a strong foundation in statistical analysis can contribute to the success of these projects.

Objectives

The objectives of this assessment are twofold: first, to select and apply the appropriate statistical tests to analyze the health care data set, and second, to interpret the results of the analysis. This will require us to apply various statistical tests, including those previously learned in the course and the new nonparametric t-test discussed in the resources provided.

Data Analysis Process

To begin the data analysis process, we first need to determine the measurement level of the variables in the data set. There are four levels of measurement: nominal, ordinal, interval, and ratio. The measurement level of a variable determines the statistical tests that can be applied to it.

If a variable is at the nominal level, we can use tests such as the chi-square test or the binomial test. If the variable is at the ordinal level, we can use tests such as the Mann-Whitney U test or the Kruskal-Wallis test. For interval or ratio level variables, tests such as the t-test or analysis of variance (ANOVA) can be used.

Once we have determined the measurement level of each variable, we can select the appropriate statistical test for analysis. We will apply these tests to the health care data set and examine the results.

Interpretation of Results

After applying the statistical tests, we will interpret the results of the analysis. This involves understanding the statistical significance of the findings and making meaningful conclusions based on the data.

Statistical significance refers to the likelihood that the observed results are not due to chance. This is typically indicated by a p-value, which is the probability of obtaining results as extreme or more extreme than the ones observed, assuming the null hypothesis is true. A p-value below a certain threshold, typically 0.05, is considered statistically significant.

When interpreting the results, we need to consider the context of the research question and the practical significance of the findings. It is important to go beyond statistical significance and understand the implications of the results in real-world terms.

Conclusion

In conclusion, this assessment involves selecting and applying appropriate statistical tests to analyze a health care data set. By considering the measurement level of the variables, we can determine the most suitable statistical tests to use. The interpretation of the results involves understanding the statistical significance and making meaningful conclusions based on the data. By successfully completing this assessment, we will demonstrate our ability to apply statistical analysis to real-world research questions in a public health context.