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.
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.
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.