A combination analysis of two variables, “armed” and “fleeing,” involves examining how these two categorical variables are related or combined within a dataset. It aims to explore the patterns and associations between the different categories of these variables and may be useful for understanding various aspects of police incidents, such as the behavior of individuals involved.
This is what I am going to do to perform combination analysis for the “armed” and “fleeing” variables:
- Data Preparation:
- First, ensure that the data is appropriately cleaned and organized, and that the “armed” and “fleeing” variables are categorical in nature, meaning they represent distinct categories or labels.
- Cross-Tabulation (Contingency Table):
- Create a cross-tabulation (also known as a contingency table) that shows the counts or frequencies of each combination of categories. The rows represent the categories of the “armed” variable, and the columns represent the categories of the “fleeing” variable.
- The cells of the table display the count of occurrences for each combination, showing how many incidents fall into each category.
- Chi-Square Test of Independence:
- To assess the statistical significance of the association between the “armed” and “fleeing” variables, you can perform a chi-square test of independence. This test evaluates whether there is a significant relationship between the two variables or if their distributions are independent.
- The null hypothesis of the chi-square test is that the two variables are independent, and the alternative hypothesis is that they are dependent.
- If the p-value obtained from the test is below a chosen significance level (e.g., 0.05), you may conclude that there is a significant association between the two variables.