Statistical Practice: Chi-square Testing
For study results to be meaningful, it is important to ensure that there are no erroneous imbalances in the data that could lead the researcher to a false conclusion. A properly designed study should anticipate where such imbalances might occur and attempt to avoid them through careful planning. In this scenario, we will assume that the study was well planned, but due to circumstances beyond the control of the researcher, some of the recruited participants did not finish the study. The researcher is concerned that there may be an imbalance in the number of male and female groups receiving the standard or vegetarian diet treatment. If there are not enough males relative to the females in the vegetarian treatment group, the results may be misleading.
Instructions
For this discussion, use the Alaska study’s Emotional Well-Being data set to perform the chi-square test of independence on the variables Gender and Dietary Treatment.
The chi-square test will help determine if there is a gender imbalance in the data set. Although this chi-square test is considered fairly easy to understand and perform, you may find it useful to find other how-to videos on the Internet. Feel free to share anything you have found to be particularly enlightening.
For your initial post, refer to the guidelines in the FEM as you complete the following:
- Describe, in your own words, when to use the chi-square test of independence.
- Using the Emotional Well-Being data set linked in Resources, report the results of the chi-square test of independence that you perform.
- Based on the results, provide a 1-2 sentence practical interpretation.
Response Guidelines
Read and respond to the posts of your peers according to the guidelines in the FEM.
Addressthe following in your response:
- How do the interpretations ofyour peers compare to yours?
Learning Components
This activity will help you achieve the following learning components:
- Identify a normal distribution assumption test for two variables.
- Perform a normal distribution assumption test for two variables to determine if data is normally distributed.
- Identify an appropriate correlation test to determine the direction and strength of the relationship of two variables.
- Perform an appropriate correlation test to determine the direction and strength or magnitude of the relationship between two variables.
- Interpret the effect size for correlation analysis results.
Resources