A researcher plans to use a sample of 500 men and women attending a well-known online university to study the relationship between the number of fast-food hamburgers consumed in a month and BMI (body mass index). The null hypothesis is that there is no relationship between fast-food hamburger consumption and BMI.
By Day 4, post a description the statistical test the researcher would use to test the null hypothesis. Then, describe how that would change if the dependent variable was yes/no obesity instead of BMI. What cofounders should be included in this analysis? Finally, explain the importance of including these potential confounders in any tests of this relationship
A researcher plans to use a sample of 500 men and women attending a well-known online university to study the relationship between the number of fast-food hamburgers consumed in a month and BMI (body mass index). The null hypothesis is that there is no relationship between fast-food hamburger consumption and BMI.
how that would change if the dependent variable was yes/no obesity instead of BMI. What cofounders should be included in this analysis?
To test the null hypothesis that there is no relationship between fast-food hamburger consumption and BMI, the researcher can use a statistical test called correlation analysis. Specifically, they can perform a Pearson correlation coefficient test.
Pearson correlation coefficient measures the strength and direction of the linear relationship between two continuous variables. In this case, the variables of interest are the number of fast-food hamburgers consumed in a month and BMI. By calculating the Pearson correlation coefficient and performing a hypothesis test, the researcher can determine whether there is a significant relationship between these variables.
If the dependent variable was changed to yes/no obesity instead of BMI, the statistical test would need to be adjusted. In this case, logistic regression analysis would be more suitable. Logistic regression allows for the prediction of a binary outcome (obesity in this case) based on one or more predictor variables (fast-food hamburger consumption in this case).
When analyzing relationships between variables, it is important to consider potential confounders. Confounders are variables that are associated with both the independent and dependent variables, and they can distort the true relationship between them. In this case, potential confounders that should be included are age, gender, physical activity level, overall diet, socioeconomic status, and genetic factors. These variables may have an impact on both fast-food hamburger consumption and BMI or obesity independently, and failing to account for them could lead to incorrect conclusions or biased results.
Including potential confounders in the analysis is crucial because it helps to isolate the true relationship between fast-food hamburger consumption and BMI or obesity. By accounting for other factors that can influence the outcome, the researcher can determine whether the observed relationship is independent or confounded by these factors. This provides a more accurate understanding of the specific relationship being investigated and strengthens the validity of the results.