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  Using your original data set, perform the following operations…

 

Using your original data set, perform the following operations to include in your report:

Select a continuous variable from the data set and provide full definition for each variable.
Convert a continuous variable to a categorical variable (which may be used to answer one of your research questions).
Provide an explanation of how you converted the variable.
Produce frequency tables for the new variable you made.
If any of the cells in your frequency table has fewer than 6 individuals in it, combine that level with other levels so there is no frequency less than 6.
Submit descriptive statistics for the new variables.

Background information:

 

Research question: What is the association between familial and community risk factors and alcohol consumption?

Null hypothesis: No association exists between familial and community risk factors and alcohol consumption.

We can use the NHANES dataset to answer this research question and perform a multiple regression analysis. We can select a categorical variable, such as gender, to dummy code as one of our predictor variables (Warner, 2021).

Interpret the coefficients of the model, explicitly commenting on the dummy variable:

The multiple regression model to answer the research question is:  

Alcohol consumption = ß0 + ß1(Familial risk factors) + ß2(Community risk factors) + ß3(Gender) + e. Where ß0 is the intercept, ß1 and ß2 are the coefficients for familial and community risk factors, respectively, ß3 is the coefficient for gender (dummy coded as 0 for males and 1 for females), and e is the error term (Warner, 2021).

Interpretation of Coefficients:

The coefficients for the model represent the change in the outcome variable (alcohol consumption) for a unit change in the predictor variable while holding other variables constant. The coefficient for the dummy variable (gender) indicates the difference in the mean alcohol consumption between males and females.

Diagnostic tests

To check the assumptions of the multiple regression model, we can run diagnostic tests such as the normality test of residuals, linearity test, homoscedasticity test, and multicollinearity test. If any of the assumptions are not met, it may affect the accuracy of the model’s results. For instance, if the normality assumption is violated, the p-values and confidence intervals may not be accurate.

Complex Samples Multiple Regression Model:

We can estimate a complex sample multiple regression model using the complex sample weighting file available from the course.

This model’s coefficients and confidence intervals may differ from the previous model, as the complex samples weighting file adjusts for the intricate sampling design of the NHANES dataset.

Contribution to Positive Social Change: 

The results of these models can contribute to positive social change by providing insights into the relationship between familial and community risk factors and alcohol consumption. These insights can help policymakers and healthcare professionals design and implement effective prevention and intervention strategies to reduce alcohol consumption and its associated health risks.

 

References:

Centers for Disease Control and Prevention. (2017). National health and nutrition examination survey: 2015-2016 data documentation, codebook, and frequenciesLinks to an external site.. https://wwwn.cdc.gov/nchs/nhanes/2015-2016/DEMO_I.htm