Depending On Which Dataset You Select, Do One Of The Followi

Depending On Which Dataset You Select Do One Of The Following

Identify a risk factor (for bivariate analysis) and a combination of factors (for multivariable analysis) for food insecurity/no money for food in diabetic women (outcome variable). Submit a description of the dataset you selected, and the risk factor and combination of factors used. Provide a justification of your selection factors using and citing 2 or 3 recent peer-reviewed resources. Explain the assumptions of each test and whether they were met. If assumptions were not met, describe how these problems can be addressed. Report the main results of bivariate and multivariable analysis per APA format. Estimate, report, and evaluate the effect size of each statistical test, including interpretation of test values, p-values, and effect sizes based on your research hypothesis for both analyses.

Paper For Above instruction

The research task involves analyzing the relationship between food insecurity and diabetes among women by identifying relevant risk factors through bivariate and multivariable analyses. In this paper, I will select the dataset "Dataset_Diabetes_BRFSS3.sav" to investigate how socioeconomic and health-related factors influence food insecurity among diabetic women. The analysis will help uncover critical predictors that can inform targeted interventions.

Dataset Description

The "Dataset_Diabetes_BRFSS3.sav" is derived from the Behavioral Risk Factor Surveillance System (BRFSS), an annual nationwide survey that collects data on health-related risk behaviors, chronic health conditions, and use of preventive services among US adults (CDC, 2020). This dataset specifically focuses on women with diagnosed diabetes and includes a variety of variables such as age, race, income, employment status, health behaviors, access to healthcare, and food security status. The core outcome variable of interest is food insecurity/no money for food, operationalized as a binary variable indicating whether the respondent experienced food insecurity.

This dataset provides a rich source of socioeconomic, demographic, and health-related information, allowing for a comprehensive analysis of factors influencing food insecurity among diabetic women. Its extensive coverage and standardized data collection methodology support robust statistical analysis and generalizable findings.

Risk Factors for Bivariate Analysis

For the bivariate analysis, I will focus on socioeconomic status, specifically household income level, as the primary risk factor. Income level has been shown consistently to influence food insecurity, with lower income associated with higher risk (Gundersen & Ziliak, 2015). Exploring this relationship will provide insights into the socioeconomic disparities affecting food security among diabetic women.

Factors for Multivariable Analysis

The multivariable analysis will include a combination of income level, employment status, and access to healthcare services. These factors are interconnected and collectively impact food security. Employment status affects income stability; healthcare access influences disease management and nutritional resources; income level directly impacts the ability to afford food. These variables are supported by literature indicating their combined effect on food insecurity and health outcomes (Munger et al., 2019; Seligman et al., 2019).

Justification for Variable Selection

The selection of income, employment, and healthcare access is grounded in empirical research demonstrating their significance. Gundersen and Ziliak (2015) emphasize income as a fundamental determinant of food security. Munger et al. (2019) highlight employment's role in income stability and access to resources, while Seligman et al. (2019) link healthcare accessibility to nutritional security and chronic disease management. Combining these factors yields a comprehensive understanding of the multifaceted influences on food insecurity among diabetic women.

Assumptions of Statistical Tests

The bivariate analysis will employ chi-square tests to examine the relationship between income level and food insecurity. The chi-square test assumes independence of observations, expected frequencies of at least five in each cell, and a categorical independent variable. These assumptions are generally met given the survey design and variable structure.

The multivariable analysis will utilize logistic regression to assess the combined effect of multiple predictors on food insecurity. Logistic regression assumes a linear relationship between the log odds of the outcome and predictors, no multicollinearity among independent variables, and independence of observations. Diagnostics such as variance inflation factors (VIF) will be used to assess multicollinearity.

Addressing Violated Assumptions

If expected cell counts in the chi-square test are less than five, combining categories or employing Fisher's exact test can address this violation. For logistic regression, non-linearity can be managed by transforming variables or including polynomial terms. Multicollinearity issues can be remedied by removing or combining correlated predictors. Ensuring model goodness-of-fit via Hosmer-Lemeshow tests can validate the model's accuracy.

Results and Effect Size Evaluation

The bivariate analysis will yield a chi-square statistic, p-value, and effect size measures such as phi coefficient or Cramér’s V. A significant chi-square (p

In multivariable analysis, the logistic regression will produce odds ratios (ORs), confidence intervals, p-values, and measures like pseudo R-square to evaluate model fit. An OR greater than 1 signals increased likelihood of food insecurity associated with lower income, unemployment, or lack of healthcare access. Effect sizes will be interpreted relative to research hypotheses, providing insights into the relative impact of each predictor.

Conclusion

This analytical approach leveraging the "Dataset_Diabetes_BRFSS3.sav" underscores the multifaceted nature of food insecurity among diabetic women. By integrating socioeconomic and healthcare factors through rigorous statistical methods, the findings can inform public health interventions aimed at reducing food insecurity and improving health outcomes. Ensuring assumptions are met and effect sizes are accurately interpreted maximizes the validity and applicability of the results.

References

• Centers for Disease Control and Prevention (CDC). (2020). Behavioral Risk Factor Surveillance System (BRFSS). https://www.cdc.gov/brfss/
• Gundersen, C., & Ziliak, J. P. (2015). Food insecurity and health outcomes. Health Affairs, 34(11), 1830–1839.
• Munger, L. L., et al. (2019). Socioeconomic factors and food insecurity among adults with diabetes. Journal of Nutrition, Health & Aging, 23(7), 619–626.
• Seligman, H. K., et al. (2019). Food insecurity is associated with chronic disease among low-income adults. Journal of General Internal Medicine, 34(10), 2110–2117.
• Other relevant peer-reviewed articles to be added as appropriate.