Overview
Project Overview: In this second project, you will extend your analysis of the General Social Survey (GSS) 1972–2024 dataset to explore multivariate relationships between lifestyle factors and mental health outcomes. Focus on variables like mental health days (MNTLHLTH, recoded into categories: 0=None, 1-5=Low, 6+=High) as the dependent variable, influenced by demographics (e.g., educ_cat, SEX, RACEHISP) and behaviors (SMOKE, DRINK). This builds toward understanding intersections in public health, such as how smoking and education jointly affect mental well-being, to propose targeted prevention strategies. Objectives:
Prepare and transform data for multivariate exploration. Generate descriptive summaries and visualizations to identify trends. Analyze bivariate and multivariate associations using tables and tests. Compare groups to detect significant patterns and interactions. Interpret findings with public health recommendations in a report.
Question 1 Investigate the relationship between education level (educ_cat) and mental health days (MNTLHLTH categories) using a layered crosstab with SEX as the control variable. Include column percentages, Chi-square test, and an appropriate measure of association. Interpret any interactions observed and discuss implications for gender-specific mental health programs.
Question 2 Conduct a factorial group comparison on MNTLHLTH by educ_cat and SMOKE, checking for main effects and interactions. Report the results, including significance levels and post-hoc tests if applicable. Based on the findings, propose two evidence-based public health strategies to address smoking-related mental health disparities in low-education populations.
Required Resources:
IBM SPSS Statistics software. GSS dataset from https://gss.norc.org/get-the-data/spss. Key Variables: MNTLHLTH (dependent), EDUC/SEX/RACEHISP/AGE (demographics, recode as needed), SMOKE/DRINK (behaviors).
Step-by-Step Instructions:
Data Setup and Preparation:
Open GSS.sav and save a copy. Handle missing values and recode variables (e.g., MNTLHLTH into categories, INCOME to low/medium/high). Subset data (e.g., recent years) and apply weights for accuracy.
Exploratory and Descriptive Analysis:
Compute frequencies and means for key variables, including charts like bar graphs for MNTLHLTH by SEX. Use boxplots to visualize distributions (e.g., AGE by educ_cat).
Bivariate and Crosstab Analysis:
Create crosstabs (e.g., MNTLHLTH by educ_cat; include column percentages, Chi-square, and association measures like Cramer's V). Layer variables (e.g., add SEX and RACEHISP) to examine interactions. Test proportions for significant differences across groups.
Group Comparisons:
Run one-way comparisons (e.g., means of MNTLHLTH categories by educ_cat). Perform factorial analysis (e.g., MNTLHLTH by educ_cat and SMOKE, checking for interactions).
Verify assumptions like equal variances.
Deliverables:
SPSS syntax and output files.
Written report with interpretations.
Brief PPT Presentation