Two distinct analyses are conducted to explore the factors influencing the health and well-being of women. Part 1 seeks to answer the question of which variables predict depression levels among low-income women. The dataset Polit2SetC in SPSS is utilized, and the analysis reveals that several predictor variables, including education, income, employment status, physical health, and mental health, significantly influence depression levels in this sample. Part 2, predicts health status, while controlling for age and BMI. The dataset Polit2SetB in SPSS is used for this purpose.
Part 1: Multiple Regression Analysis
Question: What variables predict the level of depression among low-income women?
Dataset: Polit2SetC in SPSS
- CESD: Depression Score
- AGE: Chronological Age
- EDUCATN: Educational Attainment
- INCOME: Family Income Prior Month
- WORKNOW: Current Employment Status
- SF12PHYS: SF-12 Physical Health Component Score
- SF12MENT: SF-12 Mental Health Component Score
Regression Model Predicting Depression in Low-Income Women:
|Predictor Variable||b||Standard Error||Beta||t||p-value|
Results Summary: In this sample of low-income women, 5 out of the 6 predictor variables were significant predictors of depression levels, after controlling for all other variables in the model (F = 114.71, p < 0.01). Education (Beta = -0.06, p = 0.03), income (Beta = -0.08, p = 0.01), work status (Beta = -0.06, p = 0.03), physical health (Beta = -0.14, p < 0.01), and mental health (Beta = -0.61, p < 0.01) were significant predictors of women's depression levels. Higher education, higher income, being employed, and having better physical and mental health were associated with lower levels of depression. Age (Beta = 0.01, p = 0.85) was not a significant predictor in this sample. Women's mental health score was the strongest predictor of depression. In total, slightly less than half of the variance (adjusted R2 = 0.46, p < 0.01) in depression levels was explained by this set of predictor variables.
Part 2: Logistic Regression Analysis
Question: Does smoking status predict health status, after controlling for age and BMI?
Dataset: Polit2SetB in SPSS
- Dependent Variable: HEALTH (0 = Fair to Poor Health, 1 = Good to Excellent Health)
- Independent Variables: SMOKER (Smoking Status), BMI (Respondent's BMI), AGE (Chronological Age)
Logistic Regression Results:
- In the logistic regression analysis, 869 cases were included.
- In the null model (Block 0), 71.3% of cases were correctly classified.
- In the null model (Block 0), 620 cases (100%) were predicted to be in good to excellent health.
- In the full model (Block 1), 71.8% of cases were correctly classified, with a marginal improvement.
- 245 cases were misclassified.
- 7 cases were predicted to be in fair/poor health but were observed to be in good/excellent health.
- 238 cases were predicted to be in good/excellent health but were observed to be in fair/poor health.
- The most common misclassification was overestimation.
- 98.9% of those observed to be in good to excellent health were correctly classified.
Results Summary: The purpose of this analysis was to determine if smoking predicts health status, after controlling for age and BMI. The odds ratio for smoking in this analysis was with a 95% CI). This suggests that, with other variables controlled, the odds of being in good/excellent health declined for women who smoked. Based on the CI, there is a reduction in the odds of being in good/excellent health at worst and a reduction at best for smokers. The 95% CI (does/does not) include 1.0, indicating (statistical significance/lack of statistical significance).
Of the three predictor variables in the model, smoking had the (strongest/weakest) effect on the odds of being in good/excellent health. The other variables had a smaller effect on the odds: BMI (OR = ?) and AGE (OR = ?). Thus, every unit increase in BMI was associated with a reduction in the odds of being in good health. Every year of advancing age was associated with a reduction in the odds of being in good health.