Now we can proceed with the ANCOVA analysis described in Exercise B1. Open the GLM Univariate dialog box again, which should already have the necessary variable information (unless you run Exercise B1 and B2 on different days). Click the Model pushbutton, select Full factorial model, then click Continue to return to the main dialog box.…

For this exercise, you will use MANOVA to test the hypothesis that there are racial/ethnic differences in scores on the SF-12, using scores from both the physical health component (sf12phys) and mental health component (sf12ment) as the dependent variables and racethn as the independent variable. Open the main dialog box through Analyze ➜ General Linear…

Re-run the analysis in exercise B3 as a MANCOVA by selecting a covariate from the data set. Was the covariate a significant predictor of the SF-12 scores? Did including the covariate in the analysis alter the relationship between racethn and SF-12 scores? Exercise B3 For this exercise, you will use MANOVA to test the hypothesis…

In this exercise, we will test racial/ethnic differences in depression over time, using CES-D scores from the two waves of interviews with a subsample of these women. You will need to begin by excluding women in the white/other group because there were too few of them in this small subsample to permit their inclusion. Go…

For these exercises, you will be using the SPSS dataset Polit2SetB. The analyses will focus on predicting the probability that a woman is in good-to-excellent health versus fair-to-poor health, which is coded 1 versus 0, respectively, on the variable health. Begin by looking at results for the odds ratio when there is only one predictor…

In this exercise, use the Logistic regression program in SPSS rather than Crosstabs to look at the bivariate relationship between health and smoker. In the Analyze ➜ Regression ➜ Binary Logistic dialog box, move health into the Dependent slot and move smoker into the Covariate slot. Click the Options pushbutton and in the next dialog…

In this next exercise, we will use five predictors to predict the probability of good health (health) in a standard logistic regression: The predictors include smoking status (smoker) and four additional predictors, which include the woman’s age (age), whether or not she is currently employed (worknow), her body mass index (bmi), and how much stress…

In this next exercise, we will again use five independent variables to predict the probability of good health (health), but instead of using bmi as a continuous variable, we will use the variable bmicat, which classifies the women based on BMI values as normal weight, overweight, obese, or morbidly obese. In the opening logistic regression…

1. Comment on the researchers’ decisions in the research example at the end of this chapter (Cˇrncˇ ec et al., 2008). What, if anything, would you recommend doing differently? 2. For these exercises, you will be using the SPSS dataset Polit2SetC. This file contains responses to individual items on the Center for Epidemiologic Studies— Depression…

Before performing a factor analysis, do a reliability analysis for the entire 20-item scale. Click Analyze ➜ Scale ➜ Reliability Analysis. Move the 16 negatively worded CESD items and the four reverse-coded items into field for items. Click the Statistics pushbutton and in the next dialog box, click Descriptives for all three options (Item, Scale,…