In this exercise, run a hierarchical multiple regression analysis to predict cesd, using the same predictors as in Exercises B3 and B4. Select the variables you would like to enter in each step, being sure to think of a reasonable rationale for the order of entry. In the first SPSS Linear Regression dialog box, enter cesd as the Dependent variable, and the predictor(s) you wish to enter in the first step in the Independent slot. Then, click the pushbutton Next in the area labeled “Block.” Now you can select your second block of predictors. Keep clicking Next Block for each set of predictors, until all seven predictors are included. The Method box should say “Enter.” Examine your output, and pay special attention to changes in R2 for each successive block of variables
In this exercise, run a stepwise multiple regression to predict depression scores, using the same predictors as in Exercise B3. In the first SPSS Linear Regression dialog box, enter cesd as the Dependent variable and the list of predictors in the Independent slot, as in the previous exercise. For Method, select “Stepwise.” For the Statistics options, you can omit Descriptives and Collinearity Diagnostics because you have already examined these, but this time you should select R squared change. Run the analysis and then answer the following questions: (a) How many predictors were entered before the regression stopped? (b) Which predictor variables made it into the regression—and which did not? Was the order of entry of predictors consistent with the values of the zero-order correlations? (c) Looking at the Model Summary panel, what was the progression of the value of R2 from one step to the next—and were these changes significant? What happens to the standard error of the estimate with each progressive step? (d) Were all models statistically significant—that is, was the value of R2 greater than zero at each step? (e) Looking at the “Excluded Variables” panel—and focusing on Model 4, were any of the remaining predictors statistically significant?
In this exercise, you will run a simultaneous multiple regression analysis to predict the women’s level of depression (scores on the CESD depression scale, cesd) based on several demographic characteristics, socioeconomic characteristics, health status, and self-reported incidence of abuse in the prior year. The list of predictors is as follows: the two race/ethnicity dummy variables you created in exercise B2; age; educatn (educational attainment); worknow (a dummy variable for currently employed); nabuse (number of different types of abuse experienced in the past year, including verbal abuse, efforts to control, threats of harm, and physical abuse); and poorhlth (a dummy coded variable indicating self-reported poor health at the time of the interview. Bring up the regression dialog box by selecting Analyze ➞ Regression ➞ Linear. Insert the variable cesd in the box labeled Dependent. Insert the 7 predictor variables that we just mentioned into the box for Independent(s). Make sure that Method is set to “Enter,” the command for entering all predictors simultaneously. Click the Statistics pushbutton and then select the following options: Estimates (under “Regression Coefficients”); Model Fit; Descriptives; and Collinearity Diagnostics. Then click Continue, and OK to run the analysis. Answer the following questions: (a) How large is the sample on which the regression analysis was run? (b) Interpret the mean value for poor health self-rating. (c) Which predictor has the highest zero-order correlation with cesd? (d) What were the values of R2 and adjusted R2 ? (e) Which predictors in the analysis were significantly predictive of the women’s depression scores, once other predictors were included? Which were not significantly predictive? (f) For this sample of women, which predictor variable appeared to be the most powerful in predicting depression? (g) Did any of the tolerance levels suggest a problem with multicollinearity?
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