It is tempting to think of the results obtained in the previous three analyses as suggesting a causal link between the women’s abuse experiences and their level of depression— that is, inferring that being abused caused higher levels of depression. However, the opposite might be the case. For example, women who are depressed, lethargic, or absorbed with personal problems might incite others to yell at them, threaten them, or hit them. In the next analysis, we explore the issue of direction of influence, though we caution against firm causal conclusions. In this hierarchical regression, we will statistically control the women’s level of depression 2 years earlier and then see if recent abuse experiences affected current depression, with earlier depression held constant. This is analogous to asking whether recent abuse was related to changes in depression. In the first SPSS Linear Regression dialog box, enter cesd as the Dependent variable, and cesdwav1 as the predictor in the Independent slot. Then, click the pushbutton Next in the area labeled “Block.” Now enter nabuse (number of types of abuse) as the predictor in the second block. The Method box should say “Enter.” For statistical options select Estimates for the Regression Coefficients, Model Fit, and R squared change. Then run the analysis and answer these questions based on the output: (a) What was the correlation between the CES-D scores in the two waves of data collection? (b) Was R2 statistically significant at both steps of the analysis? (c) What was the change to R2 when abuse was added to the regression? Was this significant—and, if so, what does this suggest? (d) If you wanted to predict a woman’s current CES-D score based on this analysis, what would the unstandardized regression equation be?
