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Topics: Regression analysis, Linear regression, Prediction interval Pages: 6 (3544 words) Published: October 27, 2014

Correlation and Regression Analysis: SPSS

Obtain from my SPSS Data Page the following files: KJ.sav, Poffhoff.sav, and Corr_Regr.savBivariate Analysis: Attitudes About Animals Predicted from Misanthropy One day as I sat in the living room, watching the news on TV, there was a story about some demonstration by animal rights activists. I found myself agreeing with them to a greater extent than I normally do. While pondering why I found their position more appealing than usual that evening, I noted that I was also in a rather misanthropic mood that day. That suggested to me that there might be an association between misanthropy and support for animal rights. When evaluating the ethical status of an action that does some harm to a nonhuman animal, I generally do a cost/benefit analysis, weighing the benefit to humankind against the cost of harm done to the nonhuman. When doing such an analysis, if one does not think much of humankind (is misanthropic), e is unlikely to be able to justify harming nonhumans. To the extent that one does not like humans, one will not be likely to think that benefits to humans can justify doing harm to nonhumans. I decided to investigate the relationship between misanthropy and support of animal rights. Mike Poteat and I developed an animal rights questionnaire, and I developed a few questions designed to measure misanthropy. One of our graduate students, Kevin Jenkins, collected the data we shall analyze here. His respondents were students at ECU. I used reliability and factor analysis to evaluate the scales (I threw a few items out). All of the items were Likerttype items, on a 5point scale. For each scale, we computed each respondent's mean on the items included in that scale. The scale ran from 1 (strongly disagree) to 5 (strongly agree). On the Animal Rights scale (AR), high scores represent support of animal rights positions (such as not eating meat, not wearing leather, not doing research on animals, etc.). On the Misanthropy scale (MISANTH), high scores represent high misanthropy (such as agreeing with the statement that humans are basically wicked). Bring the data, KJ.sav, into SPSS. Click Analyze, Correlate, Bivariate. Move all three variables into the Variables box. Ask for Pearson and Spearman coefficients, two-tailed, flagging significant coefficients. Click OK. Look at the output. The only significant correlation is that between misanthropy and support for animal rights.

Click Analyze, Regression, Linear. Scoot the AR variable into the Dependent box and Misanthropy into the Independent(s) box.

Click on “Statistics.” “Estimates” and “Model Fit” should already be selected. Select “Casewise diagnostics” as well. Click “Continue,” OK.

Look at the output. You get the same value of r obtained with the correlation analysis, of course. The r2 shows that although significant, our linear model does not explain much of the variance in support for animal rights (only between 4 and 5 percent). The adjusted R2, also known as the “shrunken R2,” is a relatively unbiased estimator of the population 2. For a bivariate regression it is computed as: .

Let’s now create a scatterplot. Click Graphs, Legacy Dialogs, Scatter/Dot, Simple Scatter, Define. Scoot AR into the Y axis box and Misanth into the X axis box. Click OK.

Go to the Output window and double click on the chart to open the chart editor. Click Elements, Fit Line at Total, Fit Method = Linear, Close. You can also ask SPSS to draw confidence bands on the plot, for predicting the mean Y given X or individual Y given X or both (to get both, you have to apply the one, close the editor, open the editor again, apply the other).

You can also edit the shape, density, and color of the marker and the lines. While in the Chart Editor, you can Edit, Copy Chart and then paste the chart into Word. You can even ask SPSS to put in a quadratic (Y = a +b1X + b2X2 + error) or cubic (Y = a +b1X + b2X2...
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