Conducting Bivariate Correlation Analysis With PASW Download Ex9.1.dat from David Howell’s Data Files Page if you are not a student in my PSYC 2101 class. If you are such a student then download Ex9-1.sav from BlackBoard – Assignments, Correlation and Regression. Bring it into PASW. The data are those for exercise 9-1, and are shown on page 237 of your text book. (a) Make the indicated scatter plot. Graphs, Legacy Dialogs, Scatter/Dot. Select “Simple Scatter” and then click “Define.”
Transcript
Conducting Bivariate Correlation Analysis With PASW
Download Ex9.1.dat from David Howell’s Data Files Page if you are not a student in my PSYC 2101 class. If you are such a student then download Ex9-1.sav from BlackBoard – Assignments, Correlation and Regression. Bring it into PASW. The data are those for exercise 9-1, and are shown on page 237 of your text book.
(a) Make the indicated scatter plot. Graphs, Legacy Dialogs, Scatter/Dot.
Associate INFMORT with the Y Axis and INCOME with the X Axis.
Click “OK.”
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(b) In your mind, visualize the line that would best fit these data. Then double click on the plot to open the editor. Then click Elements, “Fit Line at Total.”
PASW draws the best fitting line for you. How does it compare with that you visualized? Notice that r2 is .309.
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(c) Explore the income variable.
The boxplot shows two way-outliers, cases 9 and 17. If you look back at the data you will see that case 9 is Gabon and case 17 is Namibia. How do you think the results would change if we were to remove these two cases?
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Howell has already created a Filter variable, Filter_$, that can be used to filter out these cases. Click Data, “Select Cases.” Select “Use filter variable” and identify “FILTER_$” as the filter variable. Click OK.
Now recreate the scatter-plot with best-fitting line. Sometimes removing outliers will drastically change the value of the correlation coefficient, but in this case you will find that the negative correlation between income and infant mortality changes very little when the way-outliers are filtered out, r2 = .291.
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{9.2} Turn the filtering off: Data, Select cases, “Use all,” and then find the correlations between all numeric variables. Analyze, Correlate, Bivariate.
Click OK. Identify any statistically significant correlations and interpret them.
Karl L. Wuensch, Ph.D., Professor, Department of PsychologyEast Carolina University, Rawl 137, Greenville, NC 27858-4353Web: http://core.ecu.edu/psyc/wuenschk/klw.htm
