1. Correlation & Regression

2. The Data http://core.ecu.edu/psyc/wuenschk/SPSS/ SPSS-Data.htmhttp://core.ecu.edu/psyc/wuenschk/SPSS/ SPSS-Data.htm Corr_Regr –See Correlation and Regression Analysis: SPSSCorrelation and Regression Analysis: SPSS Master’s Thesis, Mike Sage, 2015 Cyberloafing = Age, Conscientiousness

3. Analyze, Correlate, Bivariate

4. Pearson Correlations CyberloafingAgeConscientiousness Cyberloafing Pearson Correlation1-.462 ** -.563 ** Sig. (2-tailed).001.000 N51 Age Pearson Correlation-.462 ** 1.143 Sig. (2-tailed).001.317 N51 Conscientiousness Pearson Correlation-.563 **.1431 Sig. (2-tailed).000.317 N51 **. Correlation is significant at the 0.01 level (2-tailed).

5. Spearman Correlations CyberloafingAgeConscientiousness Spearman's rho Cyberloafing Correlation Coefficient 1.000-.431 ** -.551 ** Sig. (2-tailed)..002.000 N51 Age Correlation Coefficient -.431 ** 1.000.110 Sig. (2-tailed).002..442 N51 Conscientiousness Correlation Coefficient -.551 **.1101.000 Sig. (2-tailed).000.442. N51 **. Correlation is significant at the 0.01 level (2-tailed).

6. Analyze, Regression, Linear

7. Statistics

8. Plots

9. r =.1 is small,.3 medium,.5 large Model Summary b ModelRR Square Adjusted R Square Std. Error of the Estimate 1.563 a.317.3037.677 a. Predictors: (Constant), Conscientiousness b. Dependent Variable: Cyberloafing

10. Coefficients a ModelUnstandardized Coefficients Standardized Coefficients tSig. BStd. ErrorBeta 1 (Constant)57.0397.288 7.826.000 Conscientiousness-.864.181-.563-4.768.000 a. Dependent Variable: Cyberloafing Cyberloafing = 57.039 -.864(Conscientiousness) + error t Consc. = 57.039/7.288 = 7.826 = SQRT(22.736) = SQRT(F)

11. Residuals Histogram

12. Graphs, Scatter, Simple, Define

13. Chart Editor, Elements, Fit Line at Total, Method = Linear, Close

17. Construct a Confidence Interval for  the calculator at Vassar

18. Trivariate Analysis

19. Statistics

20. Plots

21. R2R2 Adding Age increased R 2 from.317 to.466. ModelRR Square Adjusted R Square 1.682 a.466.443

22. ANOVA ANOVA a Model Sum of Squares df Mean Square FSig. 1 Regression1968.0292984.01520.906.000 b Residual2259.3044847.069 Total4227.33350

23. Coefficients ModelUnstandardized Coefficients BStd. Error 1 (Constant)64.0666.792 Conscientiousness-.779.164 Age-.276.075

24. Unstandardized Coefficients Cyberloaf = 64.07 -.78 Consc -.28 Age When Consc and Age = 0, Cyber = 64.07 Holding Age constant, each one point increase in Consc produces a.78 point decrease in Cyberloafing. Holding Consc constant, each one point increase in Age produces a.28 point decrease in Cyberloafing.

25. How Large are these Effects? Is a.78 drop in Cyberloafing a big drop or a small drop? When the units of measurement are arbitrary and not very familiar to others, best to standardize the coefficients to mean 0, standard deviation 1. Z Cyber = 0 +  1 Consc +  2 Age

26. More Coefficients tSig.Correlations BetaZero-orderPartialPart Constant 9.433.000 Conscie -.507-4.759.000-.563-.566-.502 Age -.389-3.653.001-.462-.466-.386

27. Beta Weights Z Cyber = 0 -.51Consc -.39Age Holding Age constant, each one SD increase in Conscientiousness produces a.51 SD decrease in Cyberloafing Holding Conscientiousness constant, each one SD increase in Age produces a.39 SD decrease in Cyberloafing.

28. Semi-Partial Correlations The correlation between all of Cyberloafing and that part of Conscientiousness that is not related to Age = -.50. The correlation all of Cyberloafing and that part of Age that is not related to Conscientiousness = -.39.

29. Partial Correlations The correlation between that part of Cyberloafing that is not related to Age and that part of Conscientiousness that is not related to Age = -.57. The correlation between that part of Cyberloafing that is not related to Conscientiousness and that part of Age that is not related to Conscientiousness = -.47.

30. Multicollinearity The R 2 between any one predictor and the remaining predictors is very high. Makes the solution unstable. Were you to repeatedly get samples from the same population, the regression coefficients would vary greatly among samples

31. Collinearity Diagnostics Tolerance, which is simply 1 minus the R 2 between one predictor and the remaining predictors. Low (.1) is troublesome. VIF, the Variance Inflation Factor, is the reciprocal of tolerance. High (10) is troublesome.

32. Coefficients a ModelCollinearity Statistics ToleranceVIF 1 Age.9801.021 Conscientiousness.9801.021

33. Residuals Residuals Statistics a MinimumMaximumMean Std. Deviation N Predicted Value10.2235.4122.676.27451 Residual-17.34415.153.0006.72251 Std. Predicted Value -1.9832.032.0001.00051 Std. Residual-2.5282.209.000.98051 No standardized residuals beyond 3 SD.

34. Residuals Histogram

35. Residuals Plot

36. Put a CI on R 2 http://core.ecu.edu/psyc/wuenschk/SPSS/ SPSS-Programs.htmhttp://core.ecu.edu/psyc/wuenschk/SPSS/ SPSS-Programs.htm CI-R2-SPSS.zip -- Construct Confidence Interval for R 2 from regression analysisCI-R2-SPSS.zip –Using SPSS to Obtain a Confidence Interval for R2 From Regression -- instructionsUsing SPSS to Obtain a Confidence Interval for R2 From Regression –NoncF.sav -- necessary data fileNoncF.sav –F2R2.sps -- see Smithson's WorkshopF2R2.spsSmithson's Workshop –NoncF3.sps -- syntax fileNoncF3.sps

37. Open NoncF.sav Enter the observed value of F and degrees of freedom.

38. Open and Run the Syntax

39. Look Back at.sav File

40. Why You Need Inspect Scatterplots Data are at http://core.ecu.edu/psyc/wuenschk/SPSS/ Corr_Regr.sav http://core.ecu.edu/psyc/wuenschk/SPSS/ Corr_Regr.sav Four sets of bivariate data. Bring into SPSS and Split File by “set.”

41. Predict Y from X in Four Different Data Sets