Annotated Output from the Correlation/Regression SPSS Lesson [DataSet1] C:\Users\Vati\Desktop\Cyberloaf_Consc_Age.sav

Statistics

Cyberloafing Age Conscientiousn

ess

N Valid 51 51 51

Missing 0 0 0

Mean 22.67 37.82 39.76

Median 23.00 33.00 41.00

Std. Deviation 9.195 12.995 5.989

Skewness .008 .941 -.269

Std. Error of Skewness .333 .333 .333

Kurtosis -.691 -.091 -.882

Std. Error of Kurtosis .656 .656 .656

Minimum 4 22 28

Maximum 43 71 50

The skewness statistics above as well as the plots below show that the cyberloafind and Conscientiousness variables are close to normal in

their distribution but that there is a distinct positive skew in the ages – but not quite large enough for me to worry about. Histogram

Pearson Correlations

Cyberloafing Age Conscientious

ness

Cyberloafing

Pearson Correlation 1 -.462** -.563

**

Sig. (2-tailed) .001 .000

N 51 51 51

Age

Pearson Correlation -.462** 1 .143

Sig. (2-tailed) .001 .317

N 51 51 51

Conscientiousness

Pearson Correlation -.563** .143 1

Sig. (2-tailed) .000 .317

N 51 51 51

**. Correlation is significant at the 0.01 level (2-tailed).

Spearman Correlations

Cyberloafing Age Conscientious

ness

Spearman's rho

Cyberloafing

Correlation Coefficient 1.000 -.431** -.551

**

Sig. (2-tailed) . .002 .000

N 51 51 51

Age

Correlation Coefficient -.431** 1.000 .110

Sig. (2-tailed) .002 . .442

N 51 51 51

Conscientiousness

Correlation Coefficient -.551** .110 1.000

Sig. (2-tailed) .000 .442 .

N 51 51 51

**. Correlation is significant at the 0.01 level (2-tailed). Regression

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .563a .317 .303 7.677

a. Predictors: (Constant), Conscientiousness

b. Dependent Variable: Cyberloafing

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 1339.801 1 1339.801 22.736 .000b

Residual 2887.532 49 58.929

Total 4227.333 50

a. Dependent Variable: Cyberloafing

b. Predictors: (Constant), Conscientiousness

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 57.039 7.288 7.826 .000

Conscientiousness -.864 .181 -.563 -4.768 .000

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 13.82 32.84 22.67 5.176 51

Residual -17.056 18.859 .000 7.599 51

Std. Predicted Value -1.709 1.965 .000 1.000 51

Std. Residual -2.222 2.457 .000 .990 51

a. Dependent Variable: Cyberloafing

The largest absolute standardized residual has value 2.22. It might be wise to investigate this case. Charts

The residuals appear to be distributed pretty close to normally. Cyberloafing was significantly negatively correlated with Conscientiousness, Cyberloafing = 57.04 - .864*Conscientiousness, t(49) = 4.768, p < .001, r = -.563, 95% CI [ -.725,

Here we add a second predictor variable, Age.

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .682a .466 .443 6.861

a. Predictors: (Constant), Age, Conscientiousness

b. Dependent Variable: Cyberloafing

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 1968.029 2 984.015 20.906 .000b

Residual 2259.304 48 47.069

Total 4227.333 50

a. Dependent Variable: Cyberloafing

b. Predictors: (Constant), Age, Conscientiousness

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig. Correlations

B Std. Error Beta Zero-order Partial Part

1

(Constant) 64.066 6.792 9.433 .000

Conscientiousness -.779 .164 -.507 -4.759 .000 -.563 -.566 -.502

Age -.276 .075 -.389 -3.653 .001 -.462 -.466 -.386

a. Dependent Variable: Cyberloafing

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 10.22 35.41 22.67 6.274 51

Residual -17.344 15.153 .000 6.722 51

Std. Predicted Value -1.983 2.032 .000 1.000 51

Std. Residual -2.528 2.209 .000 .980 51

a. Dependent Variable: Cyberloafing

Charts

The plots above reveal no problems with normality of the residuals or with heteroscedasticity.

The multiple regression model predicting cyberloafing from Conscientiousness and age was significant, F(2, 48) = 20.91, p < .001, R2 = .466,

90% CI [.272, .577]. Both Conscientiousness ( = -.507) and Age ( = -.389) had significant partial effects.

Four Bivariate Data Sets GET

FILE='C:\Users\wuenschk\Documents\_Stats\SPSS\Corr_Regr.sav'.

DATASET NAME DataSet1 WINDOW=FRONT.

SORT CASES BY set.

SPLIT FILE LAYERED BY set.

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT y

/METHOD=ENTER x.

Model Summary

set Model R R Square Adjusted R

Square

Std. Error of

the Estimate

A 1 .816a .667 .629 1.23660

B 1 .816a .666 .629 1.23721

C 1 .816a .666 .629 1.23631

D 1 .817a .667 .630 1.23506

ANOVAa

set Model Sum of Squares df Mean Square F Sig.

A 1

Regression 27.510 1 27.510 17.990 .002b

Residual 13.763 9 1.529

Total 41.273 10

B 1

Regression 27.500 1 27.500 17.966 .002b

Residual 13.776 9 1.531

Total 41.276 10

C 1

Regression 27.470 1 27.470 17.972 .002b

Residual 13.756 9 1.528

Total 41.226 10

D 1

Regression 27.500 1 27.500 18.028 .002b

Residual 13.728 9 1.525

Total 41.228 10

Coefficientsa

set Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

A 1 (Constant) 3.000 1.125 2.667 .026

x .500 .118 .816 4.241 .002

B 1 (Constant) 3.001 1.125 2.667 .026

x .500 .118 .816 4.239 .002

C 1 (Constant) 3.002 1.124 2.670 .026

x .500 .118 .816 4.239 .002

D 1 (Constant) 3.000 1.123 2.671 .026

x .500 .118 .817 4.246 .002

Does Idealism Moderate the Effect of Misanthropy on Attitude About Animals (An Interaction)?

(Idealism was dichotomized to high versus not high)

SORT CASES BY idealism.

SPLIT FILE SEPARATE BY idealism.

REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT ar

/METHOD=ENTER misanth.

idealism = 0 Nonideal

Descriptive Statisticsa

Mean Std.

Deviation

N

ar 2.33869 .555053 91

misanth 2.376 .6732 91

Correlationsa

ar misanth

Pearson

Correlation

ar 1.000 .364

misanth .364 1.000

Sig. (1-tailed) ar . .000

misanth .000 .

N ar 91 91

misanth 91 91

Model Summarya

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .364b .132 .123 .519889

ANOVAa,b

Model Sum of

Squares

df Mean

Square

F Sig.

1

Regression 3.672 1 3.672 13.586 .000c

Residual 24.055 89 .270

Total 27.728 90

Coefficientsa,b

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 1.626 .201 8.091 .000

misanth .300 .081 .364 3.686 .000

idealism = 1 Idealist

Descriptive Statisticsa

Mean Std.

Deviation

N

ar 2.43887 .503066 63

misanth 2.241 .6712 63

Correlationsa

ar misanth

Pearson

Correlation

ar 1.000 .020

misanth .020 1.000

Sig. (1-tailed) ar . .437

misanth .437 .

N ar 63 63

misanth 63 63

Model Summarya

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .020b .000 -.016 .507067

ANOVAa,b

Model Sum of

Squares

df Mean

Square

F Sig.

1

Regression .007 1 .007 .026 .874c

Residual 15.684 61 .257

Total 15.691 62

Coefficientsa,b

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1 (Constant) 2.405 .224 10.719 .000

misanth .015 .096 .020 .160 .874

a. idealism = 1 Idealist

b. Dependent Variable: ar

Return to the SPSS Correlation/Regression Lesson

Karl L. Wuensch, June, 2015.