Annotated Output from the Correlation/Regression SPSS...
Transcript of Annotated Output from the Correlation/Regression SPSS...
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.