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> APA: LINEAR REGRESSION
>A bivariate regression was conducted to examine how well Burnout could predict job satisfaction. A scatterplot showed that the relationship between burnout and job satisfaction was negative and linear and did not reveal any bivariate outliers. The correlation between burnout and job satisfaction was statistically significant, r(000)=.00, p<.000. The regression equation for predicting job satisfaction from burnout was y ̂ = 78.386
>-.567(x). The r^2 for this equation was .164; that is, 16.4% of the variance in job station was predictable from the level of burnout. This is a moderately strong relationship (Cohen, 1998). The bootstrapped 95% confidence interval* for the slope to predict job satisfaction from burnout ranges from -0.00 to -0.00; thus, for each unit of burnout increase, job satisfaction decreases by about 1.8 to 2.5 points.
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GGraph
Notes
Output Created | 18-JUL-2024 22:11:43 | |
Input | Data | /Users/carlosvilladiego/ Downloads/job_satisfacti on.sav |
Active Dataset | DataSet1 | |
Filter | <none> | |
Weight | <none> | |
Split File | <none> | |
N of Rows in Working Data File | 218 | |
Syntax | GGRAPH /GRAPHDATASET NAME="graphdataset" VARIABLES=Burnout Satisfaction MISSING=LISTWISE REPORTMISSING=NO /GRAPHSPEC SOURCE=INLINE /FITLINE TOTAL=NO SUBGROUP=NO. BEGIN GPL GUIDE: axis(dim(1), label("Burnout")) GUIDE: axis(dim(2), label("Satisfaction")) GUIDE: text.title(label ("Scatter Plot of Satisfaction by Burnout")) ELEMENT: point (position (Burnout*Satisfaction)) END GPL. | |
Resources | Processor Time | 00:00:01.02 |
Elapsed Time | 00:00:01.00 | |
[DataSet1] /Users/carlosvilladiego/Downloads/job_satisfaction.sav
Scatter Plot of Satisfaction by Burnout
y=78.39-0.57*x
R2 Linear = 0.164
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Satisfaction
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Burnout
Regression
Notes
Output Created | 18-JUL-2024 22:23:01 | |
Comments | ||
Input | Data | /Users/carlosvilladiego/ Downloads/job_satisfacti on.sav |
DataSet1 | ||
Filter | <none> | |
Weight | <none> | |
Split File | <none> | |
N of Rows in Working Data File | 218 | |
Missing Value Handling | Definition of Missing | User-defined missing values are treated as missing. |
Cases Used | Statistics are based on cases with no missing values for any variable used. | |
Notes
Syntax | REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) R ANOVA /CRITERIA=PIN(.05) POUT(.10) TOLERANCE(. 0001) /NOORIGIN /DEPENDENT Satisfaction /METHOD=ENTER Burnout /SCATTERPLOT= (*ZRESID ,*ZPRED) /RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID) /CASEWISE PLOT (ZRESID) OUTLIERS(3). | |
Resources | Processor Time | 00:00:00.62 |
Elapsed Time | 00:00:01.00 | |
Memory Required | 3328 bytes | |
Additional Memory Required for Residual Plots | 896 bytes | |
Descriptive Statistics
Mean | Std. Deviation | N | |
Satisfaction | 66.16 | 8.825 | 218 |
Burnout | 21.57 | 6.305 | 218 |
Correlations
Satisfaction | Burnout | ||
Pearson Correlation | Satisfaction | 1.000 | -.405 |
Burnout | -.405 | 1.000 | |
Sig. (1-tailed) | Satisfaction | . | <.001 |
Burnout | .000 | . | |
N | Satisfaction | 218 | 218 |
Burnout | 218 | 218 | |
Variables Entered/Removeda
Variables Model Entered | Variables Removed | Method | |
1 | Burnoutb | . | Enter |
Dependent Variable: Satisfaction
All requested variables entered.
Model Summaryb
Model R | R Square | Adjusted R Square | Std. Error of the Estimate | Durbin-Watson | |
1 | .405a | .164 | .160 | 8.088 | 2.009 |
Predictors: (Constant), Burnout
Dependent Variable: Satisfaction
ANOVAa
Model Sum of Squares | df | Mean Square | F | Sig. | ||
1 | Regression | 2771.029 | 1 | 2771.029 | 42.359 | <.00 1b |
Residual | 14130.351 | 216 | 65.418 | |||
Total | 16901.381 | 217 | ||||
Dependent Variable: Satisfaction
Predictors: (Constant), Burnout
Coefficientsa
Unstandardized Coefficients | Standardized Coefficients Beta | t | Sig. | |||
Model B | Std. Error | |||||
1 | (Constant) | 78.386 | 1.957 | 40.062 | <.001 | |
Burnout | -.567 | .087 | -.405 | -6.508 | <.001 | |
-.395
-.738
82.242
74.529
Coefficientsa
Model
1 (Constant) Burnout
95.0% Confidence Interval for B Lower Bound Upper Bound
Dependent Variable: Satisfaction
Casewise Diagnosticsa
Case Number Std. Residual | Satisfaction | Predicted Value | Residual | |
97 | -3.118 | 39 | 64.22 | -25.216 |
217 | 4.128 | 84 | 50.61 | 33.387 |
Dependent Variable: Satisfaction
Residuals Statisticsa
Minimum | Maximum | Mean | Std. Deviation | N | |
Predicted Value | 50.61 | 73.85 | 66.16 | 3.573 | 218 |
Residual | -25.216 | 33.387 | .000 | 8.069 | 218 |
Std. Predicted Value | -4.351 | 2.152 | .000 | 1.000 | 218 |
Std. Residual | -3.118 | 4.128 | .000 | .998 | 218 |
Dependent Variable: Satisfaction
Charts
Histogram Dependent Variable: Satisfaction
Mean = 7.91E-16
60 Std. Dev. = 0.998
N = 218
Frequency
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Regression Standardized Residual
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: Satisfaction
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Expected Cum Prob
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Observed Cum Prob
Scatterplot Dependent Variable: Satisfaction
Regression Standardized Residual
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Regression Standardized Predicted Value
