dependent (criterion) variable – academic success: academic major grade point average (major_gpa)
DESCRIPTION
EPS 625 – Intermediate Statistics Block Entry (Hierarchical) Multiple Linear Regression Example II – Academic Success. Dependent (Criterion) Variable – Academic Success: Academic Major Grade Point Average (Major_GPA). Independent (Predictor) Variables: - PowerPoint PPT PresentationTRANSCRIPT
Dependent (Criterion) Variable – Academic Success:Academic Major Grade Point Average (Major_GPA)
Independent (Predictor) Variables:Socio Economic Status when entering college (SES)
Age when entering college (Age)High School Grade Point Average (HS_GPA)
ACT composite score (ACT_Comp)
Model 1 (Block 1, Control) Variables – Personal:Socio Economic Status when entering college (SES)
Age when entering college (Age)
Model 2 Change (Block 2) Variables – Ability/Aptitude:High School Grade Point Average (HS_GPA)
ACT composite score (ACT_Comp)
EPS 625 – Intermediate StatisticsBlock Entry (Hierarchical) Multiple Linear Regression
Example II – Academic Success
Model Summary
.151a .023 .019 .47537 .023 5.835 2 497 .003
.820b .672 .669 .27606 .649 489.337 2 495 .000
Model1
2
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), Age, SESa.
Predictors: (Constant), Age, SES, HS_GPA, ACT_Compb.
ANOVAc
2.637 2 1.319 5.835 .003a
112.309 497 .226
114.946 499
77.222 4 19.305 253.319 .000b
37.724 495 .076
114.946 499
Regression
Residual
Total
Regression
Residual
Total
Model1
2
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), Age, SESa.
Predictors: (Constant), Age, SES, HS_GPA, ACT_Compb.
Dependent Variable: Major_GPAc.
Total (Model 2 – Full) R2 = .672 and is significantF(4, 495) = 253.319, p < .001
Model Summary
.151a .023 .019 .47537 .023 5.835 2 497 .003
.820b .672 .669 .27606 .649 489.337 2 495 .000
Model1
2
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), Age, SESa.
Predictors: (Constant), Age, SES, HS_GPA, ACT_Compb.
ANOVAc
2.637 2 1.319 5.835 .003a
112.309 497 .226
114.946 499
77.222 4 19.305 253.319 .000b
37.724 495 .076
114.946 499
Regression
Residual
Total
Regression
Residual
Total
Model1
2
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), Age, SESa.
Predictors: (Constant), Age, SES, HS_GPA, ACT_Compb.
Dependent Variable: Major_GPAc.
Control Variables (Model 1) R2 = .023 and is significantF(2, 497) = 5.835, p < .01
Model Summary
.151a .023 .019 .47537 .023 5.835 2 497 .003
.820b .672 .669 .27606 .649 489.337 2 495 .000
Model1
2
R R SquareAdjustedR Square
Std. Error ofthe Estimate
R SquareChange F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), Age, SESa.
Predictors: (Constant), Age, SES, HS_GPA, ACT_Compb.
ANOVAc
2.637 2 1.319 5.835 .003a
112.309 497 .226
114.946 499
77.222 4 19.305 253.319 .000b
37.724 495 .076
114.946 499
Regression
Residual
Total
Regression
Residual
Total
Model1
2
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), Age, SESa.
Predictors: (Constant), Age, SES, HS_GPA, ACT_Compb.
Dependent Variable: Major_GPAc.
Block 2 (Model 2, Change) R2 Change = .649 and is significantF Change(2, 495) = 489.337, p < .001