Running & Reporting a Multiple Linear Regression in SPSS - Example

The Study: Are the number of cyberbullying incidents a student experiences and there absenteeism significant predictors of self-esteem scores?

Decision Path: Inferential / Relationship / Predictive / Three Variables = Multiple-Linear Regression

The Hypothesis: GPA and years in academic clubs are significant predictors of ACT scores.

The Null-hypothesis: GPA and years in academic clubs are NOT significant predictors of ACT scores.

Question: Do we have enough evidence to reject the null hypothesis?

The Decision rule: If the probability that we are wrong is .05 or 5 out of 100 times we will reject the null-hypothesis or in other words, accept the hypothesis.

Results

ANOVAa

Model

Sum of

Squares df

Mean

Square F Sig.

1 Regression 25.272 2 12.636 58.669 .000b

Residual 38.122 177 .215

Total 63.394 179

a. Dependent Variable: Absenteeism

b. Predictors: (Constant), Self_Esteem_Index, Cyberbullying_incidents

Report: Based on the results of the study, the number of cyberbullying incidents experienced by students and their self-esteem index scores are statistically significant predictors of absenteeism F(2) = 58.669 p = .000.

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) -.712 .267 -2.673 .008

Cyberbullying_incidents .167 .030 .398 5.550 .000

Self_Esteem_Index .024 .006 .311 4.341 .000

a. Dependent Variable: Absenteeism

The undstandardized coefficients in the regression equation were each statistically significant: constant p = .008, regression coefficient for cyberbullying p = .000, and regression coefficient for self-esteem index scores p = .000. Therefore, student’s predicted absenteeism are equal to -.712 + .167 (incidents of cyberbullying) + .024 (self-esteem).

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .631a .399 .392 .464

a. Predictors: (Constant), Self_Esteem_Index, Cyberbullying_incidents

39.2% of the variance in absenteeism are explained by the variance in cyberbullying incidents and self-esteem scores.

Therefore, here are the final results:

Based on the results of the study, the number of cyberbullying incidents experienced by students and their self-esteem index scores are statistically significant predictors of absenteeism F(2) = 58.669 p = .000.

The undstandardized coefficients in the regression equation were each statistically significant: constant p = .008, regression coefficient for cyberbullying p = .000, and regression coefficient for self-esteem index scores p = .000. Therefore, student’s predicted absenteeism are equal to -.712 + .167 (incidents of cyberbullying) + .024 (self-esteem).

39.2% of the variance in absenteeism are explained by the variance in cyberbullying incidents and self-esteem scores.