Week 1 | Multiple Regression Analysis (MRA)
When? Independent X1, X2 (INT) + Dependent Y (INT)
Y is always regressed on X
Q: Calculate the Pearson correlations between the five variables.
Analyze > Correlate > Bivariate > insert variables >
Q: What is the sample size N?
Under ‘N’
Q: Does it make sense to perform a linear regression of GPA on IQ, age, gender and/or
self-concept?
Check ‘Correlations’ table and check for each variable the correlation and the significance, if it’s
significant we can say it makes sense or think theoretically
Q: Which variable is likely to be a good predictor of GPA?
Check ‘Correlations’ table and check for each variable the correlation and the significance, if it’s
significant it is likely to be a good predictor
Next, perform a linear regression of GPA on IQ, age, gender and self-concept. In Statistics, ask for
part and partial correlations, and collinearity diagnostics. In Save ask for Cook’s distances and
Leverage values.
Q: Can the null hypothesis of no relationship between GPA and IQ, age, gender and/or
self-concept be rejected?
< Analyze > Regression > Linear > in ‘Statistics’ check ‘part and partial correlations’ and ‘collinearity
diagnostics’ > in ‘Save’ ask for ‘Cook’s distances’ and ‘Leverage values’
H0: none of the predictors are good predictors
Ha: at least some of the predictors are good predictors
• Check ANOVA table > report the F value → e.g. F(dfregression, dfresidual) = value → F(4,73) =
23.117, p<0.001
• If F value is significant, we can reject H0, at least some of the predictors are good predictors
Q: How much variance of GPA is explained by IQ, age, gender and SC together?
• Look at ‘Model Summary’ table > look at R squared > report the value
Q: What predictor explains the most unique variance?
• Look at ‘Coefficients’ table > under ‘correlations’ look at the ‘part’ column > the biggest number
should be squared, that variable explains the most unique variance
Q: Is there evidence of multicollinearity in the predictors?
Test whether we have too much of a dependence between our predictors
• Look at ‘Coefficients’ table and under VIF column > VIF should be below 10 > Tolerance needs to
be bigger than 0.1
,Q: Do Cook’s distances and Leverage values suggest the presence of outliers?
• Formula center leverage value: 3(p+1)/N where p = number of predictors > the value calculated is
the largest value we can have in the centered leverage values > look at ‘Residuals Statistics’ under
‘maximum’ column at ‘centered leverage value’ > determine whether the calculate value is higher
than the maximum value in the table > if value calculated is bigger than given value, it suggests
outliers
• Cook’s distance tells us whether an outlier is influential > look at ‘Residuals Statistics’ > check
Cook’s distance value under ‘minimum’ and ‘maximum’ > should not be higher than 1
Q: If one or more outliers are detected, all previous steps are repeated with exclusion of the
outlier(s). Use Selection to get rid of the outlier(s).
• Look at Data View tab > go to new Cook’s Distance variable > select with right mouse and click
‘sort descending’ > look at which participants have a distance above 1 > don’t delete the participant
> go to ‘Data’ and ‘Select cases’ > click ‘if conditions is satisfied’ and insert criteria of the study (e.g.
participants should be below the age of 14)
Q: Finally, remove the non-significant predictors from the model
• Run the analysis of LRA again > remove Cook’s Distance and Leverage under ‘Save’ > Look at
‘Coefficients’ table and look at whether the variables are significant (<0.05) and determine the ones
that are not significant
Q: Perform a linear regression of GPA on the remaining predictors. In Plots, make a scatter plot
of the standardized predicted values versus the standardized residuals, and ask for the normal
probability plot.
• Run LRA again > remove non-significant predictors > in ‘Plots’ add ZPred to X and ZResid to Y and
check normality probability plot
Q: Is there evidence of non-linearity, heteroscedasticity or non-normality of the residuals?
• Look at ‘Scatterplot’
• Linearity = if one creases the other increases, if one decreases the other one decreases > the plot
should look like there is no relationship (look a bunch of random spots) and if the best description is
a horizontal line there is evidence of non-linearity
• Heteroscedasticity → we want homoscedasticity > so we don’t want differences > check from
value 0 on the Y-axis, if there is approx the same amount of dots on both sides, we don’t violate the
assumption of heteroscedasticity
• Normality → look at ‘Normal P-P Plot of Regression’ > the more the dots are on the line the better
the normality → ??????
Q: What is the estimated regression equation? Interpret the regression coefficients.
• Look at ‘Coefficients’ table > constant = intercept (b0) > b1 = variables > add constant and all
variables in the equation
• ŷ = b0 + b1(var) → use unstandardized
Q: How much variance of GPA is explained by the predictors?
, • Look at ‘Model Summary’ and R squared
Q: What predictor explains the most unique variance?
• Look at ‘Coefficients’ table > look under ‘correlations’ and ‘part’ > report the biggest number and
square it
Hierarchical Regression Analysis
Q: How much variance of VarX is explained by VarY?
• Check ‘Model Summary’ table > check R squared
Q: add VarZ as a predictor in a second block to the linear model. In Statistics, ask for R squared
change
Analyze > Regression > Linear > add predictor to Independent in Next’ Block > in ‘Statistics’ ask for
‘R squared change’ > check the same options under ‘Statistics’
Q: Does adding VarZ significantly improve the linear model?
• Look at the ‘Model Summary’ table > look at R squared change > if the model contributed, the
value under model 2 should be positive (and thus the R square has become higher with model 2 >
report the significaince with F and df
Q: Is there evidence of non-linearity, heteroscedasticity or non-normality of the residuals?
In this example we don’t violate the assumption of linearity, heteroscedasticity, or normality
Q: What is the estimated regression equation? Interpret the regression coefficients.
• Do the same as a normal regression equation, except we now look at the values for model 2
• If everything else remains the same, improving 1 point of the score of VarY (independent), would
make the predicted score of VarX (dependent) … (value of VarY B unstandardized) higher
• If we hold everything else the same, if a participant scores one point higher on VarZ, the predicted
value of VarX (dependent) would become (value of VarZ B unstandardized) higher
Q: How much variance of VarX is explained by VarY and VarZ together?
• Look at ‘Model Summary’ > look at R square model 2 > that would explain the variance
Q: How much variance is uniquely explained by neuroticism?
