Multiple Regression

Question 1

Multiple Regression

Answer 1

Used to predict a continuous outcome from several categorical or continuous predictors.

Question 2

Multiple Regression Statistic

Answer 2

R-squared is the key statistic (estimation of the amount of variability that can be accounted for by the predictor/s.

Question 3

Hierarchical Regression

Answer 3

Experimenter decides the order of predictor entry, based on theory. Good for assessing unique influence of unknown predictors on the outcome, however requires a very good knowledge of the research area.

Question 4

Forced Entry Regression

Answer 4

All predictors are entered simultaneously. Theory driven, however the results depend on the variables entered.

Question 5

Stepwise Regression

Answer 5

Predictors are mathematically selected by SPSS to go into the model. Should only be used for exploratory research because it is influenced by the correlations between the variables.

Question 6

Semi-partial Correlation

Answer 6

Measures the relationship between two variables while controlling for the effect of a third variable on only one of the original two variables.

Question 7

Beta Values in Regression

Answer 7

Beta (b) reflects the change in the outcome associated with a unit change in the predictor. Standardised Beta (B) reflects the change in the outcome associated with a SD change in the predictor.

Question 8

Assessing Model Fit of Multiple Regression

Answer 8

Standardized residuals - indicate outliers (95% should fall between -2 and +2).
Cook’s Distance - indicates the influence of a single case on the data (value over 1 is problematic)

Question 9

Assumptions of Multiple Regression

Answer 9

Normality, Homogeneity of variance, linearity, independence, multicollinearity, homoscedacity, independent errors.