10: MORE REGRESSION

Question 1

zero order

Answer 1

zero-order^2: variance explained by x, expressed as a proportion of the total variance in y

Question 2

part

Answer 2

part^2: unique variance explained by x, expressed as a proportion of the total variance in y

Question 3

partial

Answer 3

partial^2: unique variance explained by x, expressed as a proportion of the variance in y that remains after the other predictors have been removed

Question 4

hierarchical regression

Answer 4

‘standard’ multiple regression: all predictor variables are entered at the same time
- obtain a measure of the overall variance explained (R^2)
- obtain measures of the influence of each separate predictor (coefficients)

predictor variables are entered in a specified order of ‘steps’, based on theoretical grounds
- the relative contribution of each ‘step’ (set of predictor variables) can be evaluated in terms of what it adds to the prediction of the outcome variable (i.e. the additional variance it explains)

Question 5

why use hierarchical regression?

Answer 5

to examine the influence of predictor variable(s) on an outcome variable after ‘controlling for’ the influence of other variables