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Flashcards in Standard & hierarchical multiple regression Deck (12)
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1
Q

How is shared variance dealt with in SMR? (2)

A
  1. It is included in tests of the overall model.

2. Excluded from tests of individual contributions (betas).

2
Q

What is partialled out in partial correlation?

A

Variance from other predictors is removed from BOTH the IV and DV

3
Q

What specific variance is used to test the individual contribution of a predictor/beta?

A

Partial correlation between predictor and DV

4
Q

How is the overall model dealt with in both ANOVA and MR?

A

ANOVA: no test of overall model

MR: tests overall model automatically

5
Q

How are the effects of IVs dealt with ANOVA and MR?

A

ANOVA: main effect of IV is tested, regardless of other variables’ effects/contributions (akin to bivariate correlation)
MR: tests unique effect of IVs, all other IVs’ effects are controlled/partialled out

6
Q

Why, historically, does ANOVA assume IVs are uncorrelated? When does this become a problem?

A

First created for analysing experiments where random assignment meant IVs were not typically correlated. When analysing data without random assignment (e.g. blocking or natural variables)

7
Q

How are interactions dealt with in ANOVA and MR?

A

ANOVA: tests all interactions automatically

MR: need to actively create an interaction term and test it with a hierarchical model

8
Q

What does a multiple correlation coefficient represent?

A

A bivariate correlation between the criterion and the best linear combination (composite) of predictors

9
Q

How do you calculate shared variance?

A

Model R squared minus the sum of unique contributions for all predictors. What remains is overlapping/shared variance in the model.

10
Q

Why can’t we compare betas/IVs between different studies?

A

Betas rely on standard deviations which can differ between samples

11
Q

What happens to shared variance in hierarchical regression?

A

It is attributed to IVs entered earlier in the model and informs the test of those IVs, making them more likely to be significant.

12
Q

Why use a hierarchical regression model over a standard regression?

A

It allows us to give the shared variance to variables which theory tells us have a strong relationship with the DV. Otherwise, in an SMR the shared variance is never used in any tests of IVs’ individual contributions.