STATS 6 CHOOSING PREDICTORS IN MR Flashcards

1
Q

What is “collinearity”?

A

Two or more predictors are highly correlated and explain essentially the same variance in the outcome

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2
Q

Why is collinearity a problem?

A

(at least one predictor is redundant) Betas will be unstable, and their standard errors large, making it difficult to interpret how much different predictors impact the regression equation

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3
Q

What does the “tolerance” index represent?

A

Tolerance measures lack of collinearity

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4
Q

What is the simplest method to measure collinearity?

A

R-squared (describes the proportion of variance in X1 explained by other predictors)

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5
Q

How can R-squared be adapted to measure tolerance?

A

1 - R-squared = the tolerance value

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6
Q

Which tolerance values represent potential and serious problems?

A

<0.2 indicates potential problem

<0.1 indicates serious problem

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7
Q

Will SPSS take care of collinearity for you?

A

Hell no! SPSS only warns you if tolerance < 0.001.

You must request tolerance specifically!

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8
Q

What are possible solutions to collinearity problem??

A

Remove some predictors or combine highly correlated predictors together

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9
Q

What is a “suppressor” variable?

A
  • improves overall prediction even though it is unrelated to outcome variable
  • supresses irrelevant variance in other predictors and enhances their role in the regression model
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10
Q

What is the plane lesson example of suppressor variable?

A

If they had high spatial, numerical and mechanical scores… A low verbal score would mean higher ability to fly

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11
Q

How to spot the suppressor variable in regression?

A

Insignificant zero-order correlation, significant beta correlation

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12
Q

What is hierarchical regression?

A

Block entry

Creating a hierarchy of predictors

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13
Q

How do you compare two blocks in hierarchical regression?

A

Assess R-square change

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14
Q

What would we consider to be a good set of predictors in MR?

A

Important, theoretically relevant and based on good theory.

low correlations with each other and high correlations with the outcome

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