Multiple Regression

Question 1

What is collinearity?

Answer 1

Collinearity is when two or more predictors are highly similar. They CORRELATE highly with each other, meaning they may produce the same or very similar results

Question 2

What is covariance?

Answer 2

Covariance is when the change in one variable is associated with the change in another one. For example, if a variable changes that has an effect on the other variable

Question 3

Why is multiple regression based on correlations data?

Answer 3

Because there is no direct manipulation of any predictor variables

Question 4

What data does multiple regression use?

Answer 4

Scale data: either ratio, interval or ordinal data

Question 5

Why do we base building a multiple regression model off previous research?

Answer 5

We need a reason for including the predictors we are choosing; do we think they will have a direct influence on our criterion variable?

Question 6

What are assumptions of multiple regression? (Hint: there are 4 and they revolve around the data)

Answer 6

1) no outliers
2) normality of data
3) linearity of data
4 reliability of data

Question 7

How is normality of data assessed in MR?

Answer 7

By looking at the skew and kurtosis. Normal distribution is between -2 and +2.

Question 8

What does MR do it there is not a linear relationship between the criterion variable and the predictors?

Answer 8

It will underestimate the relationship. This increases the type 2 error.

Question 9

Why does having lots of predictor variables potentially violate an assumption of MR?

Answer 9

Having lots of variables can make the relationship between criterion and predictors non-linear

Question 10

How do we measure reliability in MR?

Answer 10

By using Cronback Alpha

Question 11

What is Cronbacks Alpha? What does it measure?

Answer 11

It is a measure of internal consistency: it measures how closely related a group of items are in a set. It measures reliability.

Question 12

What is homoscedacity? What is it called if you do not have it?

Answer 12

Where the variance is the same for all the predictor variables.

Hetroscedacity is what occurs if you do not have homodecsity.

Question 13

Why is hetrosecdasity bad for MR results? How do we test for hetrodecsity?

Answer 13

It is bad as it can lead to a distortion in our results, which leads to a type-1 error.

We check for it by looking at residuals

Question 14

What will the correlation be it multicolinesrity is at a moderate level?

Answer 14

Between 0.3 and 0.8

Question 15

How many variables (predictors) do you need to be able to do a multiple regression analysis?

Answer 15

Two or more independent variables

Question 16

How can you check for linearity between criterion and predictor variables in SPSS?

Answer 16

You can check this by looking at scatter plots.

Question 17

What table in SPSS output helps us see how well a regression model fits the data?

Answer 17

The “model summary” table

Question 18

How do residuals help us determine linearity in multiple regression?

Answer 18

They help us see if the standardised residuals are in relation to the models predictions. We can see if they are randomly spread or not

Question 19

How do you check for homoscedasiticity in multiple regression?

Answer 19

By looking at residuals, is it the same?

Question 20

When you have ran your MR and you are looking at the SPSS output tables, where do you look to assess for multicolinesrity (what table)? What value suggests multicolinesrity?

Answer 20

You look at the correlations table. Correlations greater than 0.8 suggest collinearity

Question 21

What is tolerance in MR?

Answer 21

How much the variance in the predictor is not explained by other predictors in the model; the unique contribution of the predictor is seen here

Question 22

Why do we want a high tolerance value?

Answer 22

A high value suggests minimal multicolinesrity

Question 23

What is VIF (variance inflation factor) in MR?

Answer 23

How much variance in the regression coefficient is caused by multicolinesrity.

Question 24

How can VIF be interpretated to overcome issues of multicooinesrity?

Answer 24

It can be a number by what to increase or sample size by to overcome issues of multicolinesrity.

A VIF of 2.5 would be 2.5x participants needed

Question 25

How can you check for outliers?

Answer 25

By looking at the line of best fit

Question 26

What value indictates a high level of consistency wth Cronbachs Alpha?

Answer 26

0.8

Question 27

How do you find the critical value?

Answer 27

50+8x( x is the number of participants)

Question 28

When might you use r2 to look at how much the variability in the criterion variable is explained by our model?

Answer 28

If sample size is greater or equal to critical value

Question 29

When might you use r2 adjusted value?

Answer 29

When looking to see how much the variability in the criterion variable is explained by our model you see if sample size is less than to critical value

Question 30

What is a beta value in multiple regression,

Answer 30

How strongly each predictor variables effects and influences the criterion variable

Question 31

What is a beta value measured in? (What unit)

Answer 31

It is measured in standard deviations

Question 32

What happens to the criterion variable if the beta coefficient for a predictor is 1 and it is statistically significant?

Answer 32

For every 1 that the predictor variable increases, while the other predictors are held constant, the criterion variable increases by one