Module 3

Question 1

What type of IVs and DVs can we have in logistic regression?

Answer 1

IVs can be categorical or continuous

DVs must be categorical

Question 2

Can logistic regression imply any causation?

Answer 2

No

Question 3

How do we calculate the odds ratio?

Answer 3

Odds of event occurring in one group divided by the odds of the event occurring in the other group

Question 4

What is complete separation. What example does Field (2013) give?

Answer 4

When our predictor variables make a perfect prediction of the outcome eg: cats and burglars. It is problematic because it gives no in-between data in order to make probabilities, which is the whole point of logistic regression

Question 5

What is overdispersion?

Answer 5

Overdispersion is when observed variance is higher than expected. It is caused by a violation of the assumption of independence and causes standard errors to become too small

Question 6

What do we use the log-likelihood statistic for?

Answer 6

To determine how well our model fits the data

Question 7

How do we calculate the deviance statistic?

Answer 7

-2 x log likelihood

Question 8

What is parsimony? How do we achieve it in logistic regression?

Answer 8

Parsimony is when we prefer simpler explanations of phenomena over complicated ones. We achieve parsimony by including all variables in the model then progressively removing the ones that aren’t relevant/causing an effect.

Question 9

Which assumption is broken by logistic regression? How do we overcome it?

Answer 9

The assumption of linearity is broken by logistic regression. We overcome it by using the log (or ‘logit’) of the data. Thus, the assumption of linearity in logistic regression assumes that there is a linear relationship between any continuous predictors and the logit of the outcome variable.

Question 10

The process of cleaning data and checking for assumptions is the same in logistic regression, expect for an additional 5 steps. What are they?

Answer 10

6. Evaluate model parsimony
7. Check the linearity of the log outcome variable (logit)
8. Assess the data using deviance statistics
9. Check for complete separation
10. Check for overdispersion

Question 11

What type of regression do we do if we want to check the model for parsimony? Do we check for interactions?

Answer 11

Hierarchical - put variable 1 and 2 in the first block, and add 3 in the second block. Only check for interactions if we have the theory to back it up.

Question 12

What does the Wald statistic test? How do we calculate it?

Answer 12

The Wald statistic is similar to the t-test. It tests the null hypothesis that b = 0. It is biased when b is large, because this makes this SE become inflated which makes the Wald underestimated.
Wald = b/SEb

Question 13

In terms of the relationship between a predictor and outcome variable, what happens when the odds ratio is less than 1 vs. when it is greater than 1?

Answer 13

When odds ratio > 1: as predictor increases, odds of outcome variable increases
When odds ratio < 1: as predictor increase, odds of outcome variable decreases

Question 14

Explain graphically why we can’t just use multiple linear regression.

Answer 14

Multiple linear regression creates a straight line of best fit when displayed graphically. However, categorical data doesn’t suit a line of best fit. Logistic regression provides a sigmoidal curve, which is much better.

Question 15

What is the basic example of a research question suited to logistic regression?

Answer 15

“Can one level of the outcome variable (coded 0) be discriminated from the other level (coded 1) using a set of predictors?”

Question 16

What other questions does logistic regression answer?

Answer 16

Which variables predict which outcome
How variables affect the outcome
Does a predictor variable increase or decrease the probability of an outcome, or does it have no effect in discriminating between the binary outcome?

Question 17

What does censored data mean in logistic regression?

Answer 17

Censored data isn’t data that has been ignored or removed from the set. Rather it refers to when data above a cutoff is classed as a success and when data below a cutoff is classed as failure, for example.

Question 18

What is the first thing we should do before the main analysis in logistic regression?

Answer 18

Check for correlation between predictors

Question 19

What does block 0 refer to?

Answer 19

Block 0 refers to the model without any predictors in it

Question 20

What does the table ‘Variables not in the Equation’ refer to?

Answer 20

This table shows us the predictive ability of each variable individually, not together in the model

Question 21

What does the Hosmer-Lemmeshow test tell us? What do we want its result to be?

Answer 21

This test refers to the goodness-of-fit of the model. We want it to be GREATER than .05.

Question 22

What does the Classification table tell us? What is a good result?

Answer 22

This table shows us what percentage of outcomes were correctly predicted. Anything about 65% is good.

Question 23

What are we looking for in the ‘Omnibus tests of model coefficients’ table?

Answer 23

This table compares the different models. If it is significant, then there is a significant difference between models (i.e. one is a better predictor than the other)

Question 24

What is another way to compare two models? What do we subtract from what?

Answer 24

Subtract the -2LL of the second model from the -2LL of the first model.

Question 25

What is the preferred R-square?

Answer 25

Nagelkerke is preferred over Cox-Snell

Question 26

What two values can R-square be between? If R-square is less than 1, what does this mean for the relationship between the predictor and odds of the outcome? What about if R-square is greater than 1?

Answer 26

R square can lie between 1 and -1
If R square is less than 1, then as the predictor increases the odds of outcome decreases
If R square is greater than 1, then as the predictor increases, the odds of outcome increases.

Question 27

What should the confidence interval for Exp(B) not cross?

Answer 27

1

Question 28

What does a significant chi-square mean?

Answer 28

The predictors in the model are significant predictors

Question 29

What do we do after we’ve checked multiple models and found out just one of them is significant?

Answer 29

Run the analysis again with just this model, to maintain parsimony

Question 30

How do we want our clarification plot to look?

Answer 30

We don’t want points clustered around the middle, we want them located at the periphery.

Question 31

When looking at residuals and influential cases, what do we want Cook’s distance to be?

Answer 31

< 1

Question 32

What do we want our leverage values to be?

Answer 32

Close to .018

Question 33

What do we want 95% of our normalized residuals to be between?

Answer 33

Between 1.96 and -1.96

Question 34

What do we want our DFBETA for the constant to be?

Answer 34

< 1

Question 35

In what situation to we have to check the linearity of the logit? How do we know if this assumption has been violated?

Answer 35

Only check for linearity of logit when we have continuous predictors. It has been violated if there is a significant interaction

Question 36

In what situation do we have to check for multicollinearity? How do we know if this assumption has been violated?

Answer 36

Only check for multicollinearity of we have more than one predictor. It has been violated if tolerance is < .1 or VIF is > 10