logistic regression

Question 1

Logistic Regression

Answer 1

to investigate the relationship of a number of predictor variables (categorical or continuous) with one categorical outcome (criterion) variable

Question 2

Types of Logistic Regression

Answer 2

Binary Logistic Regression/ multinomial logistic regression

Question 3

Binary Logistic Regression

Answer 3

Used when the categorical criterion variable has two levels

Question 4

Multinomial Logistic Regression

Answer 4

Used when the categorical criterion variable has three or more levels

Question 5

Use of logistic regression

Answer 5

How well your set of predictor variables explain your categorical outcome variable/ How accurate your model is at classifying individuals into groups (in the sample)

Question 6

binary logistic regression

Answer 6

Logistic regression is about assessing the probability of belonging to one group as opposed to another (based on scores for the predictor variables)

Question 7

Goodness of Fit Tests

Answer 7

test compares the performance of your model (your combination of PVs) to a model with no PVs/ p < .05 is good fitting model

Question 8

specificity

Answer 8

what proportion of the sample are correctly
classified as not belonging to the group of interest - True negatives

Question 9

WALD test

Answer 9

WALD test is used to determine if a PV is a significant predictor of belonging to the group of interest.

Question 9

Sensitivity

Answer 9

what proportion of the sample are correctly classified as belonging to the group of interest - True positives

Question 10

Odds ratio

Answer 10

the odds of belonging to the group of interest based on a one unit change in the PV.

Question 11

The directional effect of the PV

Answer 11

assessed through inspection of the Beta value (B)

Question 12

what do statistically significant PVs indicate

Answer 12

indicate the variables that make a person more or less likely to belong to the group of interest.