M2 - Logistic Regression

Question 1

What differentiates LR from MR? pick best answer.

1. LR is used for predicting group membership.
2. LR only uses binary independent variables.
3. LR has a dependent variable that is binary.
4. LR output are graphs that have a straight line for the line of best fit.

Answer 1

3. LR has a dependent variable that is binary.

Question 2

Part B - Question 2: The Independent variables in a Logistic Regression should be:

1. Binary.
2. Continuous or binary.
3. Ordinal.
4. Continuous or metric.
5. Metric.

Answer 2

2. Continuous or Binary

Question 3

Part B - Question 3: What are key differences between ANOVA and logistic regression?

1. The DV is binary for LR and not for ANOVA.
2. The DV is binary for both LR and ANOVA.
3. The DV is continuous for ANOVA and not for LR.
4. The IV and DVs are binary for both LR and ANOVA

Answer 3

1. The DV is binary for LR and not for ANOVA.

Question 4

Part B - Question 4: Is it possible to use a continuous variable for use in Logistic regression?

1. Continuous dependent variables can be used in a regression analyses if the scale includes a 1 and zero in the metric scale.
2. No, a dependent variable that is continuous can only be used in a multiple regression analyses.
3. Only if the continuous variable has more than 2 values.
4. A cut-point can be identified on a continuous variable, and this can be used to form a binary variable.

Answer 4

4. A cut-point can be identified on a continuous variable, and this can be used to form a binary variable.

Question 5

Part C - Question 1: What is the general shape of a plotted logistic regression formula?

1. A straight line.
2. A parabola.
3. An S shape.
4. A U shape

Answer 5

3. An S shape.

Question 6

Part C - Question 2: What is the scale of the DV for the logistic regression model?

A parabolic scale.
A decimal scale.
A hexadecimal scale.
A logarithmic scale

Answer 6

A logarithmic scale

Question 7

Part C - Question 3: Is binary logistic regression a linear model?

Yes, it has the function of y=c+mx.
Yes, it has the formula of y=bx+c
Both a and b.
No, it is non-linear function.

Answer 7

No, it is non-linear function.

Question 8

Part D - Question 1: Can the approach used for MR model building be used for LR?

No, it can only use, standard model building.
No, it must use no- linear strategies.
Yes, it can use standard, sequential and statistical model building.
Yes, it must use ordinal strategies

Answer 8

Yes, it can use standard, sequential and statistical model building.

Question 9

Part D - Question 2: How many ordinal outcomes can a multinomial logistic regression predict?

One continuous category.
Three or more categories.
One multivariate variable.
Two categories

Answer 9

Three or more categories.

Question 10

Part D - Question 3: Which type of LR is the 4th year lecture covering?

Multinomial.
Ordinal.
Binary.

Answer 10

Binary

Question 11

Part D - Question 4: Sequential logistic regression is what?

Binary variables are randomly entered in blocks.
Binary and continuous variables are entered in blocks and pre-specified by the researcher.
Binary variables are entered in blocks and pre-specified by the researcher.
Binary and continuous variables are randomly entered in blocks

Answer 11

Binary and continuous (predictor) variables are entered in blocks and pre-specified by the researcher.

Question 12

Part E - Question 1: What does target variable mean?

This is the independent variable with the most number of categories.
This is the outcome category of the dependent variable that is the focus of the research question.
This is the dependent variable with the least number of categories.
This is the variable that can be used as a independent or dependent variable

Answer 12

This is the outcome category of the dependent variable that is the focus of the research question.

Question 13

Part E - Question 2: What is the reference category in logistic regression analyses?

It is the reference category used to interpret the categories of a categorical variable.
It is the reference for logistic regression.
It is the same as the Target category.
The variable that the research question is focussed on

Answer 13

It is the reference category used to interpret the categories of a categorical variable.

Question 14

Part E - Question 3: How does SPSS choose the target category for an outcome variable in a logistic regression?

It chooses the highest numeric value.
It chooses the lowest numeric value.
SPSS does not choose the DV target category, the analyst always needs to select this to run the analysis

Answer 14

It chooses the highest numeric value.

Question 15

Part E - Question 4: Can a categorical DV have more than one category in logistic regression?

Categorical DVs in a logistic regression must be continuous.
Categorical DVs in logistic regression can only have two values.
Categorical DVS in a logistic regression must be ordinal.
Categorical DVs in a logistic regression can have two or more values.

Answer 15

Categorical DVs in logistic regression can only have two values.

Question 16

Part E - Question 5: Can a categorical IV have more than one category?

Categorical IVs in a logistic regression can have two or more values.
Categorical IVS in a logistic regression must be ordinal.
Categorical IVs in a logistic regression must be continuous.
Categorical IV variables in logistic regression can only have two values.

Answer 16

Categorical IVs in a logistic regression can have two or more values.

Question 17

Part E - Question 6: For an IV with more than one category, can any category be the reference category?

Yes, any category can be the reference variable.
No, SPSS will force a reference category, and this cannot be changed.
No, the lowest category should be the reference category.
No, the highest category should be the reference category

Answer 17

Yes, any category can be the reference variable.

Question 18

Part G - Question 1: When checking the linearity assumption in logistic regression we are doing the following:

There is linear relationship between the independent variables.
There is log linear relationship between continuous independent variables and the dependent variable.
There is a log linear relationship between categorical variables.
There is a linear relationship between all independent variables and the dependent variable

Answer 18

There is log linear relationship between continuous independent variables and the dependent variable.

Question 19

Part G - Question 2: What statistic is recommended to interpret model fit and can also be used as pseudo measure of R2?

The Cox and Snell statistic.
The Chi-square classification.
The Wald statistic.
The -2 Log Likelihood

Answer 19

The -2 Log Likelihood

Question 20

Part G - Question 3: An odds ratio > 1 means what?

Outcome is more likely for target level of IV.
No difference between groups.
Outcome is less likely for target level of IV

Answer 20

Outcome is more likely for target level of IV.

Question 21

Part G - Question 4: An odds ratio < 1 means what?

Outcome is more likely for target level of IV.
Outcome is less likely for target level of IV.
No difference between groups

Answer 21

Outcome is less likely for target level of IV.

Question 22

Part G - Question 5: An odds ratio = 1 means what?

No difference between groups.
Outcome is more likely for target level of IV.
Outcome is less likely for target level of IV

Answer 22

No difference between groups.

Question 23

Part G - Question 6: The odds ratio is also referred to as what?

Tetrachoric correlation.
Exp(B).
The standard error.
Wald statistic

Answer 23

Exp(B).

Question 24

Part G - Question 7: Odds ratios should always be interpreted in the context of what?

Sample size.
Number of correct classifications.
Number of false classifications.
Prevalence

Answer 24

Prevalence

Question 25

In what circumstances should logistic regression be used?

Answer 25

- predicting group membership - DV is binary / categorical - continuous variables can be converted to binary using cut offs - Multiple IVs can be categorical or continuous

Question 26

Outline the differences between logistic regression and multiple regression in terms of - types and number of IVs and DVs

Answer 26

LR IVs - multiple continuous or categorical DVs - single binary or categorical (binomial, multinomial or ordinal) MR IVs - multiple continuous or categorical DVs - single continuous

Question 27

Outline the differences between logistic regression and multiple regression in terms of - approach for entering variables

Answer 27

LR Standard (entered altogether) Sequential (entered in blocks) - theory directed Statistical (forward to backward) MR Standard (forced entry) Hierarchical (entered in blocks) - theory directed Stepwise (statistics based, forward or backward) - only user for exploration

Question 28

Outline the differences between logistic regression and multiple regression in terms of - assumptions

Answer 28

LR - Independence of errors - errors should not be correlated (clustered data) - linearity - IV should have linear relationship with log of the DV - distribution normality - distribution should be normal and outliers should be dealt with (transformed or removed) using Standardised residuals Cook Distance - samples size --> 5 cases per possible combination required - singularity and multicollinearity

Question 29

How does the logistic function differ compared to other functions

Answer 29

``` log function = log(Y/1-Y) = b0 + b1x1 + b2x2 + e log function is an exponential function that is shaped liked an S curve +ve coefficients will increase Y -ve coefficients will decrease Y interpret using Odds Ratio ``` linear function = Y = bx + c linear function is a straight line as DV increase by 1 unit IV increases by b units ``` quadratic function = Y = ax2 + bx + c quadratic function is a parabola +ve a is happy face -ve a is sad face ```

Question 30

When is categorical coding useful?

Answer 30

Useful to deal with categorical variables with 2 or more outcomes

Question 31

What is binomial, multinomial and ordinal categorical coding?

Answer 31

Binomial is 2 (Yes/No) Multinomial is for nominal groups eg brown = 1, blue = 2, green = 3 Ordinal is multinomial moving in a progressive way eg education level achieved 1 = high school, 2 = grad school, 3 = postgrad

Question 32

Which group of DV category should be the referent in binomial logistic regression and how should it be coded?

Answer 32

Referent group should be coded lower and should be the group you that is not the target of interest ie control group

Question 33

For IVs with 2 and 3 categories, how should they be coded?

Answer 33

Binomial categorical IV -SPSS will assign automatically - target should be the group of interest - referent should be the group not of interest Multinomial categorical IVs - can be anyway - needs to make sense - set the referent as the variable you are most interested in so other groups can be compared directly to that one

Question 34

What does the choice of referent group in categorical coding impact?

Answer 34

interpretation of the DV | interpretation of the coefficient o the IVs

Question 35

Name the model overall approaches for logistic regression interpretation

Answer 35

Model Improvement - 2LL change - 2LL proportion (% improvement of model fit) Classification Accuracy % correct % improvement relative to baseline

Question 36

Describe model improvement methods for interpreting LR

Answer 36

``` -2LL change = -2LLbase - -2LLnew = -2LL change (Omnibus test) used for nested models only significant at p =.05 if 1 df > 3.84 ``` -2LL proportion = x2 model / -2LLbase then transform to improvement of model fit

Question 37

Describe classification accuracy methods for interpreting LR

Answer 37

Classification accuracy 1. % correct = the # of correctly predicted to be in one group and not in another group 2. % improvement = the # of correctly predicted relative to if everyone was predicted to be in the category with the most outcomes = hits + correct rejection -nmax / sample n - nmax then transform to % for model improvement over baseline

Question 38

Name and explain the individual predictors of LR interpretation

Answer 38

b weight = change in log odds of Y =1 for 1 unit change in the IV Odd Ratio = change in likelihood of Y = 1 for 1 unit change in IV <1 = less likely, > 1 is more likely, 1 = no difference Significance test - Wald's test

Question 39

Where do you find the log odds and odd ratio in SPSS output?

Answer 39

log odds - b weight = B For every unit increase level of group, the log odds of being in the outcome increase by B units Odd ratio - Exp(B) likelihood of increase relative to referent group