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1

What are the approaches to multiple regression? Describe them

-Standard: all predictors are entered at the same time (produces one model)

-Sequential: predictors are entered in steps (produces multiple models) - can be either data driven (frowned upon) or model drive

2

Why is data drive/stepwise approaches frowned upon?

because they are specific to your dataset

3

What is multicollinearity?

When a combination of one or more of the predictor variables are highly correlated with another set of predictor variables within the same multiple regression model

4

What is perfect collinearity?

+1 or -1 (perfect negative or positive correlation) 0 = no relationship

5

What indicates multicollinearity is probably an issue in standard regression?

excessively large standard errors

6

What indicates multicollinearity is probably an issue in hierarchical regression?

large changes in regression coefficients and/or associated error terms as predictor variables are added or removed from the analysis

7

What is mediation?

when the relationship between a predictor variable and an outcome variable can be explained by their relationship to a third variable (the mediator)

8

What is moderation?

combined effect of two variables on an outcome -> known as a statistical interaction

9

What are the 4 conditions of mediation?

1. the predictor variable must significantly predict the outcome variable in model 1;
2. the predictor variable must significantly predict the mediator in model 2;
3. the mediator must significantly predict the outcome variable in model 3; and
4. the predictor variable must predict the outcome variable less strongly in model 3 than in model 1.

Where model 1 refers to the simple/direct relationship and model 2 refers to the mediation model

10

What does R squared represent

the amount of variance explained by a model

11

What does the beta value represent in multiple regression?

the total effect of a predictor variable given the presence of other predictor variables

12

What is a disturbance term in AMOS and why is it needed?

A disturbance term represents the variability in a predicted variable that is not accounted for by the predictor variable

13

In path analysis, which variables require disturbance terms?

every predicted variable needs a disturbance term

14

What is confirmatory factor analysis

verifying the ability of a theoretical model to explain the common variance among several variables using previously identified latent variables

15

in SEM what are exogenous variables?

Variables that are not influenced by any other variables in a model

16

in SEM what are endogenous variables?

variables that are influenced by other variables in a model

17

What is a latent variable

A variable that is not directly measured

18

What are the steps in structural equation modelling? (6)

1. Construction of a theoretical model
2. Formalization of the model
3. Estimation of the model
4. Identification of the model
5. Interpretation of the model -> 6. can lead to modification of the theoretical model

19

What is communality in Exploratory Factor Analysis

the proportion of common variance that is shared with other variables

20

In structural equation modeling what is the measurement model?

The model that relates measured variables to the latent variables

21

In structural equation modeling what is the structural model?

relates latent variables to one another

22

which indices does the prof consider mandatory to report in CFA?

Chi squared, CFI, RMSEA