Flashcards in MR Deck (22)

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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