Flashcards in 5. Regression mediational and moderation Deck (46):

1

## What is the Baron and Kenny theory for?

### Mediation

2

## What do you write (in a path diagram) on the regression pathway?

### the b values

3

## What are the 4 steps to the Baron and Kenny model?

###
X on Y is sig. (SLR1)

X on M is sig. (SLR1)

M on Y is sig. (SMLR1)

But X on Y now not sig. (MLR1)

= full mediation

4

## How do you test for significance of mediation after running the Baron and Kenny analysis?

### Use a Sobel Test

5

## What can you use to find the effect size of the mediation effect?

### Haze Process Macros

6

## What is a direct effect?

###
When X effects Y (SLR)

7

## What is an indirect effect?

### When X effects Y through M (MLR)

8

## What is the equation for the mediation line?

###
M = intercept + a*X + e

(a being the b1 value for X)

9

## What is the equation of the regression line where mediation is present?

###
Y = intercept + c'*X + b*M + e

(where c' is the b1 value for X on Y through M in a MLR and b is M on Y)

10

## Which b values do you use in a regression line equation?

### The unstandardised b values

11

## What are the 3 types of mediation?

###
Full

Partial

None

12

## What does a dotted line in a path diagram mean?

### Non sig. effect

13

## When does partial mediation occur?

### When the size of the p (sig. value) decreases but still remains sig. (so not full mediation)

14

## When does no mediation occur?

### When the path a or b are not sig. or close to 0

15

## What is an exogenous variable?

### Unrelated variables (e.g. no sig. effect... X predict Y and M predicts Y but X does not predict Y through M... e.g. no mediation)

16

## When you have exogenous variables you have....?

### No mediation

17

## What is a spurious relationship?

###
Pearson r = sig.

But unstand. b value = 0

(no causal relationship)

18

## What type of multiple regression analysis do you do in the third step of the barron and kenny process?

### Simultaneous

19

## What does the Adjusted R squared signify?

### The percentage that accounts for the variance in the DV

20

## What happens if the F value is sig. in an ANOVA?

### The regression equation using the IV is sig. better than using the mean of the IV to predict the DV

21

## What do F values compare in an ANOVA?

### the mean and the model's ability to predict the DV

22

## What do you find in the coefficients table?

### The unstandardised b values

23

## What do the unstandardised b values tell us?

### That when the IV increases by 1 point, the DV increased/ decreases by the b value*IV score

24

## The b values can be either?

###
Sig. or non sig increase/ decrease

Given by the t-values

25

## What are Cohen's conventions for r?

###
.1

.3

.5

26

## What are Cohen's conventions for r sq?

###
1%

9%

25%

27

## The adjusted R sq values (percentage of variance) can be either... which means?

###
Sig. or non sig.

Given by F values

(which means the model is either sig. or non sig. better than using the mean)

28

## When put in a hierarchical multiple regression what can we see?

###
How each variable contributes to the variance and whether that contribution has a sig. effect

(whilst controlling for the other variables)

29

## What is the formula for the sobel test?

### a path * b path / SE a*b paths

30

## What does the Haze Process Macro calculate?

###
The effect size (using the Confidence intervals using bootsrap)

31

## Why is it good to use Haze process macros?

### It gives more power in smaller samples

32

## What is reported from Haze Macros output?

### The Preacher and Kelly Kappa (K)

33

## What is the maximum possible value of effect size (Kappa)?

### 1

34

## How is Kappa intrepreted?

### Cohen's conventions or r sq

35

## When is the indirect effect not genuine?

### When confidence intervals of Kappa are -x and +x (include 0 point)

36

## When does an interaction effect occur?

### When X has an effect on Y when Z is present (better than adding both X and Z to get Y)

37

## What is the interaction term also referred to?

###
The product term

or

The centred product term

38

## Why do we need to centre the product term?

###
So that the scales are balanced

to standardise metrics (both carry same impact)

39

## How do we centre a IV?

###
Standardise by:

IV - Mean of IV

40

## What type of multiple linear regression do you use when using an interaction term?

### Hierarchical (Blocking)

41

## What term do you enter first into a hierarchical MLR when using an interaction term?

###
The normal IVS

(then in block to the Interaction Term)

IV1 and IV2

Then IV1*IV2

42

## What can you conclude if after doing a MLR using an Interaction effect, the interaction term is non sig.?

### Then the additive (main) effect is best (Normal linear regression will do e.g. DV = b0 +bIV1 + bIV2 + e)

43

## What can you conclude if after doing a MLR using an Interaction effect, the interaction term is sig.?

### Then predictors are synergistic (moderating each other)

44

## What does synergistic mean?

### Moderating (working together)

45

## What are the df used when reporting F

###
Regression df

Residual df (N - k)

46