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Stats 4 > 5. Regression mediational and moderation > Flashcards

5. Regression mediational and moderation Flashcards

1
Q

What is the Baron and Kenny theory for?

A

Mediation

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2
Q

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

A

the b values

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3
Q

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

A

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

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4
Q

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

A

Use a Sobel Test

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5
Q

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

A

Haze Process Macros

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6
Q

What is a direct effect?

A

When X effects Y (SLR)

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7
Q

What is an indirect effect?

A

When X effects Y through M (MLR)

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8
Q

What is the equation for the mediation line?

A

M = intercept + a*X + e

a being the b1 value for X

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9
Q

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

A

Y = intercept + c’X + bM + e

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

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10
Q

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

A

The unstandardised b values

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11
Q

What are the 3 types of mediation?

A

Full
Partial
None

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12
Q

What does a dotted line in a path diagram mean?

A

Non sig. effect

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13
Q

When does partial mediation occur?

A

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

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14
Q

When does no mediation occur?

A

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

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15
Q

What is an exogenous variable?

A

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)

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16
Q

When you have exogenous variables you have….?

A

No mediation

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17
Q

What is a spurious relationship?

A

Pearson r = sig.
But unstand. b value = 0
(no causal relationship)

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18
Q

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

A

Simultaneous

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19
Q

What does the Adjusted R squared signify?

A

The percentage that accounts for the variance in the DV

20
Q

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

A

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

21
Q

What do F values compare in an ANOVA?

A

the mean and the model’s ability to predict the DV

22
Q

What do you find in the coefficients table?

A

The unstandardised b values

23
Q

What do the unstandardised b values tell us?

A

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

24
Q

The b values can be either?

A

Sig. or non sig increase/ decrease

Given by the t-values

25
Q

What are Cohen’s conventions for r?

A

.1
.3
.5

26
Q

What are Cohen’s conventions for r sq?

A

1%
9%
25%

27
Q

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

A

Sig. or non sig.
Given by F values

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

28
Q

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

A

How each variable contributes to the variance and whether that contribution has a sig. effect
(whilst controlling for the other variables)

29
Q

What is the formula for the sobel test?

A

a path * b path / SE a*b paths

30
Q

What does the Haze Process Macro calculate?

A

The effect size (using the Confidence intervals using bootsrap)

31
Q

Why is it good to use Haze process macros?

A

It gives more power in smaller samples

32
Q

What is reported from Haze Macros output?

A

The Preacher and Kelly Kappa (K)

33
Q

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

A

1

34
Q

How is Kappa intrepreted?

A

Cohen’s conventions or r sq

35
Q

When is the indirect effect not genuine?

A

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

36
Q

When does an interaction effect occur?

A

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

37
Q

What is the interaction term also referred to?

A

The product term
or
The centred product term

38
Q

Why do we need to centre the product term?

A

So that the scales are balanced

to standardise metrics (both carry same impact)

39
Q

How do we centre a IV?

A

Standardise by:

IV - Mean of IV

40
Q

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

A

Hierarchical (Blocking)

41
Q

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

A

The normal IVS

(then in block to the Interaction Term)

IV1 and IV2
Then IV1*IV2

42
Q

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

A

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

43
Q

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

A

Then predictors are synergistic (moderating each other)

44
Q

What does synergistic mean?

A

Moderating (working together)

45
Q

What are the df used when reporting F

A 
Regression df 
Residual df (N - k)
46
Q

What are the df when reporting t?

A

Residual df

N - k

Stats 4 (11 decks)

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