Random Slope model Flashcards

1
Q

How does the random slope differ from the random intercept model?

Equation

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

How does the random slope differ from the random intercept model?

visually

A

we allow the slopes to vary:

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Random Slope model in Stata

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

How to check/test whether RS is better than RI model?

A

Log likelihood ratio test for whether the RS model fits better
-> quite common statistical testing to see whether a random slope pays off

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Variance components of RI vs RS model

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

How to calculate slope variance?

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What does Intercept-slope covariance tell us?

A

If the covariance is positive, then a higher intercept value is associated with a higher slope.

If negative, a higher intercept value is associated with a lower slope.

If near-zero, there is minimal/no relationship between the intercept and slope values.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Why do random slopes vary less than fixed slopes?

A

Random slopes are subject to shrinkage, which is a statistical technique that pulls estimates towards a common value, typically the overall mean. Shrinkage helps to stabilize the estimates and reduces the influence of extreme or noisy observations. As a result, the estimated random slopes tend to be less variable compared to fixed slopes, which do not undergo shrinkage.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Practical advice: Random slopes

A
  • Do not include random slopes without random intercepts (possible but doesn’t make a lot sense)
  • Many random slopes make the model unstable (limit number of random slopes)
  • Include random slopes only if strongly suggested by theory (Rabe-Hesketh & Skrondal 2012)
  • For correct statistical inference, include a random slope for all lower-level variables involved in cross-level interactions (Heisig & Schaeffer 2019) → not intuitive
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Why does centering matter?

A

In random intercept models, Level-2 variance is constant. In random slope models, Level-2 variance is a function of the random slope.
You can center at different levels but depending on where you center your intercept, the variance will vary - you have to be aware

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Level-2 variance function of RS models

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q
A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Cross-level interactions
Typical setup

A
  • Theory suggests slope variance
  • Model finds slope variance
  • We have measures of L2 factors explaining slope variance
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

a) What is the estimated slope of ESCS for private schools?
b) How much of ESCS slope variance is associated with school type?

A

a) 26 (main + interaction effect)
b) Bild

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Which effect/estimator is b1?

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

What is the mixed meaning of RE?

A
17
Q

Level-1 & Level-2 hypothesis of ESCS on math score?

A
18
Q

Level-1 & Level-2 mechanism of ESCS on math score?

A
19
Q

Separating within and between theoretically:
ESCS on mscore

A
20
Q

Separating within and between statistically:
ESCS on mscore

A
21
Q

Interpret the RE, FE & BE estimator

A
22
Q

Interpret the cross-level interaction

A