Interaction between both indicators
Mean |
Std. dev | 2.5% |
97.5% |
|
---|---|---|---|---|
|
10.20 | 0.03 | 10.13 | 10.27 |
|
0.05 | 0.05 | -0.04 | 0.14 |
|
0.52 | 0.04 | 0.44 | 0.61 |
|
0.13 | 0.06 | 0.02 | 0.25 |
|
0.81 | 0.01 | 0.79 | 0.83 |
Mean | Exp(Mean) | |
---|---|---|
|
10.20 | 26,853 |
|
0.05 | 1.05 |
|
0.52 | 1.69 |
|
0.13 | 1.14 |
The pay boost for being over 35 years old (
OR
The income advantage for men over women (
Standardization:
Transforming a variable
Mean | Exp(Mean) | |
---|---|---|
|
10.58 | 39,549 |
|
0.40 | 1.49 |
|
0.20 | 1.22 |
|
-0.07 | 0.93 |
The extra income associated with increased union and professional dues (
OR
The extra money made by working more hours (
Causal question:
Does a change in one variable (X)
cause a change in another (Y)?
Regression only identifies statistical relationships, not causal relationships
To draw a “causal arrow” you need theory
To establish a causal relationship you (usually) need
A variable Z is a confounder of the relationship between X and Y if Z is a causal influence on both X and Y
A variable Z is a confounder of the relationship between X and Y if Z is a causal influence on both X and Y
For example:
To establish a causal relationship between education and income, you need to account for race, which could affect both education and income
Z is a causal factor on both X and Y.
Must be “controlled for” to establish non-spurious relationship between X and Y.
E.g.:
Race confounds the relationship between education and income.
Z is influenced by X and influences Y.
Including as covariate elaborates on relationship between X and Y.
E.g.:
Occupation mediates the relationship between gender and income.
Z alters the relationship between X and Y.
Can be included as interaction variable to better describe the relationship between X and Y.
E.g.:
Marital status moderates the relationship between gender and income.
Z causally influenced by both X and Y.
Must not be “controlled for” when establishing relationship between X and Y.
E.g.:
Income is a collider for the relationship between gender and occupation.
Figures by Peter McMahan (source code)
Still from Severance (2022)
Road Runner and Wile E. Coyote © Warner Bros. Entertainment
Still from Gilmore Girls (2000)
Point out that the horizontal axis isn't from zero