Dear colleagues,
Starting situation:
- Balanced sample on individual level for two survey years.
- Variable "Happiness" coded from 0 - 10.
- Other variables: Age (continuous), marital_status (categorical: Single, Married, Widowed, Divorced, Separated), I would like to know whether the ΔHappiness between two points in time is related toΔage, Δmarital status etc.
Proceeding: Define ΔHappiness (dependent variable):
Model (I): ΔHappiness = 1 if positive change in Happiness, 0 otherwise.
Model (II): ΔHappiness = 1 if negative change in Happiness, 0 otherwise.
This (1/0) coding is a demand of my supervisor.
probit ΔHappiness Δage Δmarital_status
Question 1)
Δage is equal for all individuals.
Does the coefficient for Δage simply reflect the constant of the model? Does the coefficient of Δage have any explanatory power?
Question 2)
Variable "marital_status" has 5 categories => There are 20 (I guess) combinations for changes between two points in time: Single -> Married, Single -> Divorced etc.
Too many to include all combinations into the model.
Supposed I would generate the variable:
single_to_married = 1 if single_year1==1 & married_year2==1
(single_year1 reflects if individual was single in year 1, married_year 2 analogously defined)
other_changes = 1 if marital_status_year1 != marital_status_year2 & single_to_married != 1
(marital_status_year1/2 reflect marital_status of ind. in year 1/2)
and my model would be:
ΔHappiness = beta1 * Δage + beta2 * single_to_married + beta3 * other_changes ..
Would this model be valid?
Is there a sensible alternative to including some combinations of marital statuses?
Would I be able to interpret this model as
"Individuals being single in year1 and married in year2 have a higher probability of experiencing a positive (negative if model II) change in happiness"?
Thanks so much in advance!
Kind regards,
Mischa
Starting situation:
- Balanced sample on individual level for two survey years.
- Variable "Happiness" coded from 0 - 10.
- Other variables: Age (continuous), marital_status (categorical: Single, Married, Widowed, Divorced, Separated), I would like to know whether the ΔHappiness between two points in time is related toΔage, Δmarital status etc.
Proceeding: Define ΔHappiness (dependent variable):
Model (I): ΔHappiness = 1 if positive change in Happiness, 0 otherwise.
Model (II): ΔHappiness = 1 if negative change in Happiness, 0 otherwise.
This (1/0) coding is a demand of my supervisor.
probit ΔHappiness Δage Δmarital_status
Question 1)
Δage is equal for all individuals.
Does the coefficient for Δage simply reflect the constant of the model? Does the coefficient of Δage have any explanatory power?
Question 2)
Variable "marital_status" has 5 categories => There are 20 (I guess) combinations for changes between two points in time: Single -> Married, Single -> Divorced etc.
Too many to include all combinations into the model.
Supposed I would generate the variable:
single_to_married = 1 if single_year1==1 & married_year2==1
(single_year1 reflects if individual was single in year 1, married_year 2 analogously defined)
other_changes = 1 if marital_status_year1 != marital_status_year2 & single_to_married != 1
(marital_status_year1/2 reflect marital_status of ind. in year 1/2)
and my model would be:
ΔHappiness = beta1 * Δage + beta2 * single_to_married + beta3 * other_changes ..
Would this model be valid?
Is there a sensible alternative to including some combinations of marital statuses?
Would I be able to interpret this model as
"Individuals being single in year1 and married in year2 have a higher probability of experiencing a positive (negative if model II) change in happiness"?
Thanks so much in advance!
Kind regards,
Mischa