Dear Statalist Forum,
I have panel data, where individuals over 4 rounds (representing time) make 4 decisions, coded as 1 if the correct decision was made, 0 otherwise.
I would like to simultaneously analyze these four categorical decisions as dependent variables.
I have transformed the data using xtset and xtreg, I cluster standard errors at the individual level and use random effects as the allocation to treatment or control is set (in a balanced way) at the beginning of the experiment and does not change.
So far, I only managed to analyze each decision on its own.
Previous posts suggested ologit, probit, and logit models, but these only allow for one dependent variable, but I would really like to analyze the four decisions per round at the same time.
Multiple logistic regression (logistic) seems not to work as it focuses on only one dependent variable but multiple independent ones.
Instead of having 4 dependent categorical variables, do you think having one dependent variable (still categorical) that combines the possible decisions is better (below is a table for better understanding)? Would this change my analysis as compared to having four dependent variables?
Thank you very much in advance and kind regards!
I have panel data, where individuals over 4 rounds (representing time) make 4 decisions, coded as 1 if the correct decision was made, 0 otherwise.
I would like to simultaneously analyze these four categorical decisions as dependent variables.
I have transformed the data using xtset and xtreg, I cluster standard errors at the individual level and use random effects as the allocation to treatment or control is set (in a balanced way) at the beginning of the experiment and does not change.
So far, I only managed to analyze each decision on its own.
Previous posts suggested ologit, probit, and logit models, but these only allow for one dependent variable, but I would really like to analyze the four decisions per round at the same time.
Multiple logistic regression (logistic) seems not to work as it focuses on only one dependent variable but multiple independent ones.
Instead of having 4 dependent categorical variables, do you think having one dependent variable (still categorical) that combines the possible decisions is better (below is a table for better understanding)? Would this change my analysis as compared to having four dependent variables?
Decision 1 | Decision 2 | Decision 3 | Decision 4 | Overall - dep var- code |
1 | 1 | 1 | 1 | 1 |
1 | 1 | 1 | 0 | 2 |
1 | 1 | 0 | 0 | 3 |
1 | 0 | 0 | 0 | 4 |
0 | 0 | 0 | 0 | 5 |
0 | 1 | 1 | 1 | 6 |
0 | 0 | 1 | 1 | 7 |
0 | 0 | 0 | 1 | 8 |
1 | 0 | 1 | 0 | 9 |
0 | 1 | 0 | 1 | 10 |
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