Hi, I posted previously (https://communities.sas.com/t5/Statistical-Procedures/longitudinal-multilevel-analysis-multiple-records-per-person/td-p/772039) and got some excellent advice about modeling a longitudinal data set.
Summary: Dependent variable = gender. Independent variables = year and person’s position_department (e.g. Senior – Marketing, Principal-Marketing). The interest is in changes in gender over time.
Received advice to: run a model of year, position_department, and their interaction which worked nicely.
Now, I want to show predicted probabilities for: 1) ALL positions_departments combined (1 plot of gender over time), and 2) by department (vs. by position_department).
Problem: there are duplicate people in a given year because the same person can be associated with more than one position_department. For example, the person below shows up in the data 4 times in a given year (therefore, their gender would be counted 4 times):
person
gender
year
Position-department
department
position
1
M
2017
Senior-Marketing
Marketing
Senior
1
M
2017
Principal-Marketing
Marketing
Principal
1
M
2017
Senior-Finance
Finance
Senior
1
M
2017
Principal-Finance
Finance
Principal
This structure worked for my first question (analysis by position-department), however, if I just look at gender and year for all position-departments this person would be counted 4 times. Seems to be 2 options:
1) drop the duplicates (which would change the data and total N) and run 2 extra models. 1 extra model for all positions-departments combined after dropping duplicates (so data would have 1 row per person-year); and 1 extra model for analysis by department (data would have 1 row per person, year and department)
2) use the model previously estimated of year, position_department, and their interaction and output the predicted probabilities at department level and for all position-departments combined, which would give slightly different results since duplicates have not been dropped.
Best option? Any thoughts would be much appreciated. thank you!
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