Dear all,
I have created a multiple linear regression model with a checklist score as dependant variable and independent variables device_group, child_age, education, monthsofexperience. The dataset is an observational dataset where healthworkers were observed when using a medical device in children under five and the checklist score describes the proportion of tasks (out of a total task list of 9) the health workers completed correctly. Because the dataset contains multiple observations for some health workers I have clustered on the health worker ID code. This is my code:
Now I am wondering what the difference between my model and a multilevel model is and how the interpretation would differ between the two? Compared to a model without clustering, the coefficients have not changed, but only the confidence intervals.
Thanks in advance,
best
Theresa
I have created a multiple linear regression model with a checklist score as dependant variable and independent variables device_group, child_age, education, monthsofexperience. The dataset is an observational dataset where healthworkers were observed when using a medical device in children under five and the checklist score describes the proportion of tasks (out of a total task list of 9) the health workers completed correctly. Because the dataset contains multiple observations for some health workers I have clustered on the health worker ID code. This is my code:
Code:
regress chechklist_score i.device_group i.child_age i.education monthsofexperience, cluster (hw_code_numeric)
Thanks in advance,
best
Theresa
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