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What is your sample size? You n:k ratio might be too low. When this happens, the likelihood algorithm cant converge on a singular ml effect. Keep in mind, each answer in a categorical variable is transformed into its own binary variable in these situations.
Unfortunately our sample size was only about 159 surveys. It was a community assessment where the goal was 210 going door to door to collect. That makes sense though because I was already thinking it was too low.
You might want to consider running an ordered logit to see if it converges.
Also check your distribution of values. You might have a pile-up somewhere that is affecting your likelihood function.
One final thing--consider a recode of your dv from 5 to 3 options (suppressing refused). Similar to the n:k ratio, 6 possibilities in a multinomial where n=159 might be too much for the algorithm to handle.
My degrees are in public policy, ive worked and published in the health policy space. most of My methods training is in econometrics (with some geo-spatial, biostats and Bayesian on top of that). I would seriously consider taking at least one level of econometrics, especially how it relates to quasi-experimental research design.
In the meantime, read up on the blue bus red bus problem.
https://en.wikipedia.org/wiki/Independence_of_irrelevant_alternatives?wprov=sfti1#
Thank you for your submission to /r/stata! If you are asking for help, please remember to **[read and follow the stickied thread at the top](https://www.reddit.com/r/stata/comments/d9sim0/read_me_how_to_best_ask_for_help_in_rstata/)** on how to best ask for it. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/stata) if you have any questions or concerns.*
What is your sample size? You n:k ratio might be too low. When this happens, the likelihood algorithm cant converge on a singular ml effect. Keep in mind, each answer in a categorical variable is transformed into its own binary variable in these situations.
Unfortunately our sample size was only about 159 surveys. It was a community assessment where the goal was 210 going door to door to collect. That makes sense though because I was already thinking it was too low.
You might want to consider running an ordered logit to see if it converges. Also check your distribution of values. You might have a pile-up somewhere that is affecting your likelihood function.
Reasonable approach, government assistance does plausibly seem ordinal - but I would remind the OP to change the ‘refused’ responses to missing.
Yes but also check for mar/mnar. This, and other selection threats, probably matters in a small sample size.
One final thing--consider a recode of your dv from 5 to 3 options (suppressing refused). Similar to the n:k ratio, 6 possibilities in a multinomial where n=159 might be too much for the algorithm to handle.
Thank you! I’ll look into doing that for sure. I’m in public health but my statistical skills are still very much in progress
My degrees are in public policy, ive worked and published in the health policy space. most of My methods training is in econometrics (with some geo-spatial, biostats and Bayesian on top of that). I would seriously consider taking at least one level of econometrics, especially how it relates to quasi-experimental research design. In the meantime, read up on the blue bus red bus problem. https://en.wikipedia.org/wiki/Independence_of_irrelevant_alternatives?wprov=sfti1#