melogit vs. xtlogit - and the level of data

[Haneul Choi]

Hello,

I have a question regarding xtlogit vs. melogit and I thank you in advance for your valuable input on my question.
I am currently running 10-year census data from 2000 - to 2010.
My DV is an individual decision to choose a certain occupation, which is a binary variable.
My IV is the ratio of black people at the Public Micro Use Area (PUMA) level.
One thing to note is, that the dataset does not track the same individuals at the PUMA level each year.
Different individuals are included each year at the PUMA level. In other words, it is cross-sectional.

In my initial analysis, I ran the logit model, using xtlogit, using the random-effect model, with state dummy variables.
However, one of my professors told me I should run a multilevel model, having the PUMA level independent variable as the level two variable in the model.
While reading the posts on this forum, however, I found that xtlogit can cover the analysis of two-level data.
So I guess what I have to make sure of is the level of my data.

Since individuals are nested within PUMA and PUMA is nested within State, and everything is nested within year, is my data four-level? or am I wrong?
I know it might be a silly question, but I haven't used multilevel modeling before, so please excuse my ignorance!

Thank you so much!

[Carlo Lazzaro]

Haneul:
welcome to this forum.
You're seemingly dealing with a survey, that is a repeated cross-sectional (RCS) dataset and you also report a geographical nesting.
While some -xt- commands (such as -xtsum-) can be suitable to investigate some features of RCS datasets, the nested desing would point you out to -melogit-.
Eventually, I would say that your nesting desing stops at state leve, whereas -i.year- is simply a predictor.

[Haneul Choi]

Thanks Carlo, it makes sense that i.year is simply a preditor.
I have one more question - I don't have state-level predictors, but PUMA is a smaller geographical unit within STATE.
If this is the case, can I just use i.state as the predictor, and treat my data as just two-level data? hence just go with xtlogit, instead of melogit?

Thank you so much for your valuable input!

[Carlo Lazzaro]

Haneul:
if your -panelid- is individuals nested within PUMA, I would still go -melogit-.

[Haneul Choi]

Thank you so much Carlo Appreciated much!!!!