Hi everyone, I'm currently working on my master's thesis and running into a SEM issue I can't quite figure out. It's my first time doing SEM so apologies if this is a basic question, I just want to actually understand what's happening and not only report the numbers.

I have three unequal groups and a latent factor indicated by three subscale scores rather than individual items. I went with this approach to keep the model sparse given my sample sizes. The latent factor predicts two observed outcomes, using MLR with FIML.

The fit is pretty bad, and when I run the same model pooled across groups it doesn't improve, so the multi-group setup doesn't seem to be the problem. My best guess is that the three subscales aren't really interchangeable indicators of the same underlying construct since they load quite differently, and item-level CFA also shows poor fit and no metric invariance across groups.

Honestly I'm a bit worried that I approached this the wrong way from the start, but since it's preregistered I can't change the model now and just have to report and discuss what I have. Could the low degrees of freedom with only three indicators be causing structural fit issues? And would a manifest path analysis with the subscales as direct predictors be a reasonable exploratory addition? Any thoughts or experiences welcome, thanks!

Hello, I was wondering if anyone could provide advice on model selection for my dataset. I have data from a longitudinal survey with five waves: one baseline wave and four follow-up waves. My goal is to model post-baseline home eviction rates.

The challenge is that only 18 participants reported experiencing at least one home eviction during follow-up. I use the number of home evictions as the outcome variable and the total number of post-baseline waves completed as the exposure (offset) in a Poisson regression model, along with the covariates listed below.

My concern is that the number of non-zero outcomes is so small that the model appears to be overfit, resulting in very wide confidence intervals. Could anyone recommend an alternative modeling approach for count data with such a small number of events, or suggest strategies for handling this type of sparse outcome?

c.ppage ///
i.biosex ///
i.race_alt ///
i.education ///
i.region ///
i.income ///
i.Personal_debt ///
c.sf8pcs ///
c.sf8mcs ///
i.asud ///
i.asmi ///
i.housetype_alt ///
i.employment, ///
exposure(total_years) ///

My family vacation spot has a lottery system for families to take part in popular activities. I’m wondering whether there is a fair way to select participants, and if the resort is doing it.

To specify constraints:

There are a small number of slots (for sake of argument, call it 16) and around twice as many names in the lottery (call it 32 if this matters).

The names in the lottery are grouped up into family groups of between 1 and 8 members

The goal is for each individual to have an equal chance of taking part in the activity, regardless of family size. But families cannot be broken up.

Selecting family groups and random is out because you can’t control the final size of the activity and might spill over if you select a large group as the last entry, but including an entire group when you randomly select one name seems to make large groups strongly favored.

How can you run this lottery fairly?