Hi everyone,
I'm kinda stuck doing my analysis and I'd really appreciate some help since my only problem (for now) is pretty much that I can't find certain answer.
So I'm testing my two sample invariance using R Studio but I miss some important information. Basically I need to understand two thing to continue my work:
After using the measurementInvariance() function how do I understand where my invariance fail? I mean what index should I look at? CFI difference level?P value?It so how much should it be?
When I know where my invariance broke I use lavTestScore() but, once again, what index should I look to know what items should I set free?
I really need some help if possible, thank you so much!
Thank you fort your answer. Right now my main problem is about how to interpret the output I'm getting from r. More specifically how to understand what index should I consider to see what level of invariance fit is not good and how choose which item should I set free. Unfortunately the link you provide me leave that part out.
I'm sorry my english is not very good, hope I managed somehow to explain myself.
Thank you again!
This is a worldwide forum, and English is a world language. That is convenient, but it also means that there's no one "good" English. Language is to communicate and even people in a country like mine, where most people grow up speaking English, there are enough differences to create misunderstandings. So, if I'm not understanding, it's our common problem, not your fault or mine! Ok?
I can help if I see your results and how you generated them, which requires a reproducible example, called a reprex. It doesn't have to be the same data that you are using. Any data that will generate the same form of input will do.
I understand that reprex doesn't always work with some code. If that's the case, provide representative data, preferably from a data() set, and the code you use to present your result. Then we can help with interpreting it.
P value in less then 0.05 on fit.intercepts so should I try to free some item there? Or should I look to CFI and work on the fit.residual model? My sample is around 590 person for this type of invariance. But I also work with sample with more than 1000 people, so is it p value ok to look at to take decision?
I'm assuming that p20.==.p47., .p21.==.p48., .p22.==.p49. are really bad but I how do I understand what item they referring to? And how do I decide how mant item should I set free (considering my scale only got 8 items)?
Thank you
There's a lot to unpack here about the interpretation of the output. I'm still studying it. Some are fairly standard (lower AIC better than higher, ceteris paribus), but I'll need some more time to answer your specific questions.
In the meantime, I hope others can weigh in on the interpretation using these reprexs