Hello everybody!
Thank you in advance for reading, and for trying to help!
To put it briefly.. I am working with Structural Equiation Modeling (for the first time :S). In this study, we have 1000+ participants and we are studying the relationship between alcohol use and a personality construct.
Personality is measured with different validated questionnaires, but alcohol use is measured using a Likert-type scale on the number of times the participant has used alcohol in the last month (this is the usual way to measure alcohol use), so that: 0 (never), 1 (1-3 times), 2 (4-7 times), 3 (8-12 times), etc... up to 7 (40+ times)
My question is..
Other models with other alcohol use outcomes (validated questionnaires for example) show a pretty decent fit. However, models with this Likert-type variable usually show good CFI (>.95)... but low TLI (0.70-0.85) and sufficiently high RMSEA (0.10 - 0-13) as to reject the models..
Is it possible that this Likert-type format is hindering our chances to find a good fit? Is there any way (e.g. recoding the variable? reducing levels of the variable?) that could help improving the fit? i.e. reducing RMSEA...
Otherwise I'm guessing I'll just have to reject the models... what else
Thank you very much in advance!!
Best
S
Thank you in advance for reading, and for trying to help!
To put it briefly.. I am working with Structural Equiation Modeling (for the first time :S). In this study, we have 1000+ participants and we are studying the relationship between alcohol use and a personality construct.
Personality is measured with different validated questionnaires, but alcohol use is measured using a Likert-type scale on the number of times the participant has used alcohol in the last month (this is the usual way to measure alcohol use), so that: 0 (never), 1 (1-3 times), 2 (4-7 times), 3 (8-12 times), etc... up to 7 (40+ times)
My question is..
Other models with other alcohol use outcomes (validated questionnaires for example) show a pretty decent fit. However, models with this Likert-type variable usually show good CFI (>.95)... but low TLI (0.70-0.85) and sufficiently high RMSEA (0.10 - 0-13) as to reject the models..
Is it possible that this Likert-type format is hindering our chances to find a good fit? Is there any way (e.g. recoding the variable? reducing levels of the variable?) that could help improving the fit? i.e. reducing RMSEA...
Otherwise I'm guessing I'll just have to reject the models... what else
Thank you very much in advance!!
Best
S