Multiple regression with continuous variables.
Conventional statistical packages, e.g. SPSS, lm() in R, typically give me an F value, dfs, and a significance test on whether the model is performing well. I can also use ANOVA to compare whether one model is significantly better than another by finding out whether the increase in R square is significant.
Structural equation modeling programs, e.g. Mplus, lavaan package in R, also allow me to run regression with added benefits of missing data handling through full information maximum likelihood. Even though parameter estimates and R squares are similar between these two approaches, I do not get the F value and the significance testing of model doing regression in SEM style. Instead, I get model fit information with df being
0, and I cannot figure out how I could test whether one regression model is better than another.
- What are the differences between these two approaches, if any?
- In SEM style, how do I know a regression model is good, and how can I compare whether one regression model has significantly larger R square than another?