RI-CLPM - Testing whether between and within effects significantly differ

[W.B. Vries, de]

Dear all,

I am currently using a random intercept cross-lagged panel model (RI-CLPM) to assess between and within effects. For the same variables, we find a negative relation at the between level (correlation between random intercepts), but a positive relation at the within level (deviation at time t as a predictor of the outcome at t+1). I was wondering whether one would be able to conduct a test to assess whether the two effects are indeed different. After some searching, I found that the following formula may be used here, but I am unsure whether that is the case:

Does anyone have any thoughts on this? Or does anyone know of a paper that attempts to do the same?

Best,

Wout de Vries

[Terrence Jorgensen]

It is a valid Wald test, if indeed beta1 and beta2 have uncorrelated sampling distributions.  I recall Dan Bauer doing that in the context of a multigroup model (can't recall the paper), and I expect independence across levels also holds.  But you can just label the 2 parameters and use lavTestWald(), which would allow for correlated sampling distributions (if necessary).  An asymptotically equivalent LRT would compare models with(out) an equality constraint on those parameters.

Terrence D. Jorgensen

Assistant Professor, Methods and Statistics

Research Institute for Child Development and Education, the University of Amsterdam

http://www.uva.nl/profile/t.d.jorgensen