I am working on my risk perception model using three measures- probability, affection, and severity. I have one item on a Likert scale (1-5) for each probability and affection. However, for the severity part, I have one item on the Likert scale (1-5) and 7 items of binary types (0,1). The Sample size is 203. I want to use CFA for risk perception measures because existing studies suggest the importance of three measures on risk perception. I use
lavaan package in r to compute CFA. Prior to this, I change all variables to ordered factor variables and run an analysis. I started by including only three items with the same scale (1-5). When I run the analysis and calculate the model fitness, I got srmr=0 rmsea=0 cfi=1 tli=1. It looks like a perfect model. I feel like something is wrong. Is it because the model is just identified?
Next approach, in my model, I compute the latent variable `severity' by combining all binary variables which is an ordered, factor. Then fit that variable along with the other 3 items of the same scale in the risk measure. When I run cfa I got this warning message: In lav_object_post_check(object) : lavaan WARNING: some estimated ov variances are negative.
I can see the negative variance in one of the items. I am not sure what can I do about that. I checked the no. of model parameters= 30. Does the error indicate a larger parameter compared to the sample size? For second model, srmr= 0.166, rmsea= 0.086, cfi=0.817 and tli=0.767
I would request your help. How can I resolve it? Should I take a different approach like PCA?