dibi | Dibi - smart database abstraction layer | Database library

The variances of several of your latent variables are very small. For example, Dlbi appears to be effectively zero. That's the source of the issue here.

There are two things you can to try to remedy this.

First, it may work better to identify the model by fixing the latent variable variances to 1, rather than fixing the first indicator factor loadings to 1. Do this by specifying std.lv = TRUE.

Even then, it will likely be the case that loadings onto one or more of the group factors will have very small loadings. This indicates that there really isn't much of a distinct group factor in your data for this items that is distinct from the general factor. You should consider estimating a model that drops that group factor (as well as comparing with models dropping the other group factors one at a time). We discuss this issue some here: https://psyarxiv.com/q356f/

Additionally, you should constrain item loadings so that they are in the theoretically expected direction (e.g., all positive with a lower bound of 0). It is common for bifactor models to overextract variance in items and produce uninterpretable group factors that have a mix of positive and negative loadings. This can also cause convergence issues.

In general, this sort of unconstrained bifactor model tends to be overly flexible and tends to overfit to a similar degree as exploratory factor analysis. You should be sure to evaluate the bifactor model based not only on global model fit statistics, but also on whether the factor loadings actually resemble a true bifactor model--do the items each show substantial loadings on both the general factor and their group factor in the expected directions, or do items tend to load on only one or the other? See some examples in the paper linked above about this issue.

Another option would be to switch to exploratory bifactor modeling. This is implemented in R in the fungible package in the fungible::BiFAD() function. This approach is discussed here: https://www.sciencedirect.com/science/article/pii/S0001879120300555

Exploratory bifactor models are useful because they rely on targeted EFA rotation to estimate loadings. This makes convergence much more likely and can help to diagnose when a group factor is too weak to identify in the data.