Table 3.

Summary of key steps and recommendations when determining the optimal number of classes that best fit the population. AIC, BIC, SABIC, LMR, VLMR, BLMR.

	Step	Description	Recommendation	Presentation
Optimal Class Selection	Fit Indices	AIC, BIC, SABIC	- For most analyses, we recommend using BIC and/or sample adjusted BIC.(32) - For analyses with small sample size (< 300) and/or multiple classes in the final model, use both AIC and BIC.(50)	- All indices used for model selection should be presented in the fit statistics table. - Both AIC and BIC should be presented if N < 300
	Model Testing	LMR, VLMR, BLMR	- VLMR should be used to test if a model with k classes is better than model with k-1 class.(34, 52) - In models with mixed indicator data types the BLMR is not recommended.	- All model statistical tests should be presented with a significance level of p < 0.05. - Clearly present the clinical or biological rationale for selecting a model where the p values may not be significant.
	Model Characteristics	Number of classes, the size of the smallest class and class separation are important determinant of model fit	- Classes with small N’s should be evaluated to determine whether outliers of a single indicator may be determining the class. - Models consisting of numerous small classes are less likely to be externally generalizable than models with fewer, well-distributed, classes.	- Present the fit statistics and the number of observations in each class of all the models used in the analysis - Present the entropy of all the models in the analysis