Table 5.

Overall summary of three clustering techniques

	TwoStep	Latent Gold	SNOB
Method	Distance-based, agglomerative hierarchical cluster analysis	Finite mixture modeling to probabilistically identify latent classes	Finite mixture modeling to probabilistically identify latent classes
Stopping rule to identify number of subgroups	Automated using either ‘Bayesian information criterion’ or ‘Akaike’s information criterion’	Analyst choice using various criteria, including ‘Bayesian information criterion’, unexplained variance, Chi-square p-value	Automated using ‘Minimum message length’ principle
Suitable data types	Ordinal data require recoding as dichotomous or handled as if interval data	All types	All types
Report classification probability of individuals	No	Yes	Yes
Sensitivity to subgroups	Least	Middle	Most
Reproducibility	Very high	Very high	Very high
Accuracy	Very high	Very high	Very high
Cost	Most expensive	Less expensive	Free
Support	Extensive documentation, fee-based support available	Extensive documentation and some free support available	Some documentation but minimal support available
Interpretability of presentation of results	Results are presented numerically and graphically (charts of certainty of the subgroup structure, bar and pie charts of cluster frequencies, and charts displaying the importance of specific variables to subgroups)	Results are presented numerically and graphically (including a tri-plot displaying the relationships between subgroups)	Results are mostly numeric (although a tree diagram is produced showing the relationship between ‘mother’ and ‘daughter’ subgroups)
Learning curve (subjective judgement)	Easy	Middle	Hard