Latent variable mixture modeling: a flexible statistical approach for identifying and classifying heterogeneity

Latent variable mixture modeling is becoming increasingly popular in nursing research, in part due to the sophistication of the method in identifying relationships, patterns, and clusters in the data.

The aim of this study was to provide an overview of mixture modeling techniques, specifically as applied to nursing research, and to present examples from two studies to illustrate how these techniques may be used cross-sectionally and longitudinally.

The first data example demonstrates the use of latent profile analysis as applied to the St. George respiratory symptoms questionnaire in 2,232 smokers from the Lovelace Smokers Cohort. The second data example demonstrates growth mixture modeling as applied to condom use trajectories among 728 at-risk adolescents on probation.

Three classes of symptoms emerged among the smokers cohort: those who were high on all symptoms, those who were low on all symptoms, and those who were high on cough and phlegm only. These classes were then distinguishable by participant gender and wood smoke exposure. In the second data example, four classes of condom use emerged. Only 59% of the sample indicated the previously reported decline in condom use over time; condom use remained stable or significantly increased for the remaining 41%.

Both sets of results provide additional substantive information about patterns in the data that were not apparent from previously applied traditional methodological techniques. Considerations for the use of latent variable mixture modeling in nursing research are discussed.