Identifying subgroups: Part 1: Patterns among cross-sectional data.

Area of Science:

• Nursing Research
• Allied Health Sciences
• Quantitative Psychology

Background:

• Non-experimental research designs are prevalent in nursing and allied health.
• Identifying distinct subgroups within study samples is crucial for understanding heterogeneity.
• Existing methods for subgroup identification may not fully capture complex patterns.

Purpose of the Study:

• To introduce latent class mixture modeling (LCMM) as a powerful analytic strategy.
• To provide a worked example of LCMM application in nursing and allied health research.
• To enhance researchers' understanding of LCMM's capabilities for exploring sample heterogeneity.

Main Methods:

• Latent class mixture modeling (LCMM) was employed as the primary analytic technique.
• A worked example demonstrating the application of LCMM is presented.
• The focus is on a person-centered approach to subgroup identification.

Main Results:

• LCMM effectively identifies distinct subgroups within complex samples.
• The analysis revealed underlying heterogeneity not apparent with traditional methods.
• The worked example illustrates the practical implementation and interpretation of LCMM.

Conclusions:

• Latent class mixture modeling is a versatile tool for subgroup analysis in health research.
• This method allows for a nuanced exploration of heterogeneity in non-experimental studies.
• Researchers are encouraged to utilize LCMM for deeper insights into their sample characteristics.