Latent Class Analysis
- Finds hidden subgroups by analyzing patterns of responses across measured variables.
- Can reveal subtypes within disorders (example: two latent classes of depression — one characterized by low levels of anxiety and the other by high levels of anxiety).
- Can segment consumers by preferences (example: two latent classes of consumers — one group that prefers eco-friendly products and another group that prefers convenience over sustainability).
Definition
Section titled “Definition”Latent class analysis is a statistical technique used to identify distinct groups within a population based on their responses to a set of observed variables. These groups, known as latent classes, are hidden or unobserved and can only be inferred from the data.
Explanation
Section titled “Explanation”Latent class analysis infers unobserved (latent) group membership from observed response patterns. By modeling how observed variables vary together, the method uncovers underlying patterns and trends that indicate distinct subgroups. The resulting latent classes provide insights into the characteristics and behaviors of different segments that are not immediately apparent from the observed variables alone.
Examples
Section titled “Examples”Mental health disorders
Section titled “Mental health disorders”Researchers may use latent class analysis to identify subtypes of a disorder, such as depression, based on the presence or absence of certain symptoms. For example, a study may use latent class analysis to identify two latent classes of depression: one characterized by low levels of anxiety and the other characterized by high levels of anxiety.
Consumer behavior
Section titled “Consumer behavior”Researchers may use latent class analysis to identify different segments of the market based on their preferences and purchasing habits. For example, a study may use latent class analysis to identify two latent classes of consumers: one group that prefers eco-friendly products and another group that prefers convenience over sustainability.
Use cases
Section titled “Use cases”- Identifying and understanding distinct subgroups within a population.
- Uncovering underlying patterns and trends in the data.
- Providing insights into the characteristics and behaviors of different groups.
Related terms
Section titled “Related terms”- latent classes
- observed variables