1 / 27

LCA General Description

2. LCA General Description. People (or objects) from a heterogeneous population assumed to belong to a limited number of homogeneous groups referred to as latent classes"Latent classes are categories of a latent variable, each one of which contains individuals who are similar to each other and di

dick
Download Presentation

LCA General Description

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. 1 LCA General Description Statistical method for finding subtypes of related cases from multivariate categorical data LCA concerned with the structure of cases (i.e., the latent taxonomic structure) person-centered differs from factor analysis which is concerned with the structure of variables similar to cluster analysis, but is based on probabilistic theory

    2. 2 LCA General Description People (or objects) from a heterogeneous population assumed to belong to a limited number of homogeneous groups referred to as “latent classes” Latent classes are categories of a latent variable, each one of which contains individuals who are similar to each other and different from individuals in other categories

    3. 3 Good examples of LCA Patterns of cigarette smoking(Chen et al., 2004) General substance use(Flaherty, 2002) Grouping cancer patients by psychosocial needs(Soothill, 2004) Types of infant temperament(Loken, 2004) Attention-deficit/hyperactivity typology(Neuman et al., 1999) Eating disorder phenotypes(Keel et al., 2004) Subtypes of depression(Parker et al., 1999) Structure of psychosis (Kendler et al., 1998) Metabolic control(Seiffge-Krenke & Stemmler, 2003)

    4. 4 What would the data look like?

    5. 5 kp ? 23 = 8 Possible Response Patterns

    6. 6 Purpose of LCA Use patterns of responses to observed categorical variables to: identify the number of underlying classes classify each individual into one class and determine class size

    7. 7 How LCA Works There are 2 primary model parameters the prevalence of each case in a class (latent class probabilities; LCP) e.g., the proportion of individuals in "disease present" vs. "disease absent" latent classes conditional response probability (CRP) the probability that an individual within a particular class will respond "present" or "yes" to a target item e.g., the probability for each symptom (item) within a latent class being present

    8. 8 The LCA Model Observed Categorical Items (u’s) Categorical Latent Class Variable (c) Continuous or Categorical Covariates (x)

More Related