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Robust Latent Class Analysis/Evidence
Method evidence record

Robust Latent Class Analysis

Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Robust Latent Class Analysis
Taxonomic method record · latent-structure / statistics
  • Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340. · DOI 10.1214/009053604000000571
  • Vermunt, J. K., & Magidson, J. (2004). Latent class models. In D. Kaplan (Ed.), The Sage Handbook of Quantitative Methodology for the Social Sciences (pp. 175–198). Sage. · URL
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Curated claims

Claims persisted in the evidence ledger, each with its own assessment.

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketCluster Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketLatent Class Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMixture Modelingmachine-suggested · Relational suggestion, not evidence.Same method familyRobust Exploratory Factor Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Latent Profile Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Mixture Modelingmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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