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CAML--maximum likelihood consensus analysis.

André Assfalg1, Edgar Erdfelder

  • 1Lehrstuhl Psychologie III, Universität Mannheim, 68131 Mannheim, Germany. asfalg@psychologie.uni-mannheim.de

Behavior Research Methods
|July 28, 2011
PubMed
Summary
This summary is machine-generated.

Consensus analysis, using the R package CAML, estimates individual differences in competencies and response tendencies for unknown answer keys. This method provides reliable model fit, consensus measures, and valid parameter estimates.

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Area of Science:

  • Psychometrics
  • Statistical modeling

Background:

  • Consensus analysis estimates individual differences in competencies and response tendencies when answer keys are unknown.
  • Previous approaches to consensus analysis have often been impractical or unfeasible.

Purpose of the Study:

  • To introduce CAML, a set of R functions for maximum likelihood estimation in the general Condorcet model.
  • To provide a practical and feasible implementation of consensus analysis.
  • To validate the CAML approach through empirical testing.

Main Methods:

  • Implementation of maximum likelihood estimation for the general Condorcet model using R.
  • Development of algorithms within the CAML package.
  • Empirical validation using a recognition memory study with experimental parameter manipulations.

Main Results:

  • CAML provides measures of model fit and consensus.
  • CAML yields point and interval estimates for competencies and response tendencies.
  • CAML accurately estimates unknown answer keys.
  • Empirical validation demonstrated CAML's practical effectiveness and validity.

Conclusions:

  • CAML offers a robust and practical solution for consensus analysis.
  • The R package CAML enables valid estimation of individual differences and answer keys.
  • This approach overcomes limitations of prior consensus analysis methods.