Bayesian Word Learning in Multiple Language Environments
Zinszer BD, Rolotti SV, Li F, Li P. Bayesian Word Learning in Multiple Language Environments. Cognitive Science 2017, 42: 439-462. PMID: 29154481, DOI: 10.1111/cogs.12567.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremChild LanguageChild, PreschoolHumansLanguage DevelopmentMultilingualismVerbal LearningVocabularyConceptsMutual exclusivity biasChild-directed speechReferential intentionsInfant language learnersSpeaker’s referential intentionsWord learningBayesian inference modelBilingual inputNew wordsCandidate lexiconsLanguage learnersDifferential demandsInductive problemsLanguage environmentLearning situationsBayesian modelIntentional modelSame corpusComputational modelSpeechWordsInference modelBilingual corpusSuch contextsCorpusAn evaluation of constrained randomization for the design and analysis of group‐randomized trials with binary outcomes
Li F, Turner EL, Heagerty PJ, Murray DM, Vollmer WM, DeLong ER. An evaluation of constrained randomization for the design and analysis of group‐randomized trials with binary outcomes. Statistics In Medicine 2017, 36: 3791-3806. PMID: 28786223, PMCID: PMC5624845, DOI: 10.1002/sim.7410.Peer-Reviewed Original ResearchConceptsGroup-level covariatesPossible allocation schemesMonte Carlo simulationsStatistical propertiesRandomization-based testsStatistical issuesCarlo simulationsPrespecified percentageAllocation schemeStatistical testsCandidate allocationsSpaceBinary outcomesAllocation techniquePermutation testPractical limitationsPower lossSchemeSuch designsGroup-randomized trialLarge numberF-testContinuous outcomesCovariate imbalanceInference