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Capturing the Heterogeneity of Word Learners by Analyzing Persons.

ABSTRACT: Accurately capturing children's word learning abilities is critical for advancing our understanding of language development. Researchers have demonstrated that utilizing more complex statistical methods, such as mixed-effects regression and hierarchical linear modeling, can lead to a more complete understanding of the variability observed within children's word learning abilities. In the current paper, we demonstrate how a person-centered approach to data analysis can provide additional insights into the heterogeneity of word learning ability among children while also aiding researchers' efforts to draw individual-level conclusions. Using previously published data with 32 typically developing and 32 late-talking infants who completed a novel noun generalization (NNG) task to assess word learning biases (i.e., shape and material biases), we compare this person-centered method to three traditional statistical approaches: (1) a t-test against chance, (2) an analysis of variance (ANOVA), and (3) a mixed-effects regression. With each comparison, we present a novel question raised by the person-centered approach and show how results from the corresponding analyses can lead to greater nuance in our understanding of children's word learning capabilities. Person-centered methods, then, are shown to be valuable tools that should be added to the growing body of sophisticated statistical procedures used by modern researchers.

SUBMITTER: Jones IT

PROVIDER: S-EPMC11351650 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Capturing the Heterogeneity of Word Learners by Analyzing Persons.

Jones Ian T IT Kucker Sarah C SC Perry Lynn K LK Grice James W JW

Behavioral sciences (Basel, Switzerland) 20240813 8

Accurately capturing children's word learning abilities is critical for advancing our understanding of language development. Researchers have demonstrated that utilizing more complex statistical methods, such as mixed-effects regression and hierarchical linear modeling, can lead to a more complete understanding of the variability observed within children's word learning abilities. In the current paper, we demonstrate how a person-centered approach to data analysis can provide additional insights ...[more]

PMID: 39199104

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Project description:BackgroundInterventions are often adapted; some adaptations may provoke more favorable outcomes, whereas some may not. A better understanding of the adaptations and their intended goals may elucidate which adaptations produce better outcomes. Improved methods are needed to better capture and characterize the impact of intervention adaptations.MethodsWe used multiple data collection and analytic methods to characterize adaptations made by practices participating in a hybrid effectiveness-implementation study of a complex, multicomponent diabetes intervention. Data collection methods to identify adaptations included interviews, observations, and facilitator sessions resulting in transcripts, templated notes, and field notes. Adaptations gleaned from these sources were reduced and combined; then, their components were cataloged according to the framework for reporting adaptations and modifications to evidence-based interventions (FRAME). Analytic methods to characterize adaptations included a co-occurrence table, statistically based k-means clustering, and a taxonomic analysis.ResultsWe found that (1) different data collection methods elicited more overall adaptations, (2) multiple data collection methods provided understanding of the components of and reasons for adaptation, and (3) analytic methods revealed ways that adaptation components cluster together in unique patterns producing adaptation "types." These types may be useful for understanding how the "who, what, how, and why" of adaptations may fit together and for analyzing with outcome data to determine if the adaptations produce more favorable outcomes rather than by adaptation components individually.ConclusionAdaptations were prevalent and discoverable through different methods. Enhancing methods to describe adaptations may better illuminate what works in providing improved intervention fit within context.Trial registrationThis trial is registered on clinicaltrials.gov under Trial number NCT03590041 , posted July 18, 2018.

Dataset Information

Capturing the Heterogeneity of Word Learners by Analyzing Persons.

Publications

Capturing the Heterogeneity of Word Learners by Analyzing Persons.

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