Assessing categorization performance at the individual level: A comparison of Monte Carlo Simulation and Probability Estimate Model procedures

► Two analytical procedures for identifying young children as categorizers, the Monte Carlo Simulation and the Probability Estimate Model, were compared. ► The two analytical procedures resulted in different percentages of children being classified as categorizers. ► Results using the Monte Carlo Si...

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Bibliographic Details
Published in:Infant behavior & development 2011-04, Vol.34 (2), p.321-326
Main Authors: Arterberry, Martha E., Bornstein, Marc H., Haynes, O. Maurice
Format: Article
Language:English
Subjects:
Biological and medical sciences
Categorization
Categorizer classification
Child development
Developmental psychology
Fundamental and applied biological sciences. Psychology
Newborn. Infant
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Toddlers
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Summary:► Two analytical procedures for identifying young children as categorizers, the Monte Carlo Simulation and the Probability Estimate Model, were compared. ► The two analytical procedures resulted in different percentages of children being classified as categorizers. ► Results using the Monte Carlo Simulation were more consistent with group-level analyses than results using the Probability Estimate Model. ► These findings recommend using the Monte Carlo Simulation for determining individual categorizer classification. Two analytical procedures for identifying young children as categorizers, the Monte Carlo Simulation and the Probability Estimate Model, were compared. Using a sequential touching method, children aged 12, 18, 24, and 30 months were given seven object sets representing different levels of categorical classification. From their touching performance, the probability that children were categorizing was then determined independently using Monte Carlo Simulation and the Probability Estimate Model. The two analytical procedures resulted in different percentages of children being classified as categorizers. Results using the Monte Carlo Simulation were more consistent with group-level analyses than results using the Probability Estimate Model. These findings recommend using the Monte Carlo Simulation for determining individual categorizer classification.
ISSN:0163-6383
1879-0453
1934-8800
DOI:10.1016/j.infbeh.2011.02.003