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. 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.

Research highlights
► 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.