Premium
Accounting for Variable Task Discrimination in Divergent Thinking Fluency Measurement: An Example of the Benefits of a 2‐Parameter Poisson Counts Model and its Bifactor Extension Over the Rasch Poisson Counts Model
Author(s) -
Myszkowski Nils,
Storme Martin
Publication year - 2021
Publication title -
the journal of creative behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.896
H-Index - 55
eISSN - 2162-6057
pISSN - 0022-0175
DOI - 10.1002/jocb.490
Subject(s) - fluency , rasch model , cognitive psychology , psychology , polytomous rasch model , divergent thinking , probabilistic logic , latent variable , creativity , equivalence (formal languages) , poisson distribution , task (project management) , latent variable model , verbal fluency test , item response theory , cognition , psychometrics , social psychology , artificial intelligence , computer science , statistics , mathematics education , developmental psychology , mathematics , neuropsychology , management , neuroscience , economics , discrete mathematics
Abstract Fluency tasks are among the most common item formats for the assessment of certain cognitive abilities, such as verbal fluency or divergent thinking. A typical approach to the psychometric modeling of such tasks (e.g., Intelligence , 2016, 57, 25) is the Rasch Poisson Counts Model (RPCM; Probabilistic models for some intelligence and attainment tests . Copenhagen: Danish Institute for Educational Research, 1960), in which, similarly to the assumption of (essential) τ ‐equivalence in Classical Test Theory, tasks have equal discriminations—meaning that, beyond varying in difficulty, they do not vary in how strongly they are related to the latent variable. In this research, we question this assumption in the case of divergent thinking tasks, and propose instead to use a more flexible 2‐Parameter Poisson Counts Model (2PPCM), which allows to characterize tasks by both difficulty and discrimination. We further propose a Bifactor 2PPCM (B2PPCM) to account for local dependencies (i.e., specific/nuisance factors) emerging from tasks sharing similarities (e.g., similar prompts and domains). We reanalyze a divergent thinking dataset ( Psychology of Aesthetics, Creativity, and the Arts , 2008, 2, 68) and find the B2PPCM to significantly outperform the 2PPCM, both outperforming the RPCM. Further extensions and applications of these models are discussed.