On the dimensionality of crystallized intelligence: A smartphone-based assessment

Diana Steger, Ulrich Schroeders and Oliver Wilhelm

Intelligence, 2019, vol. 72, issue C, 76-85

Abstract: Crystallized intelligence (gc) is a prominent factor in consensual theories on the structure of intelligence. Although declarative knowledge is arguably a core aspect of gc, little is known about the dimensionality of knowledge in adults; the proposed dimensional models vary broadly from unidimensionality, to three-dimensional models (science, humanities, and civics), to a six-dimensional model with an overarching g-factor. While previous studies were mostly based on narrow item samples once administered to a specific sample within a restricted time frame, we used a smartphone-based approach to investigate the dimensionality of knowledge based on a large set of items administered to a heterogeneous sample. More specifically, questions were randomly drawn from a pool of 4050 items from 34 subject domains such as chemistry, arts, and politics and administered to an age- and ability-heterogeneous sample of 1117 participants. We calculated Weighted Likelihood Estimates separately for each domain and then estimated a series of principal component analyses with increasing number of factors. The component solution at different levels match models reported in previous studies on the dimensionality of knowledge. We conclude that the dimensionality of declarative knowledge highly depends on the item and person sample. Finally, we discuss different approaches to model gc, give advice on the measurement of gc in general and discuss weaknesses and strengths of mobile assessments.

Keywords: Crystallized intelligence; Declarative knowledge; Dimensionality; Smartphone-based assessment (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intell:v:72:y:2019:i:c:p:76-85

DOI: 10.1016/j.intell.2018.12.002

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