General fluid/inductive reasoning battery for a high-ability population.

Fluid ability Gf Intelligence Item-response theory Reasoning Test assembly Working memory training

Journal

Behavior research methods
ISSN: 1554-3528
Titre abrégé: Behav Res Methods
Pays: United States
ID NLM: 101244316

Informations de publication

Date de publication:
04 2019
Historique:
pubmed: 28 11 2018
medline: 5 9 2019
entrez: 28 11 2018
Statut: ppublish

Résumé

The validity of studies investigating interventions to enhance fluid intelligence (Gf) depends on the adequacy of the Gf measures administered. Such studies have yielded mixed results, with a suggestion that Gf measurement issues may be partly responsible. The purpose of this study was to develop a Gf test battery comprising tests meeting the following criteria: (a) strong construct validity evidence, based on prior research; (b) reliable and sensitive to change; (c) varying in item types and content; (d) producing parallel tests, so that pretest-posttest comparisons could be made; (e) appropriate time limits; (f) unidimensional, to facilitate interpretation; and (g) appropriate in difficulty for a high-ability population, to detect change. A battery comprising letter, number, and figure series and figural matrix item types was developed and evaluated in three large-N studies (N = 3,067, 2,511, and 801, respectively). Items were generated algorithmically on the basis of proven item models from the literature, to achieve high reliability at the targeted difficulty levels. An item response theory approach was used to calibrate the items in the first two studies and to establish conditional reliability targets for the tests and the battery. On the basis of those calibrations, fixed parallel forms were assembled for the third study, using linear programming methods. Analyses showed that the tests and test battery achieved the proposed criteria. We suggest that the battery as constructed is a promising tool for measuring the effectiveness of cognitive enhancement interventions, and that its algorithmic item construction enables tailoring the battery to different difficulty targets, for even wider applications.

Identifiants

pubmed: 30478802
doi: 10.3758/s13428-018-1098-4
pii: 10.3758/s13428-018-1098-4
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

507-522

Auteurs

Patrick Kyllonen (P)

Educational Testing Service, Princeton, NJ, USA. pkyllonen@ets.org.

Robert Hartman (R)

MITRE Corporation, Bedford, MA, USA.

Amber Sprenger (A)

MITRE Corporation, Bedford, MA, USA.

Jonathan Weeks (J)

Educational Testing Service, Princeton, NJ, USA.

Maria Bertling (M)

Educational Testing Service, Princeton, NJ, USA.

Kevin McGrew (K)

Institute for Applied Psychometrics, St. Joseph, MN, USA.

Sarah Kriz (S)

MITRE Corporation, Bedford, MA, USA.

Jonas Bertling (J)

Educational Testing Service, Princeton, NJ, USA.

James Fife (J)

Educational Testing Service, Princeton, NJ, USA.

Lazar Stankov (L)

Institute for Positive Psychology & Education, Australian Catholic University, Sydney, New South Wales, Australia.

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Classifications MeSH