Evidence map›Paper›PMID 37998704›Full record

ArticleJournal of Intelligence2023

Visualizing Cross-Sections of 3D Objects: Developing Efficient Measures Using Item Response Theory.

Mitchell E Munns, Chuanxiuyue He, Alexis Topete, Mary Hegarty

Open access · goldAbstract read
In one paragraph

Article in Journal of Intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Mitchell E MunnsDepartment of Psychological & Brain Sciences, University of California, Santa Barbara, CA 93106, USA.
Chuanxiuyue HeDepartment of Psychological & Brain Sciences, University of California, Santa Barbara, CA 93106, USA.ORCID 0000-0002-5819-7171
Alexis TopeteDepartment of Psychological & Brain Sciences, University of California, Santa Barbara, CA 93106, USA.ORCID 0009-0000-6316-6006
Mary HegartyDepartment of Psychological & Brain Sciences, University of California, Santa Barbara, CA 93106, USA.ORCID 0000-0002-7777-8844
University of California, Santa Barbara · US

Funding

Office of Naval Research N00014211242
6 · The paper itself

Abstract

Spatial ability is important for success in STEM fields but is typically measured using a small number of tests that were not developed in the STEM context, have not been normed with recent samples, or have not been subjected to modern psychometric analyses. Here, an approach to developing valid, reliable, and efficient computer-based tests of spatial skills is proposed and illustrated via the development of an efficient test of the ability to visualize cross-sections of three-dimensional (3D) objects. After pilot testing, three measures of this ability were administered online to 498 participants (256 females, aged 18-20). Two of the measures, the Santa Barbara Solids and Planes of Reference tests had good psychometric properties and measured a domain-general ability to visualize cross-sections, with sub-factors related to item difficulty. Item-level statistics informed the development of the refined versions of these tests and a combined measure composed of the most informative test items. Sex and ethnicity had no significant effects on the combined measure after controlling for mathematics education, verbal ability, and age. The measures ofcross-sectioning ability developed in the context of geology education were found to be too difficult, likely because they measured domain knowledge in addition to cross-sectioning ability. Recommendations are made for the use of cross-section tests in selection and training and for the more general development of spatial ability measures.

Indexed as

cross-sectionitem response theorypersonnel selectionspatial abilitySTEM education

Identifiers

PMID37998704
PMCPMC10672093
OpenAlexW4388443530

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.