Evidence map›Paper›PMID 31853157›Full record

ArticleApplied psychological measurement2020

A Psychometric Model for Discrete-Option Multiple-Choice Items.

Daniel M Bolt, Nana Kim, James Wollack, Yiqin Pan, Carol Eckerly, John Sowles

Abstract read
In one paragraph

Article in Applied psychological measurement, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Two New Models for Item Preknowledge.Applied psychological measurement · 2022
    Article
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

6 authors.

Daniel M BoltUniversity of Wisconsin-Madison, USA.ORCID https://orcid.org/0000-0001-7593-4439
Nana KimUniversity of Wisconsin-Madison, USA.
James WollackUniversity of Wisconsin-Madison, USA.
Yiqin PanUniversity of Wisconsin-Madison, USA.
Carol EckerlyEducational Testing Service, Princeton, NJ, USA.
John SowlesEricsson, Inc., Santa Clara, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Discrete-option multiple-choice (DOMC) items differ from traditional multiple-choice (MC) items in the sequential administration of response options (up to display of the correct option). DOMC can be appealing in computer-based test administrations due to its protection of item security and its potential to reduce testwiseness effects. A psychometric model for DOMC items that attends to the random positioning of key location across different administrations of the same item is proposed, a feature that has been shown to affect DOMC item difficulty. Using two empirical data sets having items administered in both DOMC and MC formats, the variability in key location effects across both items and persons is considered. The proposed model exploits the capacity of the DOMC format to isolate both (a) distinct sources of item difficulty (i.e., related to the identification of keyed responses versus the ruling out of distractor options) and (b) distinct person proficiencies related to the same two components. Practical implications in terms of the randomized process applied to schedule item key location in DOMC test administrations are considered.

Indexed as

discrete-option multiple-choice itemsitem response theorymultiple-choice items

Identifiers

PMID31853157
PMCPMC6906389

What OpenQuestion holds

Textmetadata
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.