Evidence map›Paper›PMID 26550803›Full record

ArticleCognition2016

Adults with poor reading skills: How lexical knowledge interacts with scores on standardized reading comprehension tests.

Gail McKoon, Roger Ratcliff

Open access · greenAbstract read
In one paragraph

Article in Cognition, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.7field-weighted citation impact, top 8% 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

7 citing papers in PubMed, 23 citations in OpenAlex.

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  7. Diffusion Decision Model: Current Issues and History.Trends in cognitive sciences · 2016
    Review
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

2 authors at 1 institution in 1 country.

Gail McKoonThe Ohio State University, United States. Electronic address: mckoon.1@osu.edu.
Roger RatcliffThe Ohio State University, United States.
The Ohio State University · US

Funding

Toward using comprehensive decision-making models in neuropsychological testingR01AG041176 · NIA · OHIO STATE UNIVERSITY · PI MCKOON, GAIL A, RATCLIFF, ROGER · 2012 to 2021
$3.8M
NIA NIH HHS R01 AG041176NIA NIH HHS R01-AG041176
6 · The paper itself

Abstract

Millions of adults in the United States lack the necessary literacy skills for most living wage jobs. For students from adult learning classes, we used a lexical decision task to measure their knowledge of words and we used a decision-making model (Ratcliff's, 1978, diffusion model) to abstract the mechanisms underlying their performance from their RTs and accuracy. We also collected scores for each participant on standardized IQ tests and standardized reading tests used commonly in the education literature. We found significant correlations between the model's estimates of the strengths with which words are represented in memory and scores for some of the standardized tests but not others. The findings point to the feasibility and utility of combining a test of word knowledge, lexical decision, that is well-established in psycholinguistic research, a decision-making model that supplies information about underlying mechanisms, and standardized tests. The goal for future research is to use this combination of approaches to understand better how basic processes relate to standardized tests with the eventual aim of understanding what these tests are measuring and what the specific difficulties are for individual, low-literacy adults.

Indexed as

ComprehensionLanguage TestsNeuropsychological TestsReadingAdolescentAdultAgedFemaleHumansMaleMiddle AgedYoung AdultDiffusion modelingLexical decisionReading scoresStruggling adult readers

Identifiers

PMID26550803
PMCPMC5112761
OpenAlexW2179277062

What OpenQuestion holds

Textmetadata
LicenceTDM
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.