Evidence map›Paper›PMID 26305400›Full record

ArticlePsychometrika2016

Detection of Test Speededness Using Change-Point Analysis.

Can Shao, Jun Li, Ying Cheng

Abstract readEvaluation Study
PubMed Publisher
In one paragraph

Article in Psychometrika, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. A general dynamic learning model framework for cognitive diagnosis.The British journal of mathematical and statistical psychology · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Using Item Scores and Distractors to Detect Test Speededness.Applied psychological measurement · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Clarifying the Effect of Test Speededness.Applied psychological measurement · 2019
    Article
  15. Article
  16. 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

3 authors.

Can ShaoDepartment of Psychology, University of Notre Dame, 118 Haggar Hall, Notre Dame, IN, 46556, USA.
Jun LiDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, 171 Hurley Hall, Notre Dame, IN, 46556, USA. jun.li@nd.edu.
Ying ChengDepartment of Psychology, University of Notre Dame, 118 Haggar Hall, Notre Dame, IN, 46556, USA. ycheng4@nd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Change-point analysis (CPA) is a well-established statistical method to detect abrupt changes, if any, in a sequence of data. In this paper, we propose a procedure based on CPA to detect test speededness. This procedure is not only able to classify examinees into speeded and non-speeded groups, but also identify the point at which an examinee starts to speed. Identification of the change point can be very useful. First, it informs decision makers of the appropriate length of a test. Second, by removing the speeded responses, instead of the entire response sequence of an examinee suspected of speededness, ability estimation can be improved. Simulation studies show that this procedure is efficient in detecting both speeded examinees and the speeding point. Ability estimation is dramatically improved by removing speeded responses identified by our procedure. The procedure is then applied to a real dataset for illustration purpose.

Indexed as

Data Interpretation, StatisticalComputer SimulationDatasets as TopicFalse Positive ReactionsHumansLikelihood FunctionsPsychometricsTime Factorschange-point analysisfalse discovery rateitem response theorylikelihood ratio statistictest speededness

Identifiers

What OpenQuestion holds

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Registered trials

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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.