Evidence map›Paper›PMID 31920838›Full record

ArticleFrontiers in psychology2019

New Perspectives in Computing the Point of Subjective Equality Using Rasch Models.

Giulio Vidotto, Pasquale Anselmi, Egidio Robusto

Open access · goldAbstract read
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Article in Frontiers in psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact, top 74% 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Giulio VidottoDepartment of General Psychology, University of Padua, Padova, Italy.
Pasquale AnselmiDepartment of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Padova, Italy.
Egidio RobustoDepartment of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Padova, Italy.
University of Padua · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In psychophysics, the point of subject equality (PSE) is any of the points along a stimulus dimension at which a variable stimulus (visual, tactile, auditory, and so on) is judged by an observer to be equal to a standard stimulus. Rasch models have been found to offer a valid solution for computing the PSE when the method of constant stimuli is applied in the version of the method of transitions. The present work provides an overview of the procedures for computing the PSE using Rasch models and proposes some new developments. An adaptive procedure is described that allows for estimating the PSE of an observer without presenting him/her with all stimuli pairs. This procedure can be particularly useful in those situations in which psychophysical conditions of the individuals require that the number of trials is limited. Moreover, it allows for saving time that can be used to scrutinize the results of the experiment or to run other experiments. Also, the possibility of using Rasch-based fit statistics for identifying observers who gave unexpected judgments is explored. They could be individuals who, instead of carefully evaluating the presented stimuli pairs, gave random, inattentive, or careless responses, or gave the same response to many consecutive stimuli pairs. Otherwise, they could be atypical and clinically relevant individuals who deserve further investigation. The aforementioned developments are implemented using procedures and statistics that are well established in the framework of Rasch models. In particular, computerized adaptive testing procedures are used for efficiently estimating the PSE of the observers, whereas infit and outfit mean-squares statistics are used for detecting observers who gave unexpected judgments. Results of the analyses carried out on simulated data sets suggest that the proposed developments can be used in psychophysical experiments.

Indexed as

computerized adaptive testinginfitmethod of constant stimulimethod of transitionsoutfitpoint of subjective equalityRasch models

Identifiers

PMID31920838
PMCPMC6927926
OpenAlexW2991098502

What OpenQuestion holds

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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.