Evidence map›Paper›PMID 28444468›Full record

ArticlePsychological research2018

A new statistical model for analyzing rating scale data pertaining to word meaning.

Felipe Munoz-Rubke, Karen Kafadar, Karin H James

Abstract read
PubMed Publisher
In one paragraph

Article in Psychological research, 2018. 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
0.2field-weighted citation impact, top 35% 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, 1 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

3 authors at 2 institutions in 1 country.

Felipe Munoz-RubkeCognitive Science Program, Indiana University Bloomington, 1101 E. 10th Street, Bloomington, IN, 47405, USA. lfmunoz@indiana.edu.
Karen KafadarDepartment of Statistics, University of Virginia, Charlottesville, USA.
Karin H JamesCognitive Science Program, Indiana University Bloomington, 1101 E. 10th Street, Bloomington, IN, 47405, USA.
Indiana University Bloomington · USUniversity of Virginia · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The concrete-abstract categorization scheme has guided several research programs. A popular way to classify words into one of these categories is to calculate a word's mean value in a Concreteness or Imageability rating scale. However, this procedure has several limitations. For instance, results can be highly distorted by outliers, ascribe differences among words when none may exist, and neglect rating trends in participants. We suggest using an alternative procedure to analyze rating scale data called median polish analysis (MPA). MPA is tolerant to outliers and accounts for information in multiple dimensions, including trends among participants. MPA performance can be readily evaluated using an effect size measure called analog R

Indexed as

Models, StatisticalSemanticsFemaleHumansLanguageMaleYoung Adult

Identifiers

PMID28444468
OpenAlexW2608703759

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.