Literature DB >> 29322399

When is best-worst best? A comparison of best-worst scaling, numeric estimation, and rating scales for collection of semantic norms.

Geoff Hollis1, Chris Westbury2.   

Abstract

Large-scale semantic norms have become both prevalent and influential in recent psycholinguistic research. However, little attention has been directed towards understanding the methodological best practices of such norm collection efforts. We compared the quality of semantic norms obtained through rating scales, numeric estimation, and a less commonly used judgment format called best-worst scaling. We found that best-worst scaling usually produces norms with higher predictive validities than other response formats, and does so requiring less data to be collected overall. We also found evidence that the various response formats may be producing qualitatively, rather than just quantitatively, different data. This raises the issue of potential response format bias, which has not been addressed by previous efforts to collect semantic norms, likely because of previous reliance on a single type of response format for a single type of semantic judgment. We have made available software for creating best-worst stimuli and scoring best-worst data. We also made available new norms for age of acquisition, valence, arousal, and concreteness collected using best-worst scaling. These norms include entries for 1,040 words, of which 1,034 are also contained in the ANEW norms (Bradley & Lang, Affective norms for English words (ANEW): Instruction manual and affective ratings (pp. 1-45). Technical report C-1, the center for research in psychophysiology, University of Florida, 1999).

Entities:  

Keywords:  Best-worst scaling; Numeric estimation; Rating scales; Semantic judgment; Semantics

Mesh:

Year:  2018        PMID: 29322399     DOI: 10.3758/s13428-017-1009-0

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  5 in total

1.  Sound symbolism shapes the English language: The maluma/takete effect in English nouns.

Authors:  David M Sidhu; Chris Westbury; Geoff Hollis; Penny M Pexman
Journal:  Psychon Bull Rev       Date:  2021-04-05

2.  Concreteness ratings for 62,000 English multiword expressions.

Authors:  Emiko J Muraki; Summer Abdalla; Marc Brysbaert; Penny M Pexman
Journal:  Behav Res Methods       Date:  2022-07-22

3.  Rating norms should be calculated from cumulative link mixed effects models.

Authors:  Jack E Taylor; Guillaume A Rousselet; Christoph Scheepers; Sara C Sereno
Journal:  Behav Res Methods       Date:  2022-09-14

4.  Specificity ratings for Italian data.

Authors:  Marianna Marcella Bolognesi; Tommaso Caselli
Journal:  Behav Res Methods       Date:  2022-09-26

5.  Best-worst scaling improves measurement of first impressions.

Authors:  Nichola Burton; Michael Burton; Dan Rigby; Clare A M Sutherland; Gillian Rhodes
Journal:  Cogn Res Princ Implic       Date:  2019-09-23
  5 in total

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