Literature DB >> 31599615

The Semantic Scale Network: An online tool to detect semantic overlap of psychological scales and prevent scale redundancies.

Hannes Rosenbusch1, Florian Wanders2, Ilse L Pit3.   

Abstract

Psychological measurement and theory are afflicted with an ongoing proliferation of new constructs and scales. Given the often redundant nature of new scales, psychological science is struggling with arbitrary measurement, construct dilution, and disconnection between research groups. To address these issues, we introduce an easy-to-use online application: the Semantic Scale Network. The purpose of this application is to automatically detect semantic overlap between scales through latent semantic analysis. Authors and reviewers can enter the items of a new scale into the application, and receive quantifications of semantic overlap with related scales in the application's corpus. Contrary to traditional assessments of scale overlap, the application can support expert judgments on scale redundancy without access to empirical data or awareness of every potentially related scale. After a brief introduction to measures of semantic similarity in texts, we introduce the Semantic Scale Network and provide best practices for interpreting its outputs. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Mesh:

Year:  2019        PMID: 31599615     DOI: 10.1037/met0000244

Source DB:  PubMed          Journal:  Psychol Methods        ISSN: 1082-989X


  3 in total

1.  Commentary: The construct validity of 'camouflaging' in autism: psychometric considerations and recommendations for future research - reflection on Lai et al. (2020).

Authors:  Zachary J Williams
Journal:  J Child Psychol Psychiatry       Date:  2021-06-17       Impact factor: 8.982

2.  MOWDOC: A Dataset of Documents From Taking the Measure of Work for Building a Latent Semantic Analysis Space.

Authors:  Kim F Nimon
Journal:  Front Psychol       Date:  2021-02-03

3.  Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation.

Authors:  Björn E Hommel; Franz-Josef M Wollang; Veronika Kotova; Hannes Zacher; Stefan C Schmukle
Journal:  Psychometrika       Date:  2021-12-14       Impact factor: 2.290

  3 in total

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