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Scaling constructs with semantic networks

Author

Listed:
  • James A. Danowski

    (University of Illinois at Chicago)

  • Kenneth Riopelle

    (Wayne State University)

Abstract

This paper introduces a method for creating scales of constructs based on word bigram cooccurrences in natural language text. Instead of using a stop-word list to drop less useful words, we use a start-word list that enables computing the cooccurrences of only these “smart words.” In this way, we can create scales to measure communication constructs by first listing the key terms in the conceptual definition, then expanding the terms by looking up synonyms in dictionaries such as WordNet. Following this, we compute the cooccurrence network among these words with a sliding window. Next, we extract the first dimension through principal components analysis and identify the words that load most highly on the first factor. For these words, we sum the frequencies, which produces the final index for the construct. This operationalization yields index scales that have high construct validity, which contributes to external validity. Extending the procedures of classic psychometric index construction into the natural language domain avoids the biases of data based on fixed-choice questionnaires. To demonstrate the procedures for construct operationalization, we use a dataset of news stories about the BP Deepwater Horizon Gulf Oil Spill, scaling environmental uncertainty and organizational responses to it including innovation, strategic planning, and changes in organizational structure.

Suggested Citation

  • James A. Danowski & Kenneth Riopelle, 2019. "Scaling constructs with semantic networks," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2671-2683, September.
    • Handle: RePEc:spr:qualqt:v:53:y:2019:i:5:d:10.1007_s11135-019-00879-5
    DOI: 10.1007/s11135-019-00879-5
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    References listed on IDEAS

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    1. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    2. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Nur Muhammaditya & Sudarsono Hardjosoekarto & One Herwantoko & Yulia Gita Fany & Mahari Is Subangun, 2022. "Institutional Divergence of Digital Item Bank Management in Bureaucratic Hybridization: An Application of SSM Based Multi-Method," Systemic Practice and Action Research, Springer, vol. 35(4), pages 527-553, August.
    2. James A. Danowski & Bei Yan & Ken Riopelle, 2021. "A semantic network approach to measuring sentiment," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(1), pages 221-255, February.

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