The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
The stress appraisal measure (SAM): A multidimensional approach to cognitive appraisal
Abstract
Cognitive‐relational theory emphasizes the fundamental role of cognitive appraisal in the stress process. However, existing measures of stress appraisal suffer from conceptual and methodological problems. The present paper reports the development of a new stress appraisal scale that attempts to overcome some of these difficulties. Six dimensions of primary and secondary appraisal were identified: threat, challenge, centrality, controllable‐by‐self, controllable‐by‐others, uncontrollable‐by‐anyone. Items were generated for each of these dimensions, as well as for an overall perceived stressfulness scale. In three studies examining anticipatory stress experienced by undergraduates, these items were subjected to item‐selection, analysis and validation procedures. The findings provide strong support for the psychometric properties of the scales. Two factor analyses showed that the six appraisal dimensions were relatively independent. In multiple regression analyses, threat and centrality consistently emerged as significant predictors of overall stressfulness. The Stress Appraisal Measure (SAM) appears to be a promising instrument for use in future stress research.
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Cites
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Cited by
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Positive psychology 2.0: Towards a balanced interactive model of the good life.
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A Conceptual Model and Implications for Coping with Stressful Events in Police Work
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Metadata
- Authors
- Peacock, E. J., & Wong, P. T. P.
- Venue
- Stress Medicine
- Publisher
- Wiley
- Published
- 1990-07
- Volume
- 6
- Issue
- 3
- Pages
- 227-236
- ISSN
- 0748-8386 (ISSN-L)
- 1099-1700
- OpenAlex
- W2020568406
- Cited by
- 565
- References
- 41
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
