Reliability in content analysis: The case of semantic feature norms classification
Semantic feature norms (e.g., STIMULUS: car → RESPONSE: <has four="" wheels="">) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the r...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Springer US
2016
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| _version_ | 1826304788274348032 |
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| author | Bolognesi, M Pilgram, R van den Heerik, R |
| author_facet | Bolognesi, M Pilgram, R van den Heerik, R |
| author_sort | Bolognesi, M |
| collection | OXFORD |
| description | Semantic feature norms (e.g., STIMULUS: car → RESPONSE: <has four="" wheels="">) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses-that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme-that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.</has> |
| first_indexed | 2024-03-07T06:23:04Z |
| format | Journal article |
| id | oxford-uuid:f35466e9-e0c1-4a3e-ab0b-c03d3efc4d9d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:23:04Z |
| publishDate | 2016 |
| publisher | Springer US |
| record_format | dspace |
| spelling | oxford-uuid:f35466e9-e0c1-4a3e-ab0b-c03d3efc4d9d2022-03-27T12:11:18ZReliability in content analysis: The case of semantic feature norms classificationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f35466e9-e0c1-4a3e-ab0b-c03d3efc4d9dEnglishSymplectic Elements at OxfordSpringer US2016Bolognesi, MPilgram, Rvan den Heerik, RSemantic feature norms (e.g., STIMULUS: car → RESPONSE: <has four="" wheels="">) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses-that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme-that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.</has> |
| spellingShingle | Bolognesi, M Pilgram, R van den Heerik, R Reliability in content analysis: The case of semantic feature norms classification |
| title | Reliability in content analysis: The case of semantic feature norms classification |
| title_full | Reliability in content analysis: The case of semantic feature norms classification |
| title_fullStr | Reliability in content analysis: The case of semantic feature norms classification |
| title_full_unstemmed | Reliability in content analysis: The case of semantic feature norms classification |
| title_short | Reliability in content analysis: The case of semantic feature norms classification |
| title_sort | reliability in content analysis the case of semantic feature norms classification |
| work_keys_str_mv | AT bolognesim reliabilityincontentanalysisthecaseofsemanticfeaturenormsclassification AT pilgramr reliabilityincontentanalysisthecaseofsemanticfeaturenormsclassification AT vandenheerikr reliabilityincontentanalysisthecaseofsemanticfeaturenormsclassification |