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 researchers into feature ty...

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Veröffentlicht in:	Behavior research methods Jg. 49; H. 6; S. 1984 - 2001
Hauptverfasser:	Bolognesi, Marianna, Pilgram, Roosmaryn, van den Heerik, Romy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2017 Springer Nature B.V
Schlagworte:	Behavioral Science and Psychology Classification Cognitive ability Cognitive Psychology Content analysis Humans Methodological problems Norms Perceptions Psycholinguistics - methods Psycholinguistics - standards Psychology Qualitative Research Reliability Reproducibility of Results Research methodology Semantic analysis Semantic features Semantics Stimulus Sustainability Taxonomy Intercoder reliability Semantic feature norms Content analysis
ISSN:	1554-3528, 1554-3528
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Abstract	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.
AbstractList	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. 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. (ProQuest: ... denotes formulae/symbols omitted.) 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. 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.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.
Author	van den Heerik, Romy Bolognesi, Marianna Pilgram, Roosmaryn
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Cites_doi	10.1037/0096-3445.132.2.163 10.1037/h0036351 10.1037/0033-295X.84.4.327 10.4159/harvard.9780674181816 10.1037/h0031619 10.1080/02699931.2013.851068 10.1016/j.actpsy.2009.02.002 10.3758/BF03196439 10.1080/02643290125857 10.1093/acprof:oso/9780199217274.001.0001 10.1016/S0010-9452(13)80293-6 10.1017/CBO9780511499968.007 10.1177/0013164415596420 10.1207/s15516709cog2303_4 10.1080/19312450709336664 10.1016/0010-0285(75)90024-9 10.1111/j.1551-6709.2009.01068.x 10.3758/s13428-012-0263-4 10.1162/089892998563752 10.1177/001316446002000104 10.3758/s13428-013-0323-4 10.3758/MC.37.7.1026 10.1037/0033-295X.85.3.207 10.1086/266577 10.1609/aimag.v36i1.2564 10.3389/fnhum.2012.00315 10.1027/1614-2241/a000086 10.3758/BF03196311 10.3758/BF03192726 10.1037/0033-295X.89.2.123 10.1207/s15516709cog0000_33 10.1162/089892904322926773 10.3758/BRM.40.1.183 10.1080/17470218.2011.560272 10.11613/BM.2012.031 10.1515/cllt.2010.009 10.1037/a0016261 10.1162/coli.07-034-R2 10.3758/s13428-010-0028-x 10.1037/0033-295X.104.2.211 10.1017/langcog.2016.27 10.3115/1608938.1608948 10.1037/0278-7393.24.3.558 10.1093/oxfordhb/9780199957996.013.11 10.7551/mitpress/1602.001.0001
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Snippet	Semantic feature norms (e.g., STIMULUS: car → RESPONSE: <has four wheels>) are commonly used in cognitive psychology to look into salient aspects of given... Semantic feature norms (e.g., STIMULUS: car → RESPONSE: <has four wheels>) are commonly used in cognitive psychology to look into salient aspects of given... Semantic feature norms (e.g., STIMULUS: car ... RESPONSE: <has four wheels>) are commonly used in cognitive psychology to look into salient aspects of given...
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SubjectTerms	Behavioral Science and Psychology Classification Cognitive ability Cognitive Psychology Content analysis Humans Methodological problems Norms Perceptions Psycholinguistics - methods Psycholinguistics - standards Psychology Qualitative Research Reliability Reproducibility of Results Research methodology Semantic analysis Semantic features Semantics Stimulus Sustainability Taxonomy
Title	Reliability in content analysis: The case of semantic feature norms classification
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