Predicting Survey Responses: How and Why Semantics Shape Survey Statistics on Organizational Behaviour
Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are no...
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| Format: | Article |
| Language: | English |
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University of Memphis, United States of America
2014
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| Online Access: | http://ir.unimas.my/id/eprint/8456/ http://ir.unimas.my/id/eprint/8456/1/Predicting.pdf |
| _version_ | 1848836380148367360 |
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| author | Jan, Ketil Arnulf Kai, Rune Larsen Øyvind, Lund Martinsen Bong, Chih How |
| author_facet | Jan, Ketil Arnulf Kai, Rune Larsen Øyvind, Lund Martinsen Bong, Chih How |
| author_sort | Jan, Ketil Arnulf |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60–86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables
were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain. |
| first_indexed | 2025-11-15T06:22:50Z |
| format | Article |
| id | unimas-8456 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:22:50Z |
| publishDate | 2014 |
| publisher | University of Memphis, United States of America |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-84562022-09-29T06:39:29Z http://ir.unimas.my/id/eprint/8456/ Predicting Survey Responses: How and Why Semantics Shape Survey Statistics on Organizational Behaviour Jan, Ketil Arnulf Kai, Rune Larsen Øyvind, Lund Martinsen Bong, Chih How H Social Sciences (General) Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60–86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain. University of Memphis, United States of America 2014 Article NonPeerReviewed text en http://ir.unimas.my/id/eprint/8456/1/Predicting.pdf Jan, Ketil Arnulf and Kai, Rune Larsen and Øyvind, Lund Martinsen and Bong, Chih How (2014) Predicting Survey Responses: How and Why Semantics Shape Survey Statistics on Organizational Behaviour. PLoS ONE, 9 (9). pp. 1-3. ISSN 1932-6203 http://www.researchgate.net/publication/265294639_Predicting_Survey_Responses_How_and_Why_Semantics_Shape_Survey_Statistics_on_Organizational_Behaviour DOI: 10.1371/journal.pone.0106361 |
| spellingShingle | H Social Sciences (General) Jan, Ketil Arnulf Kai, Rune Larsen Øyvind, Lund Martinsen Bong, Chih How Predicting Survey Responses: How and Why Semantics Shape Survey Statistics on Organizational Behaviour |
| title | Predicting Survey Responses: How and Why Semantics
Shape Survey Statistics on Organizational Behaviour |
| title_full | Predicting Survey Responses: How and Why Semantics
Shape Survey Statistics on Organizational Behaviour |
| title_fullStr | Predicting Survey Responses: How and Why Semantics
Shape Survey Statistics on Organizational Behaviour |
| title_full_unstemmed | Predicting Survey Responses: How and Why Semantics
Shape Survey Statistics on Organizational Behaviour |
| title_short | Predicting Survey Responses: How and Why Semantics
Shape Survey Statistics on Organizational Behaviour |
| title_sort | predicting survey responses: how and why semantics
shape survey statistics on organizational behaviour |
| topic | H Social Sciences (General) |
| url | http://ir.unimas.my/id/eprint/8456/ http://ir.unimas.my/id/eprint/8456/ http://ir.unimas.my/id/eprint/8456/ http://ir.unimas.my/id/eprint/8456/1/Predicting.pdf |