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Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response
Author(s) -
Arnulf Jan K.,
Dysvik Anders,
Larsen Kai R.
Publication year - 2018
Publication title -
human resource development quarterly
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 65
eISSN - 1532-1096
pISSN - 1044-8004
DOI - 10.1002/hrdq.21324
Subject(s) - semantics (computer science) , computer science , syntax , variation (astronomy) , resource (disambiguation) , presentation (obstetrics) , semantic data model , data science , natural language processing , information retrieval , medicine , computer network , physics , astrophysics , radiology , programming language
This is a methodological presentation of the relationship between semantics and survey statistics in human resource development (HRD) research. This study starts with an introduction to the semantic theory of survey response (STSR) and proceeds by offering a guided approach to conducting such analyses. The reader is presented with two types of semantic algorithms and a brief overview of how they are calculated and how they can be accessed by interested researchers. Subsequently, we use semantic data to reanalyze a previously published study on the relationships between perceptions of a trainee program, intrinsic motivation, and work outcomes. The semantic algorithms can explain between 31 and 55% of the variation in the observed correlations. This article shows how the statistical models originally used to explore the survey data can be replicated using semantics either alone or as an identifiable source of variation in the data. All the steps are presented in detail, and the datasets as well as the statistical syntax necessary to perform the analyses are made available to the readers. Implications for methodology and the improvement of predictive validity in HRD research are discussed.