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Quantifying definitional relations in psychological measurement

Rohner, Jean Christophe LU and Levinsson, Henrik LU (2020) In New Ideas in Psychology 56.
Abstract

Quantitative inferences about psychological attributes, such as extraversion, depression and empathy, involve measurement instruments as well as mathematical models that specify how indicators should be aggregated. The type of model that is appropriate for doing so is conditional on the type of relation that exists between an attribute, its facets and its indicators. The common assumption is that such relations are causal relations. Here, instead, we address definitional attribute-facet relations. Our aim is to find an appropriate mathematical model for when an attribute is defined in terms of facets, instead of causing or being caused by facets. In doing so, we describe the semantics of definitions in logical form. From this form we... (More)

Quantitative inferences about psychological attributes, such as extraversion, depression and empathy, involve measurement instruments as well as mathematical models that specify how indicators should be aggregated. The type of model that is appropriate for doing so is conditional on the type of relation that exists between an attribute, its facets and its indicators. The common assumption is that such relations are causal relations. Here, instead, we address definitional attribute-facet relations. Our aim is to find an appropriate mathematical model for when an attribute is defined in terms of facets, instead of causing or being caused by facets. In doing so, we describe the semantics of definitions in logical form. From this form we then derive continuous functions for attribute-facet relations using fuzzy logic. A model with main effects and interactions between facets seems to be more powerful for representing definitional relations than traditional formative and reflective models. This has important implications for measurement in basic and applied research.

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publication status
published
subject
keywords
Causal relation, Construct validity, Definitional relation, Fuzzy logic, Measurement model, Psychological assessment
in
New Ideas in Psychology
volume
56
article number
100749
publisher
Elsevier
external identifiers
  • scopus:85080837736
ISSN
0732-118X
DOI
10.1016/j.newideapsych.2019.100749
language
English
LU publication?
yes
id
255f7967-9162-4f49-8d8f-2ad57e353c9b
date added to LUP
2020-03-20 15:38:35
date last changed
2022-04-18 21:24:16
@article{255f7967-9162-4f49-8d8f-2ad57e353c9b,
  abstract     = {{<p>Quantitative inferences about psychological attributes, such as extraversion, depression and empathy, involve measurement instruments as well as mathematical models that specify how indicators should be aggregated. The type of model that is appropriate for doing so is conditional on the type of relation that exists between an attribute, its facets and its indicators. The common assumption is that such relations are causal relations. Here, instead, we address definitional attribute-facet relations. Our aim is to find an appropriate mathematical model for when an attribute is defined in terms of facets, instead of causing or being caused by facets. In doing so, we describe the semantics of definitions in logical form. From this form we then derive continuous functions for attribute-facet relations using fuzzy logic. A model with main effects and interactions between facets seems to be more powerful for representing definitional relations than traditional formative and reflective models. This has important implications for measurement in basic and applied research.</p>}},
  author       = {{Rohner, Jean Christophe and Levinsson, Henrik}},
  issn         = {{0732-118X}},
  keywords     = {{Causal relation; Construct validity; Definitional relation; Fuzzy logic; Measurement model; Psychological assessment}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{New Ideas in Psychology}},
  title        = {{Quantifying definitional relations in psychological measurement}},
  url          = {{http://dx.doi.org/10.1016/j.newideapsych.2019.100749}},
  doi          = {{10.1016/j.newideapsych.2019.100749}},
  volume       = {{56}},
  year         = {{2020}},
}