Academic Journal
Improving the Discriminant Validation of Multi-Item Scales.
| Τίτλος: | Improving the Discriminant Validation of Multi-Item Scales. |
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| Συγγραφείς: | Pieters, Constant, Baumgartner, Hans, Pieters, Rik |
| Πηγή: | Journal of Marketing Research (JMR); Jun2026, Vol. 63 Issue 3, p502-517, 16p |
| Θεματικοί όροι: | Test validity, Scaling (Social sciences), Test reliability, Confirmatory factor analysis, Statistical correlation, Sampling errors |
| Περίληψη: | Discriminant validation examines to what extent constructs measured with multi-item scales, which are hypothesized to be conceptually distinct, are empirically distinct. A literature review of published scale development studies shows that a variety of criteria and approaches to assess discriminant validity are in use. However, the requirements for an appropriate criterion have not been spelled out, which has led to the use of problematic criteria. The present research introduces three requirements that an appropriate discriminant validation criterion should satisfy, concerning the correlation, comparison standard, and comparison method. It shows that the common Fornell–Larcker criterion is based on an inappropriate comparison standard and method and that alternative criteria have weaknesses as well. The authors therefore propose an improved comparison standard, congeneric reliability, and develop a systematic discriminant validation procedure based on congeneric reliability and the existing phi criterion, both of which satisfy the three requirements. The procedure provides continuous measures of support for discriminant validity and accounts for measurement and sampling error. A detailed case study and reanalyses of seven published scale development articles demonstrate the application and strengths of the procedure. Example code and an online application facilitate its implementation. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Marketing Research (JMR) is the property of American Marketing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://www.jstor.org/openurl?issn=00222437&date=2026&volume=63&issue=3&spage=502 Name: JSTOR (all content) (s7799221) Category: fullText Text: Icon: https://imageserver.ebscohost.com/branding/jstor/iconJSTOR.gif MouseOverText: Full Text from JSTOR |
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| Header | DbId: edb DbLabel: Complementary Index An: 193488431 RelevancyScore: 1141 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1140.76538085938 |
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| Items | – Name: Title Label: Title Group: Ti Data: Improving the Discriminant Validation of Multi-Item Scales. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pieters%2C+Constant%22">Pieters, Constant</searchLink><br /><searchLink fieldCode="AR" term="%22Baumgartner%2C+Hans%22">Baumgartner, Hans</searchLink><br /><searchLink fieldCode="AR" term="%22Pieters%2C+Rik%22">Pieters, Rik</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Marketing Research (JMR); Jun2026, Vol. 63 Issue 3, p502-517, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Test+validity%22">Test validity</searchLink><br /><searchLink fieldCode="DE" term="%22Scaling+%28Social+sciences%29%22">Scaling (Social sciences)</searchLink><br /><searchLink fieldCode="DE" term="%22Test+reliability%22">Test reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Confirmatory+factor+analysis%22">Confirmatory factor analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+errors%22">Sampling errors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Discriminant validation examines to what extent constructs measured with multi-item scales, which are hypothesized to be conceptually distinct, are empirically distinct. A literature review of published scale development studies shows that a variety of criteria and approaches to assess discriminant validity are in use. However, the requirements for an appropriate criterion have not been spelled out, which has led to the use of problematic criteria. The present research introduces three requirements that an appropriate discriminant validation criterion should satisfy, concerning the correlation, comparison standard, and comparison method. It shows that the common Fornell–Larcker criterion is based on an inappropriate comparison standard and method and that alternative criteria have weaknesses as well. The authors therefore propose an improved comparison standard, congeneric reliability, and develop a systematic discriminant validation procedure based on congeneric reliability and the existing phi criterion, both of which satisfy the three requirements. The procedure provides continuous measures of support for discriminant validity and accounts for measurement and sampling error. A detailed case study and reanalyses of seven published scale development articles demonstrate the application and strengths of the procedure. Example code and an online application facilitate its implementation. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Marketing Research (JMR) is the property of American Marketing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/00222437251388994 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 502 Subjects: – SubjectFull: Test validity Type: general – SubjectFull: Scaling (Social sciences) Type: general – SubjectFull: Test reliability Type: general – SubjectFull: Confirmatory factor analysis Type: general – SubjectFull: Statistical correlation Type: general – SubjectFull: Sampling errors Type: general Titles: – TitleFull: Improving the Discriminant Validation of Multi-Item Scales. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pieters, Constant – PersonEntity: Name: NameFull: Baumgartner, Hans – PersonEntity: Name: NameFull: Pieters, Rik IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00222437 Numbering: – Type: volume Value: 63 – Type: issue Value: 3 Titles: – TitleFull: Journal of Marketing Research (JMR) Type: main |
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