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http://dx.doi.org/10.25673/81384
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DC Field | Value | Language |
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dc.contributor.author | Cho, Gyeongcheol | - |
dc.contributor.author | Hwang, Heungsun | - |
dc.contributor.author | Sarstedt, Marko | - |
dc.contributor.author | Ringle, Christian M. | - |
dc.date.accessioned | 2022-04-05T09:30:45Z | - |
dc.date.available | 2022-04-05T09:30:45Z | - |
dc.date.issued | 2020 | - |
dc.date.submitted | 2020 | - |
dc.identifier.uri | https://opendata.uni-halle.de//handle/1981185920/83339 | - |
dc.identifier.uri | http://dx.doi.org/10.25673/81384 | - |
dc.description.abstract | Generalized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice. | eng |
dc.description.sponsorship | Projekt DEAL 2020 | - |
dc.language.iso | eng | - |
dc.relation.ispartof | http://link.springer.com/journal/41270 | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Component-based structural equation modeling | eng |
dc.subject | Generalized structured component analysis | eng |
dc.subject | Model fit | eng |
dc.subject | GFI | eng |
dc.subject | SRMR | eng |
dc.subject.ddc | 650: Management | - |
dc.title | Cutoff Criteria for Overall Model Fit Indexes in Generalized Structured Component Analysis | eng |
dc.type | Article | - |
dc.identifier.urn | urn:nbn:de:gbv:830-882.0106459 | - |
dc.identifier.urn | urn:nbn:de:gbv:ma9:1-1981185920-833398 | - |
local.versionType | publishedVersion | - |
local.bibliographicCitation.journaltitle | Journal of marketing analytics | - |
local.bibliographicCitation.volume | 8 | - |
local.bibliographicCitation.issue | 3 | - |
local.bibliographicCitation.pagestart | 189 | - |
local.bibliographicCitation.pageend | 202 | - |
local.bibliographicCitation.publishername | Palgrave Macmillan | - |
local.bibliographicCitation.publisherplace | Houndmills | - |
local.bibliographicCitation.doi | 10.15480/882.2916 | - |
local.openaccess | true | - |
dc.identifier.ppn | 1733746412 | - |
local.bibliographicCitation.year | 2020 | - |
cbs.sru.importDate | 2022-04-05T09:27:20Z | - |
local.bibliographicCitation | Enthalten in Journal of marketing analytics - Houndmills : Palgrave Macmillan, 2013 | - |
local.accessrights.dnb | free | - |
Appears in Collections: | Fakultät für Wirtschaftswissenschaft (OA) |
Files in This Item:
File | Description | Size | Format | |
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Cho et al._Cutoff criteria_2020.pdf | Zweitveröffentlichung | 708.59 kB | Adobe PDF | View/Open |