Please use this identifier to cite or link to this item:
http://dx.doi.org/10.25673/80397
Title: | Data generation for composite-based structural equation modeling methods |
Author(s): | Schlittgen, Rainer Sarstedt, Marko Ringle, Christian M. |
Issue Date: | 2020 |
Type: | Article |
Language: | English |
URN: | urn:nbn:de:gbv:ma9:1-1981185920-823514 |
Subjects: | Composite models Data generation Generalized structural component analysis GSCA Partial least squares PLS Structural equation modeling SEM |
Abstract: | Examining the efficacy of composite-based structural equation modeling (SEM) features prominently in research. However, studies analyzing the efficacy of corresponding estimators usually rely on factor model data. Thereby, they assess and analyze their performance on erroneous grounds (i.e., factor model data instead of composite model data). A potential reason for this malpractice lies in the lack of available composite model-based data generation procedures for prespecified model parameters in the structural model and the measurements models. Addressing this gap in research, we derive model formulations and present a composite model-based data generation approach. The findings will assist researchers in their composite-based SEM simulation studies. |
URI: | https://opendata.uni-halle.de//handle/1981185920/82351 http://dx.doi.org/10.25673/80397 |
Open Access: | Open access publication |
License: | (CC BY 4.0) Creative Commons Attribution 4.0 |
Sponsor/Funder: | Projekt DEAL 2020 |
Journal Title: | Advances in data analysis and classification |
Publisher: | Springer |
Publisher Place: | Berlin |
Volume: | 14 |
Issue: | 4 |
Original Publication: | 10.1007/s11634-020-00396-6 |
Page Start: | 747 |
Page End: | 757 |
Appears in Collections: | Fakultät für Wirtschaftswissenschaft (OA) |
Files in This Item:
File | Description | Size | Format | |
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Schlittgen et al._Data generation_2020.pdf | Zweitveröffentlichung | 449.03 kB | Adobe PDF | View/Open |