Tamara Schamberger
5 Papers
Tamara Schamberger is an academic researcher. The author has contributed to research in topics: Computer science & Structural equation modeling. The author has an hindex of 1, co-authored 4 publications.
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Papers
The choice of structural equation modeling technique matters: A commentary on Dash and Paul (2021)
Florian Schuberth,Geoffrey S. Hubona,Ellen Roemer,Sam Zaza,Tamara Schamberger,Francis Chuah,Gabriel Cepeda-Carrión,Jörg Henseler +7 more
TL;DR: In this article , a Monte Carlo simulation demonstrates that the choice of the approach to structural equation modeling can have a substantial impact on the results and their validity, in general, analysts should choose a structural equation modelling approach that fits their conceptual model.
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Conducting Monte Carlo simulations with PLS-PM and other variance-based estimators for structural equation models: a tutorial using the R package cSEM
TL;DR: In this paper , the authors provide guidelines on how to conduct a Monte Carlo simulation for structural equation models with variance-based estimators using the R packages cSEM and c-SEM.
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Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023).
TL;DR: In this paper , the authors show that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the signal-to-noise ratio (SNR).
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Confirmatory composite analysis in human development research
TL;DR: The confirmatory composite analysis (CCA) as mentioned in this paper is a statistical approach suitable to assess composites, which is a special type of structural equation modeling that consists of model specification, model identification, model estimation and model assessment.
More powerful parameter tests? No, rather biased parameter estimates. Some reflections on path analysis with weighted composites.
TL;DR: Researchers object to a study's conclusion that path analysis via weighted composites yields more powerful parameter estimates, arguing that the comparison is flawed and that CB-SEM can deliver comparable results with alternative scaling methods.