Manuel Elias Rademaker

Manuel Elias Rademaker
University of Wuerzburg | JMU · Department of Econometrics

MSc

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7
Publications
3,294
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58
Citations

Publications

Publications (7)
Article
Purpose This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessme...
Article
The study’s purpose is threefold: (i) to propose partial least squares path modeling (PLS-PM) as a way to estimate models containing composites of composites and to compare the performance of the PLS-PM approaches in this context, (ii) to provide and evaluate two testing procedures to assess the overall model fit of such models, and (iii) to introd...
Thesis
Full-text available
Structural equation modeling (SEM) has been used and developed for decades across various domains and research fields such as, among others, psychology, sociology, and business research. Although no unique definition exists, SEM is best understood as the entirety of a set of related theories, mathematical models, methods, algorithms, and terminolog...
Article
Full-text available
Purpose – The paper enhances consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors. Design/methodology/approach – Correction for attenuation as originally applied by PLSc is modified to include a priori assumptions on the struct...
Article
In this paper we provide an extensive comparison between commonly used linear econometric methods in the audit fee literature and explicitly address their underlying assumptions. As opposed to common practice in similar papers we explicitly consider violations of the strict exogeneity assumption in terms of unobserved firm-specific effects and argu...

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Projects

Project (1)
Project
Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.).