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Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method's flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software's SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the "how-tos" of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM

Finance --- Least squares. --- R (Computer program language) --- Structural equation modeling. --- Mathematical models. --- Open Access --- PLS-SEM) Using R --- Workbook --- Partial Least Squares Structural Equation Modeling --- R Software Environment

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This open access methodological book summarises existing analysing techniques using data from PIAAC, a study initiated by the OECD that assesses key cognitive and occupational skills of the adult population in more than 40 countries. The approximately 65 PIAAC datasets that has been published worldwide to date has been widely received and used by an interdisciplinary research community. Due to the complex structure of the data, analyses with PIAAC datasets are very challenging. To ensure the quality and significance of these data analyses, it is necessary to instruct users in the correct handling of the data. This methodological book provides a standardised approach to successfully implementing these data analyses. It contains examples of and tools for the analysis of the PIAAC data using different statistical approaches and software, and it offers perspectives from various disciplines. The contributing authors have hands-on experience of using PIAAC data, and/or they have conducted data analysis workshops with these data.

Assessment. --- Statistics . --- Education—Economic aspects. --- International education . --- Comparative education. --- Assessment, Testing and Evaluation. --- Statistics for Social Sciences, Humanities, Law. --- Education Economics. --- International and Comparative Education. --- Education, Comparative --- Education --- Global education --- Intellectual cooperation --- Internationalism --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- History --- Assessment, Testing and Evaluation --- Statistics for Social Sciences, Humanities, Law --- Education Economics --- International and Comparative Education --- Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy --- PIAAC data --- Large-scale assessment --- Data analysis --- Plausible values --- International comparison --- Key cognitive skills --- Literacy of adult population --- Numeracy of adult population --- Analysis with PIAAC datasets --- Stata --- R software --- Open access --- Education: examinations & assessment --- Social research & statistics --- Economics of specific sectors