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Now in its second edition, this edited book presents recent progress and techniques in partial least squares path modeling (PLS-PM), and provides a comprehensive overview of the current state-of-the-art in PLS-PM research. Like the previous edition, the book is divided into three parts: the first part emphasizes the basic concepts and extensions of the PLS-PM method; the second part discusses the methodological issues that have been the focus of recent developments, and the last part deals with real-world applications of the PLS-PM method in various disciplines.
This new edition broadens the scope of the first edition and consists of entirely new original contributions, again written by expert authors in the field, on a wide range of topics, including: how to perform quantile composite path modeling with R; the rationale and justification for using PLS-PM in top-tier journals; psychometric properties of three weighting schemes and why PLS-PM is a better fit to mode B; a comprehensive review of PLS software; how to perform out-of-sample predictions with ordinal consistent partial least squares; multicollinearity issues in PLS-PM using ridge regression; theorizing and testing specific indirect effects in PLS and considering their effect size; how to run hierarchical models and available approaches; and how to apply necessary condition analysis (NCA) in PLS-PM.
This book will appeal to researchers interested in the latest advances in PLS-PM as well as masters and Ph.D. students in a variety of disciplines who use PLS-PM methods. With clear guidelines on selecting and using PLS-PM, especially those related to composite models, readers will be brought up to date on recent debates in the field.
Produkteigenschaften
- Artikelnummer: 9783031377747
- Medium: Buch
- ISBN: 978-3-031-37774-7
- Verlag: Springer International Publishing
- Erscheinungstermin: 15.11.2024
- Sprache(n): Englisch
- Auflage: 2. Auflage 2023
- Produktform: Kartoniert
- Gewicht: 762 g
- Seiten: 472
- Format (B x H x T): 155 x 235 x 28 mm
- Ausgabetyp: Kein, Unbekannt