000			03010nam a22002177a 4500
999			_c4461 _d4461
005			20230113104607.0
008			230113b ||||| |||| 00| 0 eng d
020			_a9780367701833
082			_a511.42 _bMEH
100			_aMehmetoglu, Mehmet _94804
245			_aStructural equation modelling with partial least squares using Stata and R
260			_bCRC Press _aBoco Raton _c2021
300			_axxxvii, 345 p.
365			_aGBP _b42.99
504			_aTable of Contents Part I Preliminaries and Basic Methods 1. Framing Structural Equation Modelling 2. Multivariate Statistics Prerequisites 3. PLS Structural Equation Modelling: Specification and Estimation 4. PLS Structural Equation Modelling: Assessment and Interpretation Part II Advanced Methods 5. Mediation AnalysisWith PLS-SEM 6. Moderating/Interaction Effects Using PLS-SEM 7. Detecting Unobserved Heterogeneity in PLS-SEM Part III Conclusions 8. How to Write Up a PLS-SEM Study Part IV Appendices A. Basic Statistics Prerequisites
520			_aPartial least squares structural equation modelling (PLS-SEM) is becoming a popular statistical framework in many fields and disciplines of the social sciences. The main reason for this popularity is that PLS-SEM can be used to estimate models including latent variables, observed variables, or a combination of these. The popularity of PLS-SEM is predicted to increase even more as a result of the development of new and more robust estimation approaches, such as consistent PLS-SEM. The traditional and modern estimation methods for PLS-SEM are now readily facilitated by both open-source and commercial software packages. This book presents PLS-SEM as a useful practical statistical toolbox that can be used for estimating many different types of research models. In so doing, the authors provide the necessary technical prerequisites and theoretical treatment of various aspects of PLS-SEM prior to practical applications. What makes the book unique is the fact that it thoroughly explains and extensively uses comprehensive Stata (plssem) and R (cSEM and plspm) packages for carrying out PLS-SEM analysis. The book aims to help the reader understand the mechanics behind PLS-SEM as well as performing it for publication purposes. Features: Intuitive and technical explanations of PLS-SEM methods Complete explanations of Stata and R packages Lots of example applications of the methodology Detailed interpretation of software output Reporting of a PLS-SEM study Github repository for supplementary book material The book is primarily aimed at researchers and graduate students from statistics, social science, psychology, and other disciplines. Technical details have been moved from the main body of the text into appendices, but it would be useful if the reader has a solid background in linear regression analysis.
650			_aStructural equation modeling _911321
650			_aLeast squares _94223
700			_aVenturini, Sergio _911322
942			_2ddc _cBK