Introduction To R In Ibm Spss Modeler


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Introduction to R in IBM SPSS Modeler


Introduction to R in IBM SPSS Modeler

Author: Wannes Rosius

language: en

Publisher: IBM Redbooks

Release Date: 2016-10-14


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This IBM RedpaperTM publication focuses on the integration between IBM® SPSS® Modeler and R. The paper is aimed at people who know IBM SPSS Modeler and have only a very limited knowledge of R. Chapters 2, 3, and 4 provide you with a high level understanding of R integration within SPSS Modeler enabling you to create or recreate some very basic R models within SPSS Modeler, even if you have only a basic knowledge of R. Chapter 5 provides more detailed tips and tricks. This chapter is for the experienced user and consists of items that might help you get up to speed with more detailed functions of the integration and understand some pitfalls.

Interpreting Quantitative Data with SPSS


Interpreting Quantitative Data with SPSS

Author: Rachad Antonius

language: en

Publisher: SAGE

Release Date: 2003-01-22


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This is a textbook for introductory courses in quantitative research methods across the social sciences. It offers a detailed explanation of introductory statistical techniques and presents an overview of the contexts in which they should be applied.

Introduction to Structural Equation Modeling Using IBM SPSS Statistics and Amos


Introduction to Structural Equation Modeling Using IBM SPSS Statistics and Amos

Author: Niels Blunch

language: en

Publisher: SAGE

Release Date: 2012-11-09


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This comprehensive Second Edition offers readers a complete guide to carrying out research projects involving structural equation modeling (SEM). Updated to include extensive analysis of AMOS′ graphical interface, a new chapter on latent curve models and detailed explanations of the structural equation modeling process, this second edition is the ideal guide for those new to the field. The book includes: Learning objectives, key concepts and questions for further discussion in each chapter. Helpful diagrams and screenshots to expand on concepts covered in the texts. Real life examples from a variety of disciplines to show how SEM is applied in real research contexts. Exercises for each chapter on an accompanying companion website. A new glossary. Assuming no previous experience of the subject, and a minimum of mathematical knowledge, this is the ideal guide for those new to SEM and an invaluable companion for students taking introductory SEM courses in any discipline. Niels J. Blunch was formerly in the Department of Marketing and Statistics at the University of Aarhus, Denmark