The examples in this book, provided to illustrate how to use Versions 20-23, are accompanied by menu-driven computer commands . Figures throughout the book show the results of those commands. In addition, a number of exercises are also included at the end of chapters 2 through 5 for additional practice.
The updated Second Edition of Alan C. Elliott and Wayne A. Woodward's "cut to the chase" IBM SPSS guide quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision making in a wide variety of disciplines. This one-stop reference provides succinct guidelines for performing an analysis using SPSS software, avoiding pitfalls, interpreting results, and reporting outcomes. Written from a practical perspective, IBM SPSS by Example, Second Edition provides a wealth of information-from assumptions and design to computation, interpretation, and presentation of results-to help users save time, money, and frustration. New to this edition: Step-by-step SPSS instructions have been integrated into every example. An all-new chapter describes the three methods used by SPSS to create graphics. A new Chapter 10 on Factor Analysis has been added. Examples in every chapter have been enhanced with added discussion and more detail. Additional screen shots and a redesigned format make the book easier to read and information easier to locate. All examples have been updated to reflect features in the latest version of SPSS.
Retaining the successful five-step process employed in Kevin Kelloway's 1998 book Using LISREL for Structural Equation Modeling, this edition updates it for Mplus, the "new LISREL". Mplus, which is growing in popularity, incorporates the ability to model both continuous and categorical latent and observed variables in a structural equation modelling framework, and the ability to estimate multi-level structural equation models. This new book provides students with a reader-friendly introduction to the major types of structural equation models implemented in the Mplus framework.
To help readers learn SPSS Syntax, it includes all kinds of examples from research in the Social Sciences with downloadable data sets so that readers can follow along. This book is brief, but still provides readers with an in-depth understanding of SPSS Syntax in a very accessible way.
An Introduction for the Uninitiated and the Unnerved
Focusing on developing practical R skills rather than teaching pure statistics, Dr. Kurt Taylor Gaubatz's A Survivor's Guide to R provides a gentle yet thorough introduction to R. The book is structured around critical R tasks, and focuses on applied knowledge, rather than abstract concepts. Gaubatz's easy-to-read approach helps students with little or no background in statistics or programming to develop real-world R skills through straightforward coverage of R objects and functions. Focusing on real-world data, the challenges of dataset construction, and the use of R's powerful graphing tools, the guide is written in an accessible, sympathetic, even humorous style that ensures students acquire functional R skills they can use in their own projects and carry into their work beyond the classroom.
Carol S. Parke's Essential First Steps to Data Analysis: Scenario-Based Examples Using SPSS provides instruction and guidance on preparing quantitative data sets prior to answering a study's research questions. Such preparation may involve data management and manipulation tasks, data organization, structural changes to the data files, or conducting preliminary analysis. Twelve research-based scenarios are used to present the content. Each scenario tells the "story" of a researcher who thoroughly examined their data and the decisions they made along the way. The scenario begins with a description of the researcher's study and his/her data file(s), then describes the issues the researcher must address, explains why they are important, shows how SPSS was used to address the issues and prepare data, and shares the researcher's reflections and any additional decision-making. Finally, each scenario ends with the researcher's written summary of the procedures and outcomes from the initial data preparation or analysis.
Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you're doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.
Building SPSS Graphs to Understand Data is a must-have for anyone needing to understand large or small amounts of data. It describes how to build and interpret graphs, showing how "understanding data" means that the graph must clearly and succinctly answer questions about the data. In most chapters, research questions are presented, and the reader builds the appropriate graph needed to answer the questions.
