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This latest edition has been fully updated to accommodate the needs of users of SPSS Releases 17, 18 and 19 while still being applicable to users of SPSS Releases 15 and 16. As with previous editions, Alan Bryman and Duncan Cramer continue to offer a comprehensive and user-friendly introduction to the widely used IBM SPSS Statistics. The simple, non-technical approach to quantitative data analysis enables the reader to quickly become familiar with SPSS and with the tests available to them. No previous experience of statistics or computing is required as this book provides a step-by-step guide to statistical techniques, including: Non-parametric tests Correlation Simple and multiple regression Analysis of variance and covariance Factor analysis. This book comes equipped with a comprehensive range of exercises for further practice, and it covers key issues such as sampling, statistical inference, conceptualization and measurement and selection of appropriate tests. The authors have also included a helpful glossary of key terms. The data sets used in Quantitative Data Analysis with IBM SPSS 17, 18 and 19 are available online at http://www.routledgetextbooks.com/textbooks/_author/bryman-9780415579193/; in addition, a set of multiple-choice questions and a chapter-by-chapter PowerPoint lecture course are available free of charge to lecturers who adopt the book.
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.
Quantitative Data Analysis with SPSS for Windows explains statistical tests using the latest version of SPSS, the most widely used computer package for analysing quantitative data. Using the same formula-free, non-technical approach as the highly successful non-windows version, it assumes no previous familiarity with either statistics or computing, and takes the reader step-by-step through each of the techniques for which SPSS for Windows can be used, including: correlation simple and multiple regression multivariate analysis of variance and covariance factor analysis The book also contains a comprehensive range of exercises with answers, and covers issues such as sampling, statistical significance, and the selection of appropriate tests.
Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS® Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output. Designed as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. The book provides in-depth chapter coverage of: IBM SPSS statistical output Descriptive statistics procedures Score distribution assumption evaluations Bivariate correlation Regressing (predicting) quantitative and categorical variables Survival analysis t Test ANOVA and ANCOVA Multivariate group differences Multidimensional scaling Cluster analysis Nonparametric procedures for frequency data Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.
The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software. In her bestselling manual, Julie Pallant guides you through the entire research process, helping you choose the right data analysis technique for your project. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic through to advanced statistical techniques. She outlines each technique clearly, providing step by step procedures for performing your analysis, a detailed guide to interpreting data output and examples of how to present your results in a report. For both beginners and experienced users in psychology, sociology, health sciences, medicine, education, business and related disciplines, the SPSS Survival Manual is an essential text. Illustrated with screen grabs, examples of output and tips, it is supported by a website with sample data and guidelines on report writing. This seventh edition is fully revised and updated to accommodate changes to IBM SPSS Statistics procedures, screens and output. 'An excellent introduction to using SPSS for data analysis. It provides a self-contained resource itself, with more than simply (detailed and clear) step-by-step descriptions of statistical procedures in SPSS. There is also a wealth of tips and advice, and for each statistical technique a brief, but consistently reliable, explanation is provided.' - Associate Professor George Dunbar, University of Warwick 'This book is recommended as ESSENTIAL to all students completing research projects - minor and major.' - Dr John Roodenburg, Monash University A website with support materials for students and lecturers is available at www.spss.allenandunwin.com
Master data management & analysis techniques with IBM SPSS Statistics 24 About This Book Leverage the power of IBM SPSS Statistics to perform efficient statistical analysis of your data Choose the right statistical technique to analyze different types of data and build efficient models from your data with ease Overcome any hurdle that you might come across while learning the different SPSS Statistics concepts with clear instructions, tips and tricks Who This Book Is For This book is designed for analysts and researchers who need to work with data to discover meaningful patterns but do not have the time (or inclination) to become programmers. We assume a foundational understanding of statistics such as one would learn in a basic course or two on statistical techniques and methods. What You Will Learn Install and set up SPSS to create a working environment for analytics Techniques for exploring data visually and statistically, assessing data quality and addressing issues related to missing data How to import different kinds of data and work with it Organize data for analytical purposes (create new data elements, sampling, weighting, subsetting, and restructure your data) Discover basic relationships among data elements (bivariate data patterns, differences in means, correlations) Explore multivariate relationships Leverage the offerings to draw accurate insights from your research, and benefit your decision-making In Detail SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data. The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed. By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease. Style and approach Provides a practical orientation to understanding a set of data and examining the key relationships among the data elements. Shows useful visualizations to enhance understanding and interpretation. Outlines a roadmap that focuses the process so decision regarding how to proceed can be made easily.
This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This is the first of a two-part guide to SPSS for Windows, introducing data entry into SPSS, along with elementary statistical and graphical methods for summarizing and presenting data. Part I also covers the rudiments of hypothesis testing and business forecasting while Part II will present multivariate statistical methods, more advanced forecasting methods, and multivariate methods. IBM SPSS Statistics offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The software is built around routines that have been developed, tested, and widely used for more than 20 years. As such, IBM SPSS Statistics is extensively used in industry, commerce, banking, local and national governments, and education. Just a small subset of users of the package include the major clearing banks, the BBC, British Gas, British Airways, British Telecom, the Consumer Association, Eurotunnel, GSK, TfL, the NHS, Shell, Unilever, and W.H.S. Although the emphasis in this guide is on applications of IBM SPSS Statistics, there is a need for users to be aware of the statistical assumptions and rationales underpinning correct and meaningful application of the techniques available in the package; therefore, such assumptions are discussed, and methods of assessing their validity are described. Also presented is the logic underlying the computation of the more commonly used test statistics in the area of hypothesis testing. Mathematical background is kept to a minimum.
This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced. Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors’ highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.
This supplementary book for the social, behavioral, and health sciences helps readers with no prior knowledge of IBM® SPSS® Statistics, statistics, or mathematics learn the basics of SPSS. Designed to reduce fear and build confidence, the book guides readers through point-and-click sequences using clear examples from real scientific research and invites them to replicate the findings. Relevant outcomes are provided for reference, and exercises at the end of Chapters 2 – 5 provide additional practice. After reading the book and using the program, readers will come away with a basic knowledge of the most commonly used procedures in statistics.