Adventures in Social Research: Data Analysis Using IBM SPSS Statistics provides a practical, hands-on introduction to data conceptualization, measurement, and association through active learning. Students get step-by-step instruction on data analysis using the latest version of SPSS and the most current General Social Survey data. The authors start with an introduction to computerized data analysis and the social research process, then walk users through univariate, bivariate, and multivariate analysis using SPSS. The book contains applications from across the social sciences-sociology, political science, social work, criminal justice, health-so it can be used in courses offered in any of these departments.
Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. The book includes: * Original case studies and data sets * Practical exercises and lists of commands for each chapter * Downloadable Stata programmes created to work alongside chapters * A wide range of detailed applications using Stata * Step-by-step guidance on writing the relevant code. This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis.
If you want to learn to use R for data analysis but aren't sure how to get started, this practical book will help you find the right path through your data. Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research. It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers. The book: Shows you how to use R packages and apply functions, adjusting them to suit different datasets. Gives you the tools to try new statistical techniques and empowers you to become confident using them. Encourages you to learn by doing when running and adapting the authors' own code. Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect. Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.
The third edition gives students the extra guidance with SPSS they need without taking up valuable in-class time. Designed to work across disciplines, the step-by-step examples, hints and insights will give students the extra guidance they need to pick an analysis, run it using SPSS, and write it up. From measures of central tendency up to linear regression, the third edition covers topics most commonly covered in introductory statistics classes, showing the user how to plan a study, prepare data for analysis, perform the analysis and interpret the output from SPSS. The new Third Edition covers IBM (R) SPSS (R) version 25, includes a new section on Syntax, and all chapters have been updated to reflect current menu options along with many SPSS screenshots, making the process much simpler for the user. In addition, helpful hints and insights are provided through the features "Tips and Caveats" and "Sidebars."
Applications in STATA (R), IBM (R) SPSS (R), SAS (R), R, & HLM (TM)
Specifically designed for instructors teaching multilevel modeling courses where students use a variety of software packages, this text offers detailed guidance on the software and uniquely focuses on introductory multilevel modeling. The authors take a hands-on, applications-focused approach, allowing students to learn multilevel modeling using the software of their choice and highlighting the different assumptions implicit in each software package.
Now with a new companion website! Using IBM (R) SPSS (R) Statistics: An Interactive Hands-On Approach, Third Edition gives readers an accessible and comprehensive guide to walking through SPSS (R), providing them with step-by-step knowledge for effectively analyzing their data. From entering data to working with existing databases, and working with the help menu through performing factor analysis, Using IBM (R) SPSS (R) Statistics covers every aspect of SPSS (R) from introductory through intermediate statistics. The book is divided into parts that focus on mastering SPSS (R) basics, dealing with univariate statistics and graphing, inferential statistics, relational statistics, and more. Written using IBM (R) SPSS (R) version 25 and 24, and compatible with the earlier releases, this book is one of the most comprehensive SPSS (R) guides available.
"This book is remarkable in its accessible treatment of interaction effects. Although this concept can be challenging for students (even those with some background in statistics), this book presents the material in a very accessible manner, with plenty of examples to help the reader understand how to interpret their results." -Nicole Kalaf-Hughes, Bowling Green State University Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects. The book develops the statistical basis for the general principles of interpretive tools and applies them to a variety of examples, introduces the ICALC Toolkit for Stata, and offers a series of start-to-finish application examples to show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS regression. The author's website at www.icalcrlk.com provides a downloadable toolkit of Stata (R) routines to produce the calculations, tables, and graphics for each interpretive tool discussed. Also available are the Stata (R) dataset files to run the examples in the book.
Engaging and accessible, this book offers students a complete guide to using NVivo for qualitative data analysis. Drawing on their wealth of expertise, the authors offer detailed, practical advice that relates to students' own experience and research projects. Packed with real-world examples and case studies, the book supports students through every stage of qualitative data analysis. The Third Edition: Contains fully integrated instructions for using NVivo on both Mac and PC, with screenshots and click-by-click guidance. Seamlessly interweaves theory and practice in easy-to-follow steps. Empowers students to develop their critical thinking. Accompanied by video tutorials for both Mac and PC, web links and a host of other helpful online resources, this step-by-step book removes students' anxiety about tackling data analysis. Whether for advanced researchers or those approaching the task for the first time, this clear, yet comprehensive guide is the perfect companion for anyone doing qualitative data analysis with NVivo.
Providing information from data preparation and mean, median and mode, to regression, Lisa Daniels and Nicholas Minot use concise descriptions to help students understand the concepts behind statistics rather than the derivations of the formulas. Examples within the text come from criminal justice, economics, political science, psychology, public health and sociology, in addition to news articles on social science research. The book also includes three introductory chapters on research and a final chapter for writing up results and presenting data analyses.
