Video data is transforming the possibilities of social science research. Whether through mobile phone footage, body-worn cameras or public video surveillance, we have access to an ever-expanding pool of data on real-life situations and interactions. This book provides a flexible framework for working with video data and understanding what it says about social life. With examples from a range of real video research projects, the book showcases step-by-step how to analyse any kind of data, including both found and generated videos. It also includes a non-technical discussion of computer vision and its opportunities for social science research. With this book you will be able to: · Complete each step of the research process fully and efficiently, from data collection to management, analysis, and interpretation · Use video data in an ethical and effective way to maximise its impact · Utilise contemporary technology and accessible platforms such as YouTube, Twitter, Tik Tok and Facebook. This book is an ideal toolkit for researchers or postgraduate students across the social sciences working with video data as a part of their research projects. Accessible and practical, is written for qualitative and quantitative researchers, newcomers and experienced scholars. Features include interactive activities for different skill levels and ‘what to read next’ sections to help you engage further with the research mentioned in the book.

Racecar data acquisition used to be limited to well-funded teams in high-profile championships. Today, the cost of electronics has decreased dramatically, making them available to everyone. But the cost of any data acquisition system is a waste of money if the recorded data is not interpreted correctly. This book, updated from the best-selling 2008 edition, contains techniques for analyzing data recorded by any vehicle's data acquisition system. It details how to measure the performance of the vehicle and driver, what can be learned from it, and how this information can be used to advantage next time the vehicle hits the track. Such information is invaluable to racing engineers and managers, race teams, and racing data analysts in all motorsports. Whether measuring the performance of a Formula One racecar or that of a road-legal street car on the local drag strip, the dynamics of vehicles and their drivers remain the same. Identical analysis techniques apply. Some race series have restricted data logging to decrease the team’s running budgets. In these cases it is extremely important that a maximum of information is extracted and interpreted from the hardware at hand. A team that uses data more efficiently will have an edge over the competition. However, the ever-decreasing cost of electronics makes advanced sensors and logging capabilities more accessible for everybody. With this comes the risk of information overload. Techniques are needed to help draw the right conclusions quickly from very large data sets. In addition to updates throughout, this new edition contains three new chapters: one on techniques for analyzing tire performance, one that provides an introduction to metric-driven analysis, a technique that is used throughout the book, and another that explains what kind of information the data contains about the track.

Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them. The second edition is a substantial update of the first based on the authors’ experiences of teaching from the book for nearly a decade. The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chapter—group testing and splines. The computing has been completely updated, with the "emmeans" package now integrated into the book. The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book. The solutions manual and teaching videos have also been updated. Features: Requires no prior experience with R, and offers an introduction to the essential features and functions of R Includes numerous examples from medicine, psychology, sports, ecology, and many other areas Integrates extensive R code and output Graphically demonstrates many of the features and properties of various analysis methods Offers a substantial number of exercises in all chapters, enabling use as a course text or for self-study Supplemented by a website with data sets, code, and teaching videos Analysis of Categorical Data with R, Second Edition is primarily designed for a course on categorical data analysis taught at the advanced undergraduate or graduate level. Such a course could be taught in a statistics or biostatistics department, or within mathematics, psychology, social science, ecology, or another quantitative discipline. It could also be used by a self-learner and would make an ideal reference for a researcher from any discipline where categorical data arise.

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

A Step-by-Step Guide to Qualitative Data Coding is a comprehensive qualitative data analysis guide. It is designed to help readers to systematically analyze qualitative data in a transparent and consistent manner, thus promoting the credibility of their findings. The book examines the art of coding data, categorizing codes, and synthesizing categories and themes. Using real data for demonstrations, it provides step-by-step instructions and illustrations for analyzing qualitative data. Some of the demonstrations include conducting manual coding using Microsoft Word and how to use qualitative data analysis software such as Dedoose, NVivo and QDA Miner Lite to analyze data. It also contains creative ways of presenting qualitative findings and provides practical examples. After reading this book, readers will be able to: Analyze qualitative data and present their findings Select an appropriate qualitative analysis tool Decide on the right qualitative coding and categorization strategies for their analysis Develop relationships among categories/themes Choose a suitable format for the presentation of the findings It is a great resource for qualitative research instructors and undergraduate and graduate students who want to gain skills in analyzing qualitative data or who plan to conduct a qualitative study. It is also useful for researchers and practitioners in the social and health sciences fields.

Drawing on the expertise of major names in the field, this text provides comprehensive coverage of the key methods for analyzing, interpreting, and writing up qualitative research in a single volume.

A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.