Secondary Data in Mixed Methods Research by Daphne C. Watkins, is the latest contribution to the Mixed Methods Research Series. This brief text offers step-by-step procedures for incorporating existing data into mixed methods research designs, as well as identifying characteristics of datasets that make them good candidates for mixed methods projects.

A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods in action. McBee clearly demonstrates techniques such as Rubin causal model, direct acyclic graphs and propensity score analysis.

Evaluating Research 3e provides students with the skills to read and evaluate research studies. Aimed at courses where it will be more important for students to read than conduct research, this book covers all aspects social, behavioral, and health science research from the ground up, as well as main types of research methods.

This bestselling guide offers students and researchers the key skills they need to complete a visual methods research project, with a clear step-by-step approach and examples to demonstrate how methods can be applied in practice.

A practical, step-by-step guide to your qualitative research project, featuring interactive Silverman workshops to help you to think critically about research and choose the best path for your project.

This provocative book discusses a new turn in mixed methods research: merged methods. Moving beyond the binary quantitative-qualitative distinction, it shows you how to integrate methods in social research.

This book provides an adaptable, step-by-step framework for using and analysing video data in social science research in an ethical, effective way.

This book offers a four-step framework for transforming data into innovative, persuasive visualisations that will appeal to different audiences.

This practical, user-friendly guide consists of 100 original activities that have been designed to inspire and support educators of research ethics and integrity at undergraduate and postgraduate level.

Statistics and Data Visualization Using R: The Art and Practice of Data Analysis teaches students statistics visually, focusing on interpreting graphs and charts to learn statistical concepts, from the mean through regression.