This bestselling text pioneered the comparison of qualitative, quantitative, and mixed methods research design. For all three approaches, John W. Creswell and new co-author J. David Creswell include a preliminary consideration of philosophical assumptions; key elements of the research process; a review of the literature; an assessment of the use of theory in research applications, and reflections about the importance of writing and ethics in scholarly inquiry.New to this Edition Updated discussion on designing a proposal for a research project and on the steps in designing a research study.   Additional content on epistemological and ontological positioning in relation to the research question and chosen methodology and method.  Additional updates on the transformative worldview.  Expanded coverage on specific approaches such as case studies, participatory action research, and visual methods.  Additional information about social media, online qualitative methods, and mentoring and reflexivity in qualitative methods.  Incorporation of action research and program evaluation in mixed methods and coverage of the latest advances in the mixed methods field Additional coverage on qualitative and quantitative data analysis software in the respective methods chapters.  Additional information about causality and its relationship to statistics in quantitative methods.  Incorporation of writing discussion sections into each of the three methodologies.  Current references and additional readings are included in this new edition.

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

This sourcebook provides an introductory treatment of various advanced statistical methods applied to research in the field of communication. Written by authors who use these methods in their own research, each chapter gives a nontechnical overview of what the method is and how it can be used to answer communication-related questions or aide the researcher in dealing with difficult data problems. Most chapters include an example based on real data to illustrate basic concepts. Students and faculty interested in diving into a new statistical topic, such as latent growth modeling, multilevel modeling, propensity scoring, or time series analysis, will find each chapter an excellent springboard for acquiring the background needed to jump into more advanced, technical readings.