Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd Edition)

"Creswell's Research Design is an accessible and useful book that stimulates students through walk through experiences, use of exercises, and production of actual writing samples. It is a book that models the types of issues that best suit different approaches and allows students to understand when to use mixed methods. Furthermore, its focus on theory and paradigms is done in a way that helps students decode their meaning."

"One of the most formidable challenges of research design is stating your purpose. Creswell's approach takes the guesswork out of the process."

--STEVE GUERRIERO, Organization & Management, Antioch New England Graduate School

The Second Edition of the bestselling Research Design: Qualitative, Quantitative, and Mixed Methods Approaches offers a unique comparison of three key approaches to inquiry. This comparison begins with preliminary consideration of knowledge claims for all three approaches, a review of the literature, and reflections about the importance of writing and ethics in scholarly inquiry. The book also addresses the key elements of the process of research: writing an introduction; stating a purpose for the study; identifying research questions and hypotheses; using theory; defining, delimiting and stating the significance of the study; and advancing methods and procedures for data collection and analysis.

Key Features:

This book is ideal for readers who seek assistance in designing a full research study or planning a proposal for a scholarly journal article, dissertation or thesis. The book is an invaluable reference on the basics of research design as well as an effective text for graduate courses in Research Methods, Research Design, and related topics. The book serves a broad audience of social and human scientists in fields of marketing, management, criminal justice, psychology, sociology, K-12 education, higher and post-secondary education, nursing, health sciences, urban studies, and family research.

"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute

"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona

"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston

Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:

* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators

While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:

* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)

The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.