Analyzing Social Science Data
50 Key Problems in Data Analysis
- David de Vaus - University of Queensland, Australia
Data Collection & Analysis | Quantitative Methods | Research Methods & Evaluation
In this novel and refreshing textbook, David de Vaus directs students to the core of data analysis. The book is an authoritative guide to the problems facing beginners in the field. Analyzing Social Science Data guides students in: problems with the initial data; problems with the initial variables; how to handle too much data; how to generalize; problems of analyzing single variables; problems examining bivariate relationships; and problems examining multivariate relationships
The book is a tour de force in making data analysis manageable and rewarding for today's undergraduate studying research methods.
`I'm full of admiration for this book. Once again, David de Vaus has come up with a superb book that is well written and organized and which will be a boon to a wide range of students. He has taken a vast array of problems that users of quantitative data analysis procedures are likely to encounter. The selection of issues and problems ... reflects the experience of a true practitioner with a grasp of his field and of the intricacies of the research process. The selection of issues clearly derives also from experience of teaching students how to do research and analyse data....A large number of practitioners will want the book. I was surprised at how much I learned from this. This will be a vital book for the bookshelves of practitioners of the craft of quantitative data analysis' - Alan Bryman, Professor of Social Research, Loughborough University
I really like de Vaus problem-based approach to quantitative data analysis. Rather than having to read an entire book from A to Z, de Vaus compiled a list of 50 problems in data analysis that deemed him important or ubiqious enough. I personally can relate to most of these problems and I think that most researcher will come across those as most of them are fundamental. Writing a method book is always a compromise between depths and breadths. The great thing of its structure is that it can be read both from beginning to end as well as from problem to problem because of the crossreferences. That's why I've would liked a more extensive coverage of some of the problems, but that's of cause subjective. Also, it would be great if the book could be updated in terms of newer controversies, i.e. which measurement of effect size should be used. Multivariate methods would be a great addition too.
Very useful guide for students looking to complete research in social science, with a number of concepts being transferable to other areas.
recommended as a "rein check" for those students who are looking to embark on a social science project with limited prior experience.
Good book which deals with those tricky situations which are not always tackeled in similar books.
The problem-based approach to data analysis is a key strength of this book.
Analysing Social Science data: 50 Key Problems in Data Analysis, covers areas of data analysis which many research methods books do not cover in as much detail as this text does. The book consists of 50 short chapters which are group in to seven parts such as: Part One: How to Prepare Data for Analysis; Part Two: How to Prepare Variables for Analysis; Part Three: How to Reduce the Amount of Data to Analyse; Part Four: How and When to Generalise; Part Five: How to Analyse a Single Variable; Part Six: How to Analyse Two Variable; and Part Seven: How to Carry out Multivariate Analysis. This book is important for anyone undertaking a quantitative research project as the text covers succinctly how to code data, how to assess the reliability of research participants answers, how to judge question validity, and tackles problems with measuring the ‘mean average’ as well as preparing data for use with SPSS social software for data analysis. This book is essential reading for Masters and PhD students, as well as, researchers designing and implementing survey research in the social sciences.
This is a must book for research practitioners in almost all fields. Since the book was introduced in 2002, it has been widely recommended by the research community.
I recommend this book to my students who have little background in statistical methods. This book deals with the core assumptions about statistical analyses, common errors and problems and ways to address them.
The topical nature of the book serves as a user friendly guide for researchers to probe a specific issue. Highly recommended.
Enjoyable and easy to understand. It gives some really great suggestions on how to avoid many of the pitfalls in Data Analysis. Well worth keeping as a reference point.
We really liked the format of this book and feel that 'outing' key problems and providing a clear rationale for how to deal with them is very relevant to students new to researching and using data. We will also use this book for a Year 2 module called Researching Education (100 students)
This text will be adopted for the next academic year and the foreseeable future. The book is very well written and at a level that students on non mathematics/statistics courses can understand. Many of the issues that this text addresses are the ones that I encounter on a day-to-day basis when students consult me with their data analysis problems. For example, dealing with outliers, missing data and non-normally distributed distributions. I particularly like the way the chapter headings are stated as questions and that each chapter is relatively short. These features should reduce student anxiety, which is known to be an important issue for most students learning statistics.