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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.
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 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.