Intermediate Statistics
A Conceptual Course
- Brett W. Pelham - Georgetown University, USA, Montgomery College, USA
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Hands on Data Analysis: A Second Course in Statistics is a student-friendly text for advanced undergraduate and graduate courses. It begins with an introductory chapter that reviews descriptive and inferential statistics in plain language, avoiding extensive emphasis on complex formulas. The remainder of the text covers 13 different statistical topics ranging from descriptive statistics to advanced multiple regression analysis and path analysis. Each chapter contains a description of the logic of each set of statistical tests or procedures and then introduces students to a series of data sets using SPSS, with screen captures and detailed step-by-step instructions. Students acquire an appreciation of the logic of descriptive and inferential statistics, and an understanding of which techniques are best suited to which kinds of data or research questions.
“The text is highly readable, and the author has done a good job making difficult topics like path analysis easy for students to comprehend. As much as possible, the author uses simple language and explains things in ways that students would be able to grasp. His incorporation of numerous examples helps to facilitate this goal as well.”
“The key strengths of this text are is applied perspective, focusing on conceptual understanding of data analyses rather than arcane statistical proofs. It finds just the right balance of technical, conceptual and practical perspectives. In addition, the heavy reliance on real-world examples and supplemental information in appendices will render it an invaluable resource for young graduate students as they progress in their research training.”
“This is a breath of fresh air compared to most statistics texts—rigorous but highly readable. The author does an impressive job of making statistical concepts feel intuitive. In addition, the integration of datasets and SPSS problems to solve make this book unique.”
Perfect for my course, both in terms of the way that the chapters are very similar to my course outline but also with the incorporation of SPSS
A well written book that provides a wonderful coverage of a range of key statistical concepts. Particularly useful for regression analysis as it covers variants of this method that are rarely seen in other statistics books for the same audience. I am not adopting this book however as apart from this it adds very little over and above what is currently being used by my students, the interactive SPSS data sets and screenshots are also a little dated by comparison. A very good book, just doesn't do enough different for it to be adopted.
This is an excellent textbook but for our purposes this would be more suitable on a postgraduate level.
Clearly written with essential chapters on more advanced topics (appropriate use of ANCOVA; path analysis). Recommended for graduate students in clinical psychology.
The following review regards the book “Intermediate Statistics a Conceptual Course” by Brett W. Pelham.
There are many textbooks about statistics. The first question we should answer is whether all these books are necessary or not. To my point of view the answer is YES and I will explain why.
Statistic, as many others disciplines, can be approached in several ways and for different “population”. There are texts for beginners, who are commonly afraid of statistic, for students who have average knowledge of the discipline and for advanced ones. In addition some books are focused on particular statistical techniques which are used for ad hoc analysis and are not discussed in essential books. Crossing all the above conditions it would produce a multi dimensions table, with several boxes, each of these “designed” for specific subjects with particular areas of interest, levels of knowledge, approach to the problems, use of tools.
The aim of all the textbooks remain common: to explain the concepts, to support the decision of the readers and to facilitate the interpretation of the results. In other words, making a different example, it is like driving a car: a driver does not need to know how the motor works to drive the car from a place to another. He needs to have a clear idea of the rules of the street, to avoid accident, to know the road to reach the destination, to use the acceleration and the braking pedal etc. Once the driver knows how to drive and the general rules, he does not need to learn something again when he changes the car.
For beginners, especially for those who approach statistic for the first time, or for those who are not very confident with math, it is mainly important to explain the idea of the analysis, clarifying the meaning of an approach instead of another, and only secondly, if it is necessary, to explain the formula which “sustain” the idea. The most important thing is to maintain a clear idea of what to do and how to do. In addition it is useful to provide examples after the notions/concepts are explained.
Intermediate Statistics a Conceptual Course is a book which intermediate students could find valuable for several reasons:
1) The text maintains what it says in the title “Intermediate Statistics a Conceptual Course”. Those who have already some concepts of the subject and need detailer explanations will benefit in reading the book. Beginners for statistic could find also helpful explanations but it is possible that they experience some difficulties due the lack of knowledge. Moreover the book is not filled of formula but mainly of concepts and what and how to do to reach the results.
2) The book introduces statistical terms and concepts using plain words. Many examples help to familiarize with statistic. Moreover the Author explains how to conduct the analysis with the support of pictures taken during the use of SPSS. This statistical software is very used not only by professional statistician but also by students who want to conduct some analysis. The program is easy to use because of the possibility to have menu and windows in the selection of the data analysis. The examples presented in the book guide the reader in experiencing, by hand, what it is said in the text, in an efficient way. The reader gets skilled in conducting analysis and becomes more confident with statistic.
3) Chapter 1 summarizes basic elements of statistics helping to refresh concepts.
4) All the chapters have a nice introduction that makes the readers more confident of what it is expected to find in the following pages. This helps the reader to be more confident in what to focus in reading the chapter.
5) In the book there are “questions” in every chapter which can help the reader to revise the concepts and to try/rethink “by hand/computer” what was presented. Moreover there are also suggestions how results should be written in a paper. This is very useful because concepts alone are not useful if they are not correctly presented.
6) I liked the appendixes which are at the end of every chapter. They provide extra material and should be considered an important part of the book. In the book there are also useful web links for further explanations.
7) There are some chapters that I found very interesting and useful:
- Chapter 5: Reliability (and little bit of factor Analysis). The presence of this chapter helps the reader to follow a good path on how to describe variability among observed, correlated variables and in reducing them.
- Chapter 10: Examining Interactions in Multiple Regression Analysis. It approaches the problem of the interaction effects of variables/levels which too many times is not considered in the analysis of many research papers.
- Chapter 13: Meditation and Path Analysis. It provides the understanding in conducting analysis thinking on what do to, and, how to approach the problem. Who conducts the analysis has to end up not only with results but with a full rationale of the phenomena.
- Chapter 14, 15 and 16 respectively: Data cleaning; Data Merging and Data management; Avoiding Bias: Characterizing without capitalizing. They are essential chapters which should be read before to use any statistical techniques.
There are some suggestions which could be useful for improving the book in future revisions:
1) The Author could introduce some summary tables or diagrams for the choice of a technique instead of another at the end of the book.
2) The book is addressed to people who what to know more about statistics. I suggest to add some extra chapters regarding: Roc curves, Survival analysis (Kaplan Meier, Poisson and Cox regression), Conditional Logistic Regression, Pannel data analysis, Multilevel models.
Dr. Gabriele Messina
Research Professor of Public Health
University of Siena - Italy
This book is well-written and covers a range of important statistical methods. However, I would have preferred more emphasis on the mathematics and less on SPSS (even though I realise that students have a tendency to rely heavily upon the programme).
I consider this book as the next in line after digesting Neil Salkind book - Statistics for people who hate statistics. I would recommend this book to Geography post-graduates given that the conceptual approach adopted in this book.
