# Interpreting Quantitative Data With IBM SPSS Statistics (2nd Ed) by Rachad Antonious

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Clive Sims
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Antonious, Rachad. Interpreting Quantitative Data with IBM SPSS Statistics. (2013) London. Sage. Reviewer: Clive Sims.

This book, the second edition of an introductory text on interpreting quantitative data using IBM SPSS, is written by a Professor of Sociology who is also a mathematician. This may be initially off-putting to many students of the social sciences who are less numerate than might be desired due to unhappy experiences at school; however it shouldn’t be. Professor Rachad Antonius  is an accomplished teacher who clearly links research methodology to statistical analysis whilst providing the necessary mathematical background to enable intelligent understanding of the processes involved. Indeed he explicitly states, “Any statistical analysis must be preceded by a clear and reasoned formulation of the theoretical framework on which it is based” (p269). In the course of this discourse he leads the student into many interesting by-ways that are often neglected in introductory texts. Once the basic theoretical ground work has been covered there are exercises for the student to complete and tutorials on the use of IBM SPSS on supplied data files containing a wealth of concrete examples that further illustrate the theoretical material previously presented. In addition to the book there is an accompanying website; http://www.uk.sagepub.com/antonius2/main.htm which provides a wealth of additional material, including further in-depth exercises.

The book is divided into five parts and each part consists of two or more chapters followed by a useful summary, key words, exercises, and an SPSS tutorial. The chapters lead the student logically from the “Language of Statistics” through to “t-tests and ANOVA” and each chapter has an introduction which sets the scene for what follows. Some major changes have occurred since the first edition, these include new chapters on one-way and two-way ANOVA, the Chi-square test and linear regression; a new presentation of the notion of statistical association, and its relation to statistical inference; sets of exercises and ‘real-life’ examples to aid teaching and learning; SPSS lab sessions following each chapter which demonstrate how SPSS can be used in practice and lists of key terms to aid revision and further reading to enhance understanding. There are, in addition, four appendices; an extremely valuable introduction to Reporting a Quantitative analysis; followed by How to Create a Data File in SPSS; Area under the Normal Curve and finally a Table of Random Numbers. The accompanying website contains the answers to the labs and exercises, along with additional data sets and PowerPoint presentations for teaching purposes as well as a spread sheet with calculations for the numerical exercises and a spread sheet for the calculation of the margin of error.

Interpreting Quantitative Data with IBM SPSS Statistics is a well-produced book which is clearly written in a reader-friendly style. The topics progress logically and are clearly explained. Unlike many books for the student quantitative researcher the author spends time not only on the interpretation of the data but also on the skill of presenting the findings in report form, both formally and informally. This is an essential skill that is often not taught with potentially disastrous results when the student comes to present his/her dissertation.

I can thoroughly recommend this book both as a set text for an introductory course in quantitative methods in the social sciences and as a text for those who are working alone without the guidance of a tutor. For those of us whose introductory courses are lost in the mists of time this book is an excellent revision aide. I certainly found bits of information that I had long forgotten.

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