Reviews

Statistics for Research with a Guide to SPSS

THIS THIRD edition is detailed and introduces the reader to the majority of statistical techniques required to analyse data sets. It is structured around the discussion of descriptive data first, which is always a good principle. Step by step the student is shown how to consider the findings within his or her data.

Well thought out examples are included and some are re-used at different points. Once the student is immersed in the book, he or she should be familiar with them and able to appreciate the contribution that different techniques can make.

There is an online resource available to download data files for what has been traditionally called statistical package for the social sciences, but which we should all start calling predictive analytics software statistics.

The files allow the reader to work through examples, which is an excellent way to learn. Otherwise the book is up to date and includes a good guide to using the chart builder and the new, but not so lovely, non-parametric dialogue box.

The strengths of this book include the depth in which it explores data sets. This means that the reader cannot jump precipitately to his or her statistical test of choice, to get the ‘magical’

...

Well thought out examples are included and some are re-used at different points. Once the student is immersed in the book, he or she should be familiar with them and able to appreciate the contribution that different techniques can make.

There is an online resource available to download data files for what has been traditionally called statistical package for the social sciences, but which we should all start calling predictive analytics software statistics.

The files allow the reader to work through examples, which is an excellent way to learn. Otherwise the book is up to date and includes a good guide to using the chart builder and the new, but not so lovely, non-parametric dialogue box.

The strengths of this book include the depth in which it explores data sets. This means that the reader cannot jump precipitately to his or her statistical test of choice, to get the ‘magical’ probability value, but must first consider what he or she is asking of the data set – a most valuable step. For nurses, this book would make a good companion to master’s and PhD level quantitative research projects because it covers the majority of statistical procedures that will be required.

But, the publication is not without its quirks. There is a lot of discussion about the use of cross tabulation tables – the author clearly is an enthusiast for these. By comparison, for healthcare students at least, more might be made of interval level scales because these are commonly used in post-graduate research.

In the discussion of Lambda testing, the same set of data is used as for Chi square testing. But, it is unclear which test would be best or why – or if both are equally valid.

Lastly the use and interpretation of logistic regression, which is used quite commonly in health research, is not included. Otherwise, I strongly recommend this to nurses who want to understand what they are doing in statistical analysis.

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