Argyrous, G. (2011) Statistics for Research with a Guide to SPSS, 3rd edn. London: SAGE.

Chapters 3 and 4 cover the graphical and tabular description of data, all explained very clearly with lots of examples and screenshots using SPSS. Chapters 9, 10 and 11 explain measures of central tendency, measures of dispersion and the normal curve. For estimation look at Chapter 17 and for an introduction to hypothesis testing try Chapter 15.

De Vaus, D. (2002) Analyzing Social Science Data: 50 Key Problems in Data Analysis. London: SAGE.

A rather different approach. It is a bit like a series of answers to frequently asked questions. Part Five considers how to analyse a single variable. It illustrates using SPSS but does not cover proportions.

Diamantopoulos, A. and Schlegelmilch, B. (1997) Taking the Fear Out of Data Analysis. London: Dryden Press. Republished by Cengage Learning, 2000.

Chapters 7–11 cover most of what is in this chapter, clearly in more detail, but do not use SPSS. The authors do, however, include the treatment of proportions, which Argyrous does not.

Field, A. (2013) Discovering Statistics Using IBM SPSS Statistics, 4th edn. London: SAGE.

At 952 pages, this book is comprehensive and uses SPSS throughout. It is very good at explaining SPSS outputs, but is probably a bit too advanced for readers of Kent (2015) and is geared more to analysing experimental data. However, Chapter 2, ‘Everything you ever wanted to know about statistics’, is well worth a read, as is Chapter 4 on exploring data with graphs. There are companion volumes on using SAS and using R. If you are interested in Stata try Longest, K.C. (2012) Using Stata for Quantitative Analysis. Thousand Oaks, CA: Sage.

Yang, K. (2010) Making Sense of Statistical Methods in Social Research. London: SAGE.

This book focuses more on the conceptual foundations of statistical thinking than on how to calculate statistics. A lot more words and very few equations. Chapter 5 on estimating and measuring one important thing is well worth a read.