Illustrating Statistical Procedures

Learning about statistics is a stressful activity for many people. This text is designed to painlessly acquaint students, beginning researchers and statistical novices with a wide range of analytical possibilities. Through straightforward descriptions and concrete illustrations, the text explores procedures ranging from the simple (e.g. graphs and correlations) to the complex (e.g. structural equation models and time series analysis). Readers will learn which of the analysis tools is best to apply for a given situation.

The text provides a conceptual overview, in plain English, of each procedure’s purpose, use and application. Also included are discussions of some of the most important and fundamental statistical concepts which underpin many of the methods explored. To apply this knowledge, information about currently available computer software to carry out specific statistical procedures is included.

Illustrating Statistical Procedures: For Business, Behavioural & Social Science Research, Second Edition provides a solid foundation on which to build more formal learning of statistics. Furthermore it provides the conceptual awareness needed to become an enlightened consumer of quantitative research findings. No matter what your initial knowledge, you can learn statistics and be competent at using and interpreting them.

Table of Contents
Chapter 1 — The logic & language of statistical analysis; Chapter 2 — Measurement issues in quantitative research; Chapter 3 — Computer programs for analysing quantitative data; Chapter 4 — Example research context & quantitative data set; Chapter 5 — Descriptive statistics for summarising data; Chapter 6 — Correlational statistics for characterising relationships; Chapter 7 — Inferential statistics for hypothesis testing; Chapter 8 — Other commonly used statistical procedures; Chapter 9 — Specialised statistical procedures; Appendix A — A primer on basic mathematics, statistical notation & formulas for measures; Appendix B — General analytical sequences for multivariate investigations; Index