In Chapter 8, analysis of quantitative data using statistical procedures will be covered. In a scientifi c investigation, the correct use of the most appropriate descriptive and inferential statistical techniques allows data to be summarised effectively and conclusions to be drawn. However, in any scientifi c investigation, the analysis is meaningless if the raw data that have been gathered are invalid or inaccurate. In many commercial, industrial and legal scenarios when management information or other evidence is being considered, there are often important questions to be addressed about the sources of evidence. Are the data up to date? What are the defi nitions of the variables being used? Are the data accurately measured? Consider the example of the university league tables published by the national press in the UK. These have columns such as teaching quality, entry standard of students, research quality, student to staff ratio, library resources and IT resources. When these league tables are published they are often analysed by the senior management of universities. However, these senior offi cers will not blindly accept the position in the league table quoted for their university. The initial response is usually to question the data reported in the league tables. What is meant by teaching quality? How is it measured? What raw data are used? Are the data valid, reliable and up-to-date? What is meant by student to staff ratio? Are part-time staff included? Are part-time students included? In performance analysis of sport, there are similar measurement issues on which the acceptance of the whole study depends. These measurement issues include validity, objectivity and reliability. Therefore, this chapter precedes
the Chapter 8 on quantitative data analysis in recognition that the quality of measurement is of critical importance to a scientifi c study.