Most statistics books will give definitions, examples and explanations of the statistics we discuss in this Part but, if you have no statistics background, some are heavy going. If you are planning to become involved in inferential statistics; you would be well advised first to consult your supervisor and whoever has responsibility for advising research students about data analysis. If at first you are told the university has no such person; persist. Ask around. Knock on doors if necessary. Ask other research students where they went for help. There might well be someone who knows everything about data analysis and statistical strategies and whose job it is to help research students. Some universities produce their own notes on the use of various statistical strategies so take advantage of what is provided. The following sources and the glossary will give definitions and examples but nothing beats face-to-face explanations.
Chi-square (X2) and Speartnan's rank of correlation coefficient (Spearman's rho) All the following will provide brief accounts of the purpose of Spearman's rho (p) and provide good summaries of chi-square.
Bryman; A. and Cramer, D. (1994) (Revised edition) Quantitative Data Analysis for Social Scientists. London: Routledge. Pages 159–64 provide a good account of the purpose and administration of chi-square. They discuss the statistical significance of the null hypothesis, significance levels, guidance about which SPSS commands to use and what tables will be provided.