The Second Edition of Interpreting Quantitative Data with IBM SPSS Statistics is an invaluable resource for students analyzing qualitative data with IBM SPSS Statistics for the first time. The book clearly sets out a range of statistical techniques and their common applications, explaining their logic and links to the research process. It also shows how IBM SPSS Statistics can be used as a tool to aid analysis. Key Features of the Second Edition: • New chapters on ANOVA Factor Analysis and General Multilinear Model • IBM SPSS Statistics lab sessions following each chapter which demonstrate how IBM SPSS Statistics can be used in practice • Sets of exercises and ‘real-life’ examples to aid teaching and learning • Lists of key terms and further reading to enhance students' understanding • An improved text design making the book easier to navigate
Sampling is a fundamental aspect of the research process. Most studies are done on a sample, and not on the whole population. The results of any analysis depend on how well the procedure of sampling is conducted, the adequacy of the size of the sample, and whether it is representative or not. The purpose of this chapter is to explain the various types of samples, and to show how a simple random sample is constituted with the help of a table of random numbers. Errors due to sampling are also briefly explained.
After studying this chapter, you should know:
- the importance of having a representative sample when the aim is to generalize;
- what characterizes each of the two broad categories of samples (probabilistic or non-probabilistic);
- the ...