SAGE Publications Ltd | ISBN 1 4129 0014 X | English | PDF | Pages: 193 | Size: 990 KB
The aim of this book is to describe some of the statistical techniques which are becoming increasingly common, particularly in the social sciences. The spread of sophisticated computer packages and the machinery on which to run them has meant that procedures which were previously only available to experienced researchers with access to expensive machines and research students can now be carried out in a few seconds by almost every undergraduate. The tendency of the packages to produce items of output which are unfamiliar to most users has lead to modifications in the content of quantitative data analysis courses, but this has not always meant that students gain an understanding of what the analytic procedures do, when they should be used and what the results provided signify. Our aim has been to provide the basis for gaining such an understanding. There are many texts and Internet resources covering the material which we do, but our experience is that many of them are too advanced, starting at too high a
level and including topics such as matrix algebra which leave many students baffled. What we have attempted to provide is an assistant which will help you make the transition from the simpler statistics (t-tests, analysis of variance) to more complex procedures; we are hoping we have translated the more technical texts into a form which matches your understanding. Each chapter provides an outline of the statistical technique, the type of question it answers, what the results produced tell you and gives examples from published literature of how the technique has been used. In recent years there has been a considerable growth in the use of qualitative research methods in many areas of social science including psychology and nursing and this has been accompanied by a decline in the previous preponderance of quantitative research. One feature of the qualitative research movement has been an emphasis upon the ethical issues involved in carrying out researchinvolving people, the need to recognise that the
participats own their data and that they should have an input – even perhaps a veto – over the interpretations made of it and the uses to which it is put. This concern has rejuvenated the ethical debate within quantitative research and brought back an awareness of the need to ensure that participants give informed consent to taking part, that they are not studied unknowingly or covertly, that they have the right to confidentiality and anonymity. This is not the place to discuss the ethics of research, but it is only proper that we should urge those considering quantitative research to be aware of the ethical guidelines applicable to their discipline and ensure they abide by them. Gathering the data which lends itself to quantitative analysis is not a value-free activity even if ‘number crunching’ may in itself appear to be so. Before describing the more complex statistical techniques, we begin by recapitulating the basics of statistical analysis, reminding you of the analysis of variance and outlining the
principles of the general linear model (GLM) which underpins many of the techniques described later.