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Research projects are among the core components of many undergraduate and Masters degrees within environmental science and physical geography, and students increasingly undertake courses in quantitative research methodology before starting on their own assignment. This one-stop-shop text guides students through their research project from the initial stages of choosing a suitable topic, of conducting the relevant experiments and interpreting the data, through to effective presentation of the results.
It takes a case-study approach to illustrate the range of environmental science topics, with cases supplied by specialists in the field. Practical worked examples and self-assessment tasks illustrate key statistical and mathematical points so as to keep heavy theory to a minimum. It covers software such as Excel, SPSS and mathematical modelling, and includes statistical tables.
Preface Acknowledgement 1. General strategies for completing your research project successfully. 1.1 Introduction - why is this book necessary? 1.2 What on earth am I going to do for my research project? 1.3 Fundamentals of scientific research, the generation and testing of hypotheses (see also Chapter 3). 1.4 What constitutes research? Distinguishing between monitoring and research 1.5 Project planning 1.6 Conducting your project safely 1.7 How to conduct a literature review (see also chapter 7) 1.8 How to be a research student 1.9 How to manage your supervisor 1.10 Summary 2. Gathering your data. 2.1 Different types of data 2.2 Designing an experimental research project 2.3 How reliable are your data? 3. How to summarise your data. 3.1 Descriptive statistics 3.2 Probabilities and data distributions 3.3 Choosing the appropriate statistical test 4. Testing hypotheses. 4.1 Coincidence or causality? 4.2 Relationships and differences 4.3 Testing for differences 5. Spotting relationships. 5.1 Linear regression - to what extent does one factor influence another? 5.2 Multiple linear regression - to what extent is a given variable influenced by a range of other variables? 5.3 Non-linear regression 5.4 Pattern recognition 6. Making sense of past, present and future systems - mathematical modelling. 6.1 What is a model? 6.2 Functions of models 6.3 Which type of model should I use? 6.4 How do I build a model? 6.5 Steps in developing a model 6.6 Illustrative case study 7. Presenting your work. 7.1 Getting started - strategies for successful writing 7.2 How to write your dissertation 7.3 How to represent graphically your data 7.4 How to cite references 7.5 How to defend your work in an oral exam 7.6 How to make effective oral presentations 7.7 Summary Index