Introduction
>The structure of this book: research design, data collection and data analysis
>Taught degree projects: what they are, things to watch out for, and practical points
>PhD second studies: what happens when your first study changes your plans
>Strategies and principles: demonstrating excellence, exploring territory rather than trying to prove a hunch, avoiding trouble, and learning from the wise
>Choosing a topic, or cups of coffee, easy pickings and blaming others
>Departments, systems and supervisors: how to behave like a professional, get a good reference, and avoid needless trouble
>‘How do I get a good mark?’ – things that can send out the wrong signal, and how to avoid these
>Academic writing: why it’s different from other writing, and how to do it well
>Doing a good project: summary
1. About research
>Research: introduction to the nature of research, and types of research
>Research questions, and the nature of evidence: deciding what type of question to ask, and how to handle the various types of answer
>Mud pits and how to avoid them: things that go wrong
>Isms: necessary assumptions, dubious assumptions, and being caught in crossfire
>Searching the literature: why, where, what for and how
>Research in society – agendas, context and the like: things we take for granted, and things that can cause you trouble
2. Research design
>Types of design: which to use and how to use them
>Surveys and sampling
>Field experiments: doing research in the world
>Controlled experiments: changing things systematically and seeing what happens
>Summary and technical terms
3. Generic advice
>Arranging a study: subjects, equipment, procedures, things to remember, things to beware
>Location and kit
>Handling subjects
>Recording
4. Data collection
>Data collection methods: the methods, and choosing and using the appropriate method
>Reports: getting respondents to talk about how things happen
>Observation: watching what happens
>Card sorts: getting respondents to categorise things
>Laddering: unpacking the respondents’ concepts systematically
>Repertory grids: a systematic representation for respondents’ knowledge
>Interviews: asking people questions
>Face-to-face interactions with respondents: the nuts and bolts of asking questions
>Questionnaires: when to use, when not to use, which questions to ask, what format to use
5. Data analysis
>Content analysis: what is said in a text, how it is said, and how often it’s said
>Discourse analysis: who says what, about what, to whom, in what format
>Knowledge representation: formats, structures and concepts for making sense of knowledge
>Statistics: describing things with numbers, and assessing the odds
>Descriptive statistics: giving a systematic description of the numbers you’ve found
>Measurement theory: types of measurement and their implications
>Inferential statistics: what are the odds against your findings being due to random chance?
Conclusion: the end game
>Writing up: demonstrating your excellence efficiently, and practical points to remember
>References and referencing: using and citing the right texts to demonstrate your excellence
>What next? thinking forward about what you really want your life to be
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