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Quantitative Methods for Researchers

This resource provides guidance on the importance of structured write-ups in quantitative research, including formal structure, elements like aims, methods, results, and discussion, and key sections like the method write-up. It emphasizes the value of clear titles, structured abstracts, and thorough reporting of all test results. The text discusses experiments as evidence, the interpretation of results, and the ongoing process of refining research methods and communication. Three writing tools are highlighted: Research Questions, Fantasy abstracts, and the Method Write-now approach. Health warnings regarding assumptions, experiment validity, and communicating results effectively are also covered, along with recommended readings for further study in research methods for Human-Computer Interaction (HCI) and statistics.

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Quantitative Methods for Researchers

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  1. Quantitative Methods for Researchers Paul Cairns paul.cairns@york.ac.uk

  2. Objectives • Need for write up • Structure of write-up

  3. Why write up?

  4. Formal structure • Title and abstract • Aims = lit review • Method • Results • Discussion

  5. Literature • Defines the community • Importance/interest • Implicit standard • Implicit style QUAN, Paul Cairns

  6. Method section • Aim and hypothesis • Participants • Variables • Design • Materials • Procedure

  7. Matching Exercise • Aim and hypothesis • Participants • Variables • Design • Materials • Procedure • Construct • Internal • External • Ecological

  8. Results • Report descriptives • Report all tests • not just the “interesting” ones • Don’t do anything else

  9. Discussion • Interpreting results • Honest criticism • Design => Results • Even if not the result you wanted! • Further work

  10. Three writing tools • RQ • Fantasy abstract • Method

  11. Write now! • Known structures • What will sig show? • Is it valid? • Forces a dialogue • With self or supervisor

  12. Experiments as evidence • If • X has really changed • Y has been properly measured • Nothing else has changed • The result was significant • Then • Evidence that X causes Y

  13. Value? • Modest but cumulative • One severe test • Isolation of phenomena • Strong pillars

  14. Not black and white • Experiments are not proof • Validity • Assumptions • Experiments have a frame • Eg speed of gravity

  15. Health warnings • Craft skill • Simpler is better • Doing it • Interpreting it • Communicating it • Experiments as evidence • Software packages are deceptively easy

  16. Q & A • Any question about any aspect • Very general or very specific • Any research method!

  17. Useful Reading • Cairns, Cox, Research Methods for HCI: chaps 6 • Rowntree, Statistics Without Tears • Howell, Fundamental Statistics for the Behavioural Sciences, 6thedn. • Abelson, Statistics as Principled Argument • Silver, The Signal and the Noise

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