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Quantitative Methods for Researchers

Quantitative Methods for Researchers. Paul Cairns paul.cairns@york.ac.uk. Your objectives. Pretty general! Landscape/area of experiments. My objectives. Importance of experimental method Experiments as evidence Validity Scrutability Statistics as model comparison. Preliminary question?.

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Quantitative Methods for Researchers

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

  2. Your objectives • Pretty general! • Landscape/area of experiments

  3. My objectives • Importance of experimental method • Experiments as evidence • Validity • Scrutability • Statistics as model comparison

  4. Preliminary question?

  5. Experiments as evidence • Randomness removes certainty • Experiments frame data • Without frame, no point

  6. Experimental argument • Theory: X causes Y • Test: change X and measure Y • But: • variation (people, stochastic) • other things affect Y • hard to measure Y • Statistics pierce through the murk!

  7. Theories in computing • Thin on the ground • Name one? • Low relevance to applications • So experiments are pointless?

  8. Experimental Computing • Experiments have own value • Experiments inform theory • Narrative context • We create the objects of study QUAN, Paul Cairns

  9. Experimental argument • Belief: X causes Y • A reason for looking • Try: change X and measure Y • Analyse carefully • Produce evidence

  10. Variables • Independent variable (IV, X) • Dependent variable (DV, Y) • quantitative • Confounding variables

  11. Devising an experiment • Research question (disposable) • One sentence • May use jargon • Answer is “yes/no” but probably “maybe” • Question suggests how to answer it QUAN, Paul Cairns

  12. Revise your research question In groups of three or four, each have a go at a research question. Take turns to explain and be criticised. Be happy to be wrong/stupid. RQs are disposable. QUAN, Paul Cairns

  13. 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

  14. Value? • Modest but cumulative • Opportunity for falsification • Evidence • Isolation of phenomena

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

  16. Write up • Title and abstract • Aims = lit review • Method • Results • Discussion

  17. Why do we do a literature review?

  18. Literature • Previous research • Defines the community • What and who • Implicit standard • Implicit style QUAN, Paul Cairns

  19. Using literature • Importance • Interest • Originality • Insight

  20. What’s the purpose of a write up?

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

  22. Writing as a tool • Necessary headings: • write them! • Before you do the expt • What will sig show? • Is it valid? • Forces a dialogue • With self or supervisor

  23. Fantasy abstract • Write an abstract for your experiment (150-250 words) specifying: • What the question is • Why it is interesting/important • What was done in the experiment • What IV and DV are • What significant results (would) show • What this means

  24. Swap abstracts – “homework” • Do you know what the question is? • Why is it interesting/important? • What is the experimental argument? • Do you believe it? • What would make it better?

  25. Reading • Abelson, Statistics as Principled Argument • Hacking, Representing and Intervening • Cairns, Cox, Research Methods for HCI: chaps 1, 6, 10 • Harris, Designing and reporting experiments in psychology, 3rd edn

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