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Quantitative Methods for Researchers

This resource provides insights into the fundamental principles of quantitative methods in research, emphasizing the significance of experimental design and the role experiments play in providing evidence for causal relationships. It explores key concepts like validity, scrutability, and the impact of randomness on certainty. The guide outlines how to formulate research questions and hypotheses, design experiments, and effectively analyze results. With practical examples and methodological considerations, this document is essential for researchers looking to enhance their quantitative skills and understanding of experimental computing.

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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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