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Quantitative Methods for Researchers. Paul Cairns [email protected]. Objectives. Statistical argument Comparison of distributions A fly-by of approaches. How are the abstracts?. Questions? Problems? Restarts?. Statistical Argument. Inference is an argument form - PowerPoint PPT Presentation
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Objectives
Statistical argument Comparison of distributions A fly-by of approaches
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How are the abstracts?
Questions? Problems? Restarts?
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Statistical Argument
Inference is an argument form Prediction is essential– Alternative hypothesis– “X causes Y”
No prediction – measuring noise
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Gold standard argument
1. Collect data2. Data variation could be chance
(null)3. Predict the variations
(alternative)4. Statistics give probabilities5. Unlikely predictions “prove” your
case
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Implications
Must have an alt hyp No multiple testing No post hoc analysis Need multiple experiments
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Silver standard argument
1. Collect data2. Data variations could be chance
(null)3. Are there “real” patterns in the
data?4. Use statistics to suggest
(unlikely) patterns5. Follow up findings with gold
standard work
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Fishing: This is bad science
1. Collect lots of data– DVs and IVs
2. Data variations could be chance3. Test until a significant result
appears4. Report the tests that were
significant5. Claim the result is important
Statistical inference
Model comparison:– Single distribution (null)– Multiple distributions (alternative)
From samples, which model is better?
From samples, is null likely?
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What terms do you know?
The statistical zoo!
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Choosing a test
What’s the data type? Do you know the distribution? Within or between What are you looking for?
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Distributions
Theoretical stance Must have this! Not inferred from samples
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Parametric tests
Normal distribution Two parameters Null = one underlying normal
distribution Differences in location (mean)
t-test models
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t-test
Two samples Two means Are means showing natural
variation? Compare difference to natural
variation
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set AB
Effect size
How interesting is the difference?– 2s difference in timings – Significance is not same as
importance Cohen’s d
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sd AB
ANOVA
Parametric Multiple groups Why not do pairwise comparison? Get an F value Follow up tests
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ANOVA++
Multiple IV– So more F values!
Within and between Effect size, η2
– Amount of variance predicted by IV
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Non-parametric tests
Unknown underlying distribution Heterogeneity of variance Non-interval data Usually test location Effect size is tricky!
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Wilcoxon test
See sheet
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Seeing location
Boxplots Median, IQR, “Range” Outliers
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Multivariate
Multiple DV Multivariate normal distribution– Normal no matter how you slice
MANOVA Null = one underlying (mv)
normal distribution
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Issues
Sample size Assumptions Interpretation Communication
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Your abstract
What sort of data will you produce?
Can you theorise about the distribution?
What sort of test do you think you will need?
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Health warnings
Craft skill Simpler is better– Doing it – Interpreting it– Communicating it
Experiments as evidence Software packages are deceptively
easy27
Q & A
Any question about any aspect Very general or very specific Any research method!
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Useful Reading
Cairns, Cox, Research Methods for HCI: chaps 6
Rowntree, Statistics Without Tears Howell, Fundamental Statistics for
the Behavioural Sciences, 6th edn. Abelson, Statistics as Principled
Argument Silver, The Signal and the Noise
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Monte Carlo
Process but not distribution Generate a really large sample Compare to your sample Still theoretically driven!
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Example
Event = 4 heads in a row from a set of 20 flips of a coin
You have sample of 30 sets 18 events How likely?– Get flipping!
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