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Techniques expérimentelles 2

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Techniques expérimentelles 2

Barbara Hemforth

Most of thisisstolenfrom a lecture by Chuck Clifton

http://webcache.googleusercontent.com/search?q=cache:nPJ6GhwZ2NkJ:people.umass.edu/cec/Experimental%2520Design%2520for%2520Linguists.ppt+Clifton+experiments+linguists&cd=5&hl=fr&ct=clnk&gl=fr&client=safari&source=www.google.fr

- Dictum 1: Formulate your question clearly
- Dictum 2: Keep everything constant that you don’t want to vary
- Dictum 3: Know how to deal with unavoidable extraneous variability
- Dictum 4: Have enough power in your experiment
- Dictum 5: Pay attention to your data, not just your statistical tests

C. Clifton Jr

- Independent variable: variation controlled be experimenter, not by what subject does
- Dependent variable: variation observed in subject’s behavior, perhaps dependent on IV
- Operationalization of variables

C. Clifton Jr

- Try to hold extraneous variables constant through norms, pretests, corpora…
- When you can’t hold them constant, make sure they are not associated (confounded) with your IV

- Subdictum B: When in doubt, randomize
- Random assignment of subjects to conditions
- Questionnaire: order of presentation of items?
- Single randomization: problems
- Different randomization for each subject
- Constrained randomizations

- Equate confounds by balancing and counterbalancing
- Alternative to random assignment of subject to conditions: match squads of subjects

- Counterbalancing
- Ensure that each item is tested equally often in each condition.
- Ensure that each subject receives an equal number of items in each condition.

- Why is it necessary?
- Since items and subjects may differ in ways that affect your DV, you can’t have some items (or subjects) contribute more to one level of your IV than another level.

- If you can test each subject on each item in each condition, life is sweet
- E.g., Ganong effect (identification of consonant in context)
- Vary VOT in 8 5-ms steps
- /dais/ - /tais/
- /daip/ - /taip/

- Classify initial segment as /d/ or /t/
- Present each of the 80 items to each subject 10 times
- Ganong effect: biased toward /t/ in “type,” /d/ in “dice”

- Vary VOT in 8 5-ms steps

Connine, C. M., & Clifton, C., Jr. (1987). Interactive use of information in speech perception. Journal of Experimental Psychology: Human Perception and Performance, 13, 291-299.

- Simple example
- Questionnaire, 2 conditions, N items
- Need 2 versions, each with N items, N/2 in condition 1, remaining half in condition 2
- Versions 1 and 2, opposite assignment of items to conditions

- More general version
- M conditions, need some multiple of M items, and need M different versions
- Embarrassing if you have 15 items, 4 conditions…
- That means that some subjects contributed more to some conditions than others did; bad, if there are true differences among subjects

- M conditions, need some multiple of M items, and need M different versions

- Order of testing
- Don’t test all Ss in one condition, then the next condition…
- At least, cycle through all combinations of conditions (all lists) before testing a second subject with the same list
- Fancier, latin square
- Avoid minor confound if always test cond 1 before cond 2 etc.
- N x n square, sequence x squad, containing condition numbers, such that each condition occurs once in each column, each order

- Location of testing
- E.g., 2 experiment stations

Latin square of order 2Latin square of order 3

abx y z

b az x y

y z x

- A latin square of order n is an n by n array of n symbols in which every symbol occurs exactly once in each row and column of the array.

- Systematic variance: variability due to manipulation of IV and other variables you can identify
- Random variance: variability whose origin you’re ignorant of
- Point of inferential statistics: is there really variability associated with IV, on top of other variability?
- Is there a signal in the noise?

- Keep everything constant
- Reduce experimental noise
- See the signal easier

- Keep environment, instructions, distractions, experimenter, response manipulanda, etc. constant
- Pretest subjects and select homogeneous ones, if that suits your purposes

- Reduce experimental noise

- Subjects differ
- …a lot, in some measures, eg. Reading speed, reaction time

- Present all levels of your IV to each subject
- Assume the subject effect is a constant across all the levels.
- Differences among conditions thus abstracted from subject differences

- Counterbalancing necessary
- Test each item in each condition for an equal number of subjects.

- Worry about experience changing what your subject did
- E.g., will reading an unreduced relative clause (The horse that was raced past the barn fell) affect reading of a reduced relative clause sentence?

- Add more data!
- Minimizes noise component of differences among condition means

- Law of large numbers
- The larger the sample size, the more probable it is that the sample mean comes arbitrarily close to the population mean
- If you’re (almost) looking at population means, any differences have to be real – not sampling error

- Look at your data, graph them, try to make sense out of them
- Don’t just look for p < .05!

- Examine confidence intervals
- Look at your data distributions
- Stem and leaf graphs
- By subjects…

http://www.cis.rit.edu/people/faculty/montag/vandplite/pages/chap_6/ch6p10.html

http://www.cis.rit.edu/people/faculty/montag/vandplite/pages/chap_6/ch6p10.html

http://www.cis.rit.edu/people/faculty/montag/vandplite/pages/chap_6/ch6p10.html

Which man did you wonder when to meet?

Assign an arbitrary number to that item, greater than zero.

Now, for each of the following items, assign a number. If the item is better than the first one, use a larger number; if it’s worse, smaller. Make the item proportional to how much better or worse the item is than the original – if twice as good, make the number 2x the start; if 1/3 as good, make the number 1/3 as big as the start.

- Which man did you wonder when to meet?
- Assign an arbitrary number, greater than 0, to this first item.
- Now, for each successive item, assign a number – bigger if the item is better, smaller if worse, and proportional – if the item is 2x as good, make the number 2x the original; if ¼ as good, make the number ¼ as big as the original.

- Which book would you recommend reading?
- When do you know the man whom Mary invited?
- This is a paper that we need someone who understands.
- With which pen do you wonder when to write.
- Who did Bill buy the car to please?

Bard, E. G., Robertson, D., & Sorace, A. (1996). Magnitude estimation of linguistic acceptability. Language, 72.