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Lecture 12

KINE 2049 Lecture 12: Lecture 12, External validity Errors, November 1st

3 Pages

Kinesiology & Health Science
Course Code
KINE 2049
Merv Mosher

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KINE 2049 F November 1st Lecture 12, External validity Errors Threats to external validity Population and ecological setting. Non representative sample, or lab is too conned and doesnt transfer to everyday life Pretesting sensitization: when the researcher says something like thats interesting, making you think you are unique in some way, changing your results Hawthorne eect: The novelty of you being in an experiment or study makes you perform better. Just because youre involved in the study you feel like you want to do your best because youre excited Over generalizing: you might measure reaction time, and state that people who have good reaction time, also have good movement time. You cant make that generalization because you didnt measure movement time Expectancy (placebo eect): if you believe something is going to work, it is probably going to work whether its actually good or not because that is what you believe Post hoc error: you assume a false cause and eect relationship Example of this: the Japanese eat little fat in their diet, and have less heart attacks than Americans. Italians eat lots of fat and have less heart attacks than Americans. The post hoc error would be saying eat and drink what you want, its speaking English that kills you Reliability: refers to the consistency and repeatability of the data. If i measure a desk one day, it will be the same length another day, so it is reliable validity: is what you measured what you thought you measured? How good was your experiment Reliability anytime you make a measurement there will always be some error any measurement has 2 parts. Xt (true component) and Xe (error component) So the formula is Xo=Xt+Xe Xo is the observed score, Xt id the true score, and Xe is the measurement error To deal with this error, you do test and retest you then nd a correlation between the two tests, to see how reliable your results are The other way you can do this is using the split half method Look at quiz 1 for example You would expect quiz 1 pink and yellow to have the same averages because they are both the same test just rearranged Experiments small sample, big population easiest way to do this is with 2 groups, 1 control and 1 experimental. Referred to as an RCT
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