WEBVTT
WEBVTT
Kind: captions
Language: en
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Reliability is one way that the randomness
can influence your study results.
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But it is not the only way and therefore it
is important to understand how different sources
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of error are related and how they differ.
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Reliability and sampling error are two main
sources of random error.
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So reliability error is considered measures
of individuals.
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How much your individual measures vary from
one measurement occasion to another?
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Sampling error on the other hand is another
kind of process that produces random differences
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to your study results.
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So sampling error refers to the composition
of the sample and how it varies from one random
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sample to another.
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So the idea here is that reliability is measure
of the same individuals - how much they vary
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from one study to another if we do the same
measurement producers.
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Sampling error refers to how much different
repeated random samples on the same population
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influence the study results.
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Then definition for reliability is how much
measure of trait of individual varies over
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measurement occasions.
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For sampling error how much statistical estimation
varies over repeated samples because different
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samples contains different individuals.
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So this again is a bit different.
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Causes of reliability and sampling error are
also different.
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Reliability is related the quality of your
measurement instrument.
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It's not related to sample size.
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And sampling error on the other hand is related
to sample size and population heterogeneity
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only if you have random sample.
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Consequences of these two sources of error
are also different.
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So reliability if you have unreliable measures
then statistical associations using those
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unreliable measures will be inconsistent and
biased unless you explicitly take reliability
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into account in your statistical models which
can be a bit complicated to do.
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Sampling error on the other hand simply influences
the efficiency of estimates.
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If you have a high sapling error then your
results could still be unbiased.
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They're just less precise.
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So these two sources of random error have
different - very different - consequences.
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Sampling error is easy to reduce by increasing
sample size.
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To reduce unreliability you have to improve
your measurement practices.
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Let's take a look at the bathroom scale example.
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So reliability here is whether...
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Sampling error is whether these real weights
here - these people here - are an accurate
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presentation of the population.
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So is this a representative sample?
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That's what the sampling error is about.
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If we happen to take shorter people than on
average on the population by chance to our
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sample then we will have larger sampling error
than we would have if our people in the sample
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are closer to the population mean.
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Then reliability is whether these real weights
and the bathroom scale readings actually agree.
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So assuming complete perfect validity there
would be no other measurement.
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So these are two different quantitative.
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Is a sample representative?
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That's the sampling error.
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Are measures representative of these actual
values?
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How closely they match that's the question
of reliability.
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Assuming perfect validity.
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We can also take a look at this through the
hierarchy of reliability and validity.
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So reliability is whether you have the same
sample measured again do you get the same
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result.
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Sampling error goes to the statistical conclusion
about validity and is about if you have a
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new sample from the same population do you
get the same result.
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If you don't get the same result from a new
sample then either you have reliability problem
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or you have sampling error issue.
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If you have a different result from the same
sample then it's a reliability issue.
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Then finally we have the external validity
which is about new population.
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So if our results don't generalize if we repeat
the study with the new population and we don't
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get the same result then if we have ruled
out sampling error we have ruled out unreliability
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then it's a issue of external validity or
generalizability.
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So these are different sources of random error
or why your measurement results could be different
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from one study to another.
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And it's important to understand what the
differences are because they are sometimes
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confusing.