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Textbook Notes
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Canada
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University of Toronto Scarborough
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Psychology
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PSYB01H3
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David Nussbaum
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Chapter 5

by
OneClass4959

Department

Psychology

Course Code

PSYB01H3

Professor

David Nussbaum

Description

Chapter 5- sampling and survey research
Conducting a census studying the entire population of interest which avoids the problem of sampling
(where only a limited number of people represent the entire population)
If we want to assess the cross-population generaliziability of our findings, we need to compare results
obtained from samples of different populations
Elements example. The students on the list. Who or what we are studying. The elementary units.
this list, from which the elements of the population are selected is termed the sampling frame
representative sample a sample that ‘looks like’ the population from which it was selected in all
respects potentially relevant to the study
sample generaliziability depends on the amount of sampling error- the difference between the
characteristics of a sample and the characteristics of the population from which it was selected
the larger the sampling error, the less representative the sample and thus the less generalizable the
findings obtained from that sample
for example you have 15 people, 5 of them are happy, 10 of them are unhappy. That’s 5 out of 15 people
which is 33% that are happy. You choose a sample of 6 out of this 15. A representative sample of this
would be if you took 2 happy people out of 6. That would also be 33% that is happy. But if you take 4
happy people out of the original population out of the 6 then it will show that 66% are happy which would
become a unrepresentative sample. Of course, representation in a sample is never perfect, but it is
important to provide information about how representative a given sample is
we can calculate the likely amount of sampling error and the tool to do this is called inferential
statistics a mathematical tool for estimating how likely it is that a statistical result based on data from a
random sample is representative of the population from which the sample is assumed to have been
selected
sampling distributions for many statistics, including the mean have a ‘normal’ curve/shape.
so a normal distribution is symmetric (this shape is produced by random sampling error- variation owing
purely to chance
random sampling error/chance sampling error differences between the population and the sample
that are due only to chance factors (random error), not to systematic sampling error.
Random sampling error may or may not result in an unrepresentative sample. This magnitude of sampling
error due to chance factors can be estimated statistically
In a sampling distribution, the most frequent value of the sample statistic—the statistic (such as the
mean) computed from sample data—is identical to the population parameter—the statistic computed for
the entire population
In other words, we can have a lot of confidence that the value at the peak of the bell curve represents the
norm for the entire population
Sample statistic value of a statistic, such as a mean, computed from sample data
Population parameter the value of a statistic, such as a mean computed using the data for the entire
population, a sample statistic is an estimate of a population parameter
The most important distinction that needs to be made about samples is whether they are based on a
probability or a nonprobability sampling method
Sampling methods that allow us to know in advance how likely it is that any element of a population will
be selected for the sample are termed probability sampling methods
You can only make a statistical estimate of a sampling error for a probability based sample Sampling methods that do not let us know in advance the likelihood of selecting each element is termed
nonprobability sampling methods
Probability sampling methods rely on a random, or chance, selection procedure
Probability of selection the likelihood that an element will be selected from the population for
inclusion in the sample
Random sampling in which cases are selected only on the basis of chance, with a haphazard method of
sampling. Where ‘leaving things up the chance’ seems to imply not exerting any control over the sampling
method but to ensure that nothing but chance influences the selection of cases, the researcher must
proceed methodically, leaving nothing to chance except the selection of the cases themselves. The
researcher must follow carefully controlled procedures if a purely random process is to be the result
Probability Sampling Methods
These methods randomly select elements and therefore have no systematic bias; nothing but chance
determines which elements are included in the sample
This feature of probability samples makes them much more desirable than nonprobability samples when
the goal is to generalize to a larger population
It is the number of cases that is most important (don’t make the mistake of thinking that a larger
sample is better because it includes a greater proportion of the population)
The 4 most common methods of drawing random samples are
1. Simple random sampling
2. Systematic random sampling
3. Stratified random sampling
4. Cluster sampling
Simple random sampling requires some procedure that generates numbers or otherwise identifies cases
strictly on the basis of chance
Organizations that conduct phone surveys often draw random samples using another automated
procedure called random digit dialing (a machine dials random numbers within the phone prefixes
corresponding to the area in which the survey is to be conducted)
Systematic random sampling is a variant of simple random sampling
the first element is selected randomly froma list or from sequential files, and then every nth element is
selected
this is a convenient method for drawing a random sample when the population elements are arranged
sequentially, such as in folders in filing cabinets
but you have to watch out for periodicity—that is, the sequence varies in some regular, period pattern
(refer to phone book example from powerpoint)
sampl

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