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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

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Psychology

PSYB01H3

David Nussbaum

Fall

Description

Chapt 5
census: studying the entire population of interest
cross-population generalizability: findings from one popular in one country to
one population in another country
- need to compare results obtained from samples of different populations
population: the entire set of individuals or other entities to which study findings
are to be generalized
elements: the individual members of the population whose characteristics are to be
measured
sampling frame: a list of all elements in a population
representative sample: a sample that “looks like” the population from which it was
selected in all respects potentially relevant to the study. The distribution of
characteristics among the elements of a representative sample is the same as the
distribution of those characteristics among the total population. In an
unrepresentative sample, some characteristics are overrepresented or
underrepresented. Random selection of elements maximizes sample
representativeness
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, the less
generalizable the findings obtained from that sample
inferential statistics (the tool for calculating sampling error): 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
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. The magnitude of sampling error due to chance factors
can be estimated statistically
sample statistic: the statistic computed from sample data (i.e mean) is identical to
the population parameter: the statistic computed for the entire population
- the value at the peak of the bell curve represents the norm for the entire
population
- population parameter also may be termed true value for the statistic in that
population - a sample statistic is an estimate of a population parameter
Probability of selection: the likelihood that an element will be selected from the
population for inclusion in the sample. As the size of the sample decreases as a
proportion of the population, so does the probability of selection
- rely on a random, or chance, selection procedure -> flipping a coin to decide which
of the two people wins and which one loses. Heads and tails are equally likely to
turn up in a coin toss -> equal chance of winning. That chance is their probability of
selection
PROBABILITY SAMPLING METHODS: lets us know in advance how likely it is
that any element of population will be selected for the sample
1. Simple random sampling: requires some procedure that generates numbers of
otherwise identifies cases strictly on the basis of chance
- random-digit dialing: a machine dials random numbers within the phone prefixes
corresponding to the area in which the survey is to be conducted
2. Systematic random sampling: a variant of simple random sampling
- the first element is selected randomly from a list or from sequential files, and then
every nth element is selected
- one problem to watch out for is periodicity: the sequence varies in some regular
periodic pattern
3. Stratified random sampling: uses info known about the total population prior
to sampling to make the sampling process more efficient
proportionate stratified random sampling: ensures that the sample is selected so
that the distribution of characteristics in the sample matches the distribution of the
corresponding characteristics in the population
disproportionate stratified random sampling: calculates separate statistical
estimates
4. Cluster sampling: used when a sampling frame of elements is not available as
often is the case for large populations spread out across a wide geographic area or
among many different organizations
cluster: naturally occurring, mixed aggregate of elements of the population
ex. schools could serve as clusters for sampling students, blocks could serve as
clusters for sampling city residents, countries could serve as clusters for sampling
the general population and businesses could serve as clusters for sampling
employees
NON PROBABILITY SAMPLING METHODS: methods that do not let us know in
advance the likelihood of selecting each element
- does not use random selection procedure
- elements are selected for availability sampling b/c they’re available or easy to
find (also known as haphazard, accidental or convenience sampling) ex. inviting interested students to do a questionnaire, including a survey inside a
magazine (with the plea to subscribers to complete and return it)
1. Quota sampling
- is intended to overcome the most obvious flaw of availability sampling that the
sample will just consist of whoever and whatever is available without any concern
for its similarity to the population of interest
- are set to ensure that the sample represents certain characteristics in proportion
to their prevalence in the population
Differences between stratified and quota sampling methods
stratified:
- unbiased (random) selection of cases
- sampling frame required
- ensures representation of key strata
quota:
- biased selection of cases
- does not require sampling frame
- ensures representation of key strata
WRITING SURVEY QUESTIONS
When writing survey questions, avoid using double negatives
ex. “Do you disagree that rich people are not happy?”
- avoid using negative words like “don’t” and “not”
- double barreled questions also guarantee uninterpretable results b/c they
actually ask two questions but allow one answer
(only allowing “yes” and “no” answers when some respondents may want to agree
with both answers)
- filter questions creates skip

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