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Chapter 5

# Chapter 5

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University of Toronto Scarborough

Psychology

PSYB01H3

David Nussbaum

Fall

Description

Chapter 5: Sampling and Survey Research
Substantive Theme: Happiness
Selecting Research Participants
Sampling: selection of individuals or other entities to represent a larger population of interest
Census: study entire population; expensive and time consuming
Sample Planning
Define the Population
Varies depending on population ex. Students disable elderly and adults report similar as do men and
women but there are cultural differences
Cross-population generalizability: need to compare results obtained from samples of different
populations
Define Sample Components
Elements: units of a population
Sampling frame: the list from which the elements of a population are selected
Population: entire set of individuals or other entities to which study findings are to be generalized
Representative sample: sample that looks like the population from which it was selected in all
respects relevant to the study
Sampling error: the differences between the characteristics of a sample and the characteristics of a
population from which it was selected
Estimating sampling error
o Inferential statistics: 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 was selected
o
Sampling distribution: graph of mean values for all samples; is normal
Mean is equal to population parameter
o Random sampling error: variation owing to pure chance not systematic sampling error; causes
bell shape; may or may not result in unrepresentative sample
o
Sample statistic: the value of a statistic ex mean computed from sample data
o Population parameter: value of a statistic computed using data for the entire population
o Confidence intervals and limits: more random samples more confidence smaller interval
Sampling Methods
Probability sampling methods: 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
Nonprobability sampling methods: sampling methods that do not let us know the likelihood of
selecting each element
Probability of selection: the likelihood that an element will be selected from the population into the
sample; sample size/population size
Random sampling: cases are selected only on the basis of chance
Probability Sampling Methods No systematic bias; nothing but chance determines elements included
Number of cases is more important than proportion of population that sample is
Simple Random Sampling: probability for each item is the same; sample size/ population size; ex
random number generator, random digit dialling
Systematic random sampling: first element is random then every nth element is selected
o
Watch out for periodicity ex. Houses on a block in sampling interval
Stratified Random Sampling: separate into groups/strata based on relevant characteristic so same
in strata them randomly sample from each strata; ensures appropriate representation of elements
o Proportionate: sample is selected so distribution of characteristic in sample matches
distribution in population
o Disproportionate:
Cluster Sampling: randomly choose from naturally occurring clusters and then random sampling
within those clusters
Nonprobability Sampling Methods
Availability sampling: dont know if representative; take whoever is available or easy to find;
haphazard, accidental or convenience
Quota Sampling: set quotas to ensure that the sample represents certain characteristics in
proportion to how they would be in the population; no random sampling
Writing Survey Questions
Avoid confusing phrasing
o shorter words and sentences (<20 words)
o Avo

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