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Political Science
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POL2156
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Stephanie Mullen
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Lecture 11

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Political Science

POL2156

Stephanie Mullen

Fall

Description

Nov. 27, 2013
Sampling
Objectives
Know the 3 Factors in Identifying a Population for a research project
Advantages of sampling
Learn the 3 factors influencing the representativeness of a sample
Definitions
Population – the group that we wish to generalize about
Sample – can’t study everyone in the population sometimes; therefore we select a
smaller group (sample) that is representative of the population under study and from
the statistical analysis on this sample, we can make generalizations about the
population as a whole.
Three Factors in Identifying a Population for a Research Project
Unit of Analysis – individual MPs
Geographic Location – Canada
Time Period – those serving from 1993-2000
Example: Instead of studying “Members of Parliament,” you would state “Canadian MPs
between 1993-2000”
Advantage of Sampling
Efficient
Less expensive
Restricted to a certain time frame
Less data collection & entry
Sampling can provide accurate estimates of the population parameters
Note: we are ultimately interested in the population and the population parameters;
the sample and the sample statistics are merely a means to these ends
Representativeness of the Sample
Three factors influencing the representativeness of a sample:
1) the accuracy of the sampling frame
2) the sample size
3) the method by which the sample is selected All three factors are important – a weakness with respect to one cannot be
compensated by strength with respect to another
Sample Frame
This is simply a list of all the units in the target population. If our target population is
Canadian MPs serving from 1993-2000, our sample frame would include all MP who
were in Parliament during this time period.
For this type of population it is not as hard to get everyone compared to a national
opinion research population.
Some problems even with this small MP population: not all MPs would be willing, not all
alive, might not find some if they were defeated or resigned, might forget to include
MPs who won during by-elections, etc.
The challenge is to find a sampling frame that minimizes inaccuracies in the sample
frame – one way is random sampling.
Not all target populations have a population with every person listed with contact
information
Sample Size
Rule of Thumb: Sample statistics are more likely to be closer to the population
parameter when the sample size is larger than when the sample is small.
Our goal is to reduce error, therefore we prefer larger samples
To determine the appropriate sample size, we need to consider a number of factors
1 – the homogeneity of the sample
2- the number of variables under study
3 - the desired degree of accuracy
4 – the method of random sampling used
1 – Homogeneity of the Sample
Refers to how similar a population is with respect to the variable of interest. (If all our
Canadian MPs who served from 1993-2000 had the exact same opinions on a topic, we
would not need a large sample).
Heterogeneity refers to how dissimilar a population is with respect to the variable of
interest
We want to estimate how homogenous or heterogeneous our population is – a highly
homogeneous population allows us to use a smaller sample, whereas a highly
heterogeneous population requires a larger sample. The appropriate sample size increases as we move along the continuum from
homogeneity to heterogeneity
2 – Number of Variables under Study
The more complex the study, the more variables and relationships that we include, the
more cases we need in our sample
The need for a larger sample stems from the desire to look at the subgroups within the
sample and to impose statistical controls
If we want to look at visible minority MPs, then our sample would have ot be larger in
order to include more non-white MPs.
3 – Desired Degree of Accuracy
Researcher can state the margin of error that they are willing to accept
Knowing the margin of error allows researchers to state their sample statistics as a
confidence interval
4 – Method of Sample Selection in Mostly Quantitative Statistics
Probability sampling can be conducted in several ways, the three most common are:
(1) Simple Random Sample
(2) Stratified Sample
(3) Cluster Sample
Error varies with the different probability sampling approaches
Stratified sampling is more precise than simple random sampling
Cluster sampling is less precise than simple random sampling
Simple Random Sampling
All the cases are listed and assigned numbers. Through computer selection or by use of
a table of random numbers, cases a

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