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Lecture 11

Lecture 11 - Sampling
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Department
Political Science
Course
POL2156
Professor
Stephanie Mullen
Semester
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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