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Class 6. Sampling.docx

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Social Sciences
Stephanie Mullen

Sampling Objectives • Know the three Factors in Identifying a Population for a Research Project • Advantages of Sampling. • Learn the three 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. 3 Factors in Identifying a Population for a Research Project • Unit of Analysis - individual MPs • Geographic Location - Canada • Time Period - those serving from1993-2000 • Example: Instead of studying “Members of Parliament,” you would state that you are studying “Canadian MPs between 1993-2000.” Advantages 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 & the population parameters; the sample & 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 fame 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 - This 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 homogeneous 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 our 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 to 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 are selected until the desired sample size is
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