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Short Answer Questions
1. What are the factors that determine what a goal sample size should be for a study?
When probability sampling techniques are used, sampling error is reduced as the sample size increases. Given that a researcher’s goal is to reduce error, it is therefore preferable to rely on large samples over small ones. Yet larger and larger samples are also more and more expensive samples. To determine the appropriate sample size, we need to consider a number of factors: the homogeneity of the sample, the number of variables under study, and the desired degree of accuracy.
Homogeneity refers to how similar a population is with respect to salient variables, while heterogeneity refers to how dissimilar a population is. A highly homogeneous population allows us to use a smaller sample, but a highly heterogeneous population requires a larger sample. The appropriate sample size increases as we move along the continuum from homogeneity to heterogeneity.
The number of variables we wish to explore also influences sample size. More complex studies require larger sample sizes. The need for a larger sample stems from the desire to look at subgroups within the sample and to impose statistical controls.
A third factor that influences sample size is the desired degree of accuracy. Before conducting an analysis, researchers can state the margin of error they are willing to accept or tolerate. Knowing the margin of error allows researchers to state their sample statistics as a confidence interval.
2. Discuss the factors that influence the representativeness of a sample.
Three important factors influence the representativeness of a sample:
- Accuracy of sampling frame: A sampling frame is a list of all the units in the target population.
- Sample selection method: Sampling techniques can be divided into two categories: probability and non-probability. Probability sampling techniques are based on probability theory and allow researchers to use inferential statistics to test the representativeness of their sample. Non-probability sampling techniques are not based on probability theory, and researchers are not able to use statistical analysis to make inferences from the sample to the larger population of study.
- Sample size: Sampling error is reduced as the sample size increases. Given that their goal is to reduce error, researchers prefer large samples over small ones.