Sampling and Sampling Methods
There are many research questions we would like to answer that involve populations that are too large to consider learning about every member of the population. How have wages of European workers changed over the past ten years? Questions such as this are important in understanding the world around us, yet it would be impractical, if not impossible, to measure the wages of all European workers. Generally, in answering such questions, social scientists examine a fraction of the possible population of interest, drawing statistical inferences from this fraction. The selection process used to draw such a fraction is known as sampling, while the group contained in the fraction is known as the sample. It is not only statisticians or quantitative researchers that sample. Journalists who select a particular case or particular group of people to highlight in a news story are engaging in a form of sampling. Most of us, in our everyday lives, do some sampling, whether we realize it or not. Although you may not have listened to all the songs of a particular band or singer, you likely would be able to form an opinion about the songs from such artist by hearing a few of them. In making such inferences you've relied on a subset of entities (some songs of an artist) to generalize to a larger group (all songs by an artist). You've sampled. Methods of Sampling
We may then consider different types of probability samples. Although there are a number of different methods that might be used to create a sample, they generally can be grouped into one of two categories: probability samples or non-probability samples. Probability Sampling
The idea behind this type is random selection. More specifically, each sample from the population of interest has a known probability of selection under a given sampling scheme. There are four categories of probability samples described below. Simple Random Sampling
The most widely known type of a random sample is the simple random sample (SRS). This is characterized by the fact that the probability of selection is the same for every case in the population. Simple random sampling is a method of selecting n units from a population of size N such that every possible sample of size an has equal chance of being drawn. An example may make this easier to understand. Imagine you want to carry out a survey of 100 voters in a small town with a population of 1,000 eligible voters. With a town this size, there are "old-fashioned" ways to draw a sample. For example, we could write the names of all voters on a piece of paper, put all pieces of paper into a box and draw 100 tickets at random. You shake the box, draw a piece of paper and set it aside, shake again, draw another, set it aside, etc. until we had 100 slips of paper. These 100 form our sample. And this sample would be drawn through a simple random sampling procedure - at each draw, every name in the box had the same probability of being chosen. In real-world social research, designs that employ simple random sampling are difficult to come by. We can imagine some situations where it might be possible - you want to interview a sample of doctors in a hospital about work conditions. So you get a list of all the physicians that work in the hospital, write their names on a piece of paper, put those pieces of paper in the box, shake and draw. But in most real-world instances it is impossible to list everything on a piece of paper and put it in a box, then randomly draw numbers until desired sample size is reached. There are many reasons why one would choose a different type of probability sample in practice. Example 1
Suppose you were interested in investigating the link between the family of origin and income and your particular interest is in comparing incomes of Hispanic and Non-Hispanic respondents. For statistical reasons, you decide that you need at least 1,000 non-Hispanics and 1,000 Hispanics. Hispanics comprise around 6 or 7%...
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