Sampling and Sampling Methods
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
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