Despite this limitation, a wide range of behavioral science studies conducted within academia, industry and government rely on non-random samples. A non-random sample is one in which every member of the population being studied does not have an equal chance of being selected into the study.īecause non-random samples do not select participants based on probability, it is often difficult to know how well the sample represents the population of interest. When it isn’t possible or practical to gather a random sample, researchers often gather a non-random sample. Nevertheless, there are very good reasons why researchers may want to study people in each of these groups. ![]() There is not, for example, a master list of all the people who use the internet, purchase coffee at Dunkin’, have grieved the death of a parent in the last year, or consider themselves fans of the New York Yankees. However, as you might imagine, it is not always practical or even possible to gather a sampling frame. Once the researcher has a sampling frame, he or she can randomly select people from the list to participate in the study. For example, a database of all landline and cell phone numbers in the U.S. In order to understand a population using random sampling, researchers begin by identifying a sampling frame-a list of all the people in the population the researchers want to study. A population is the group that researchers want to understand. So, everyone who has purchased a Ford in the last five years can be a population and so can registered voters within a state or college students at a city university. Instead, a population can refer to people who share a common quality or characteristic. Importantly, ‘the population being studied’ is not necessarily all the inhabitants of a country or a region. ![]() Random sampling occurs when a researcher ensures every member of the population being studied has an equal chance of being selected to participate in the study. But what does it mean to randomly sample people, and how does a researcher do that? Thanks to this quality of probability, researchers are able to understand large populations by sampling small groups from the population. If each observation is selected randomly, then the sample will naturally reflect the qualities of the population. Each red circle represents an observation, or a person sampled from the population. The blue line represents a normal distribution, also commonly known as a bell curve. ![]() What this means in plain English is that, as long as researchers randomly sample from a population and obtain a sufficiently sized sample, then the sample will contain characteristics that roughly mirror those of the population. At some point during the early 1900s, they discovered that several observations randomly drawn from a population will naturally take on the shape of the population distribution. Glivenko and Cantelli were mathematicians who studied probability. How can researchers accurately understand hundreds of millions of people by gathering data from just a few thousand of them? Your answer comes from Valery Ivanovich Glivenko and Francesco Paolo Cantelli. Now, you may be asking yourself how that works. Even when the population being studied is as large as the U.S.-about 330 million people-researchers often need to sample just a few thousand people in order to understand everyone. So, just like the sample of glazed salmon you eat at Costco or the double chocolate brownie ice cream you taste at the ice cream shop, behavioral scientists often gather data from a small group (a sample) as a way to understand a larger whole (a population). ![]() But what, exactly, is sampling, and how does it work?Īt its core, a research sample is like any other sample: It’s a small piece or part of something that represents a larger whole. In fact, sampling is what the Census Bureau does in order to gather detailed information about the population such as the average household income, the level of education people have, and the kind of work people do for a living. Instead of contacting every person in the population, researchers can answer most questions by sampling people. The entire operation takes years of planning and billions of dollars, which begs the question: Is there a better way? After the data are gathered, they have to be processed, tabulated and reported. household and tries to gather data that will allow each person to be counted. The Census Bureau sends a letter or a worker to every U.S. government conducts a census-a count of every person living in the country-as required by the constitution. What Is the Purpose of Sampling in Research?Įvery ten years, the U.S. Different Use Cases for Online Samplingīy Aaron Moss, PhD, Cheskie Rosenzweig, PhD, & Leib Litman, PhD Online Researcher’s Sampling Guide, Part 1:.The Importance of Knowing Where to Sample.Why is Sampling Important for Researchers?.
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