Difference in Terms of Representatives of the Sample.

September 26, 2019

The difference in Terms of Representatives of the Sample
What is a Representative Sample?
If you are conducting research on a specific population, you will want to make sure that your sample of that population is representative. If your sample is representative of your population, you will be able to confidently generalize the results of your study to that population. But what exactly does that mean? During the early phases of a survey project, a common question is, “How do I get responses from every person I survey? The answer is, usually, you don’t need to. Unless you’re working with a very small group, the vast majority of the time, you only need to get responses from a smaller portion of the population. What matters is getting a good, representative sample of your population.

First, let’s review the difference between your population and your sample, as many students often get these terms confused. Your sample is a group of individuals who participate in your study. These are the individuals that provide the data for your study. Your population is the broader group of people that you are trying to generalize your results to. A representative sample is one that accurately represents, reflects, or “is like” your population. A representative sample should be an unbiased reflection of what the population is like. There are many ways to evaluate representativeness gender, age, socioeconomic status, profession, education, chronic illness, even personality or pet ownership. 

So, if most shark biologists in the population are women, but your sample is all-male, you do not have a good case for representativeness because your sample does not share the same characteristics as the larger population. In this case, you cannot generalize the results of your study to the population. Lack of representativeness often comes from sampling errors or biases. 

So, how do you avoid sampling error and select a representative sample? First, thoughtfully consider your sampling frame (your possible participants) and recruitment procedures. Avoid only recruiting members of a certain subset of your population, like the fraternity members or vegan café-goers in the above examples. Next, a good way to reduce bias in sampling is to randomly sample from your sample frame. Through this, you minimize any selection biases that might occur, such as volunteer bias. You also can implement a stratification protocol, such as proportionate stratified sampling. 
Sample Size vs. Representative Samples
Your target sample size is how many people you need to reach to derive accurate insights from your study. Larger sample size should hypothetically lead to more accurate or representative results, but when it comes to surveying large populations, bigger isn’t always better. In fact, trying to collect results from a larger sample size can add costs – without significantly improving your results.

Before you get too far into a sample size, take a moment to consider representative samples, too. They are two related, but different issues. The sheer size of a sample does not guarantee its ability to accurately represent a target population. Large unrepresentative samples can perform as badly as small unrepresentative samples.

A survey sample’s ability to represent a population is much more closely related to the sampling frame (the list from which the sample is selected) than it is to the sample size. When some parts of the target population are not included in the sampled population, we are faced with selection bias, which prevents us from claiming that the sample is representative of the target population.
Avoiding Selection Bias in Your Survey Responses. When not every member of your target population has an equal chance of being chosen to take your survey, you’re at risk of polluting your data with selection bias.
There are four common ways that this occurs:
Bias Through Convenience Samples
Convenience samples are just what they sound like: choosing respondents that we can conveniently reach without regard to their demographic data. These samples include respondents who are easier to select or who are most likely to respond; they will not be representative of harder-to-select individuals. Samples from online panels are a good example of convenience samples. These panels are composed of individuals who have expressed interest in participating in surveys, leaving out individuals who may be part of the target population but are not available for interviews through the panel.
Selection Bias Via Undercoverage
Undercoverage happens when we fail to include all of the target population in the sampling frame.
Many online panels work hard at avoiding undercoverage bias, but the fact remains that certain demographics are underrepresented in panels.
Nonresponse and Selection Bias
Selection bias also happens when we fail to obtain responses from everyone in the selected sample. Nonrespondents tend to differ from respondents, so their absence in the final sample makes it difficult to generalize the results to the overall target population. This is why the design of a survey is far more important than the absolute sample size to get a representative sample of the target population.
Final Three Sources of Sample Bias
Three other common ways that sample bias can creep into a survey are:
Judgment Sample: This is a sample selected based on “representative” criteria that are chosen based on prior knowledge of the topic or target population. An example would be a study looking for a sample of teenagers, and trying to intercept them at a cross-section near a high school. So, when it comes to getting a representative sample, the sample source is more important than the sample size.

If you want a representative sample of a particular population, you need to ensure that: The sample source includes all the target population
The selected data collection method (online, phone, paper, in-person) can reach individuals that represent that target population The screening criteria truly reflect the target population and do not inadvertently exclude valuable subpopulations. You can minimize nonresponse bias with good survey design, incentives, and the appropriate distribution method. There are quality controls in place during the data collection process to guarantee that designated members of the sample are reached.

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