When you want to survey a sample, you don’t want to just get information from the people in the sample. You want information that can be considered as an extrapolation of the large population. Sampling errors occur if the sample population is not in line with the general population. And that’s exactly what we’ll cover here.
In this article, we will learn about:
What are sampling errors?
A sampling error is a skewed result when the analyst fails to include a sample that is not representative of the entire population of data. Sampling is performed by selecting a number of observations from a population set. The method that you select can produce both sampling and non-sampling errors.
Having the know-how about the sampling errors in your study is important as it can be an indicator of the level of confidence in the results. If the sampling errors are not considered, it can lead to poor decisions by the management that can negatively affect the business that is using the research.
What causes a sampling error?
When the researcher takes a random sample instead of taking into account every single individual in the population, sampling error occurs. The samples selected should be done carefully. Most sampling errors occur because these samples are representative ones, a small group that you assume is similar to the population you want to research. Why? Because it is impossible to survey everyone who is a part of the population under consideration.
Sampling errors vs Non-sampling errors
When you are collecting statistical data, there are a number of errors that you will encounter. Sampling errors are just the random differences that you will see between the sample population and that of the general population that you wanted to research in the first place. Sampling errors arise because the sample sizes are usually limited, and there is no way you can survey everyone.
Non-sampling errors are those that you will find during data collection that cause the data to differ from the true values significantly. They are usually a result of human errors during the course of the data collection.
Methods of reducing sampling errors
Identifying sample errors is a process that should be done methodically, and it is not difficult. Here’s how you can reduce sampling errors.
1. Audience panel
An audience panel is a set of pre-recruited individuals representing your target population. They voluntarily agree to participate in surveys or studies. This can significantly reduce sampling error and improve the reliability of your findings.
A well-balanced panel reduces the need for excessive weighting correction. Moreover, a diverse set of individuals also reflects the demographics and characteristics of your target audience. It will also help you to conduct longitudinal studies and track changes in opinions and behaviors over time.
It is extremely important to have a trusted panel to back you up. You can consider using SurveySparrow’s audience panel if you wish. You can easily find your strengths, spot blind spots, and make smarter decisions. The panel covers 149+ countries, offering diverse demographics tailored to your survey needs.
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Now, where were we? Yes, the next important solution is to…
2. Increase sample size
This can be seen as an add-on to the first point. An audience panel will help here as well.
Both the quality and the quantity of the considered population are important. You can increase the sample size by adding more ideas and inputs, all the while making sure that the sample population is still representative of the entire population. Larger samples will give effective results as the researchers will come closer to the true population size.
3. Random Sampling
When you use random sampling methods, you end up eliminating bias in selecting respondents for your sample population. It provides a great chance for every individual from the target population. Instead of choosing participants for the survey in a disorganized manner, you can select individuals whose name starts with B, C, or S.
4. Segment the population:
While choosing a random sample is great, you can also create and test groups based on the population size. Let’s say that people from a certain demographic represent 25% of the population. Now, you have to ensure that they are a sizeable part of your sample population. This is where segmenting the population based on similar characteristics becomes important.
You can do this with the help of stratified random sampling, a type of probability sampling. In this sampling method, you need to divide the population into homogenous subgroups that are known as strata. Doing so ensures that the sample group has a composition that is similar to the target population that is representative of it.
5. Understand your population well
To reduce your sampling errors, you have to possess a great deal of understanding of your people and become aware of the demographic mix it possesses. Who are the people that predominantly use your product? Why do they use your product? Why do some choose your competitor? Answers to these questions will also help reduce sampling errors.
6. Use multiple data sources
To collect data, you can use more than one source. In fact, that will increase the veracity of the data as it is coming from different sources. When you are borrowing data that has been published by researchers, the insights that you will get from it will be even more accurate.
7. Better sample design
Improve your sample design by considering different sub-populations within the population you are targeting.
8. Replicate the study
Instead of relying on a single study, researchers can reduce sampling errors by replicating the study a number of times. Take the same measurements repeatedly, but use more than one subject. For example, multiple studies or multiple groups for consideration.
9. Better training
You can train your team to perform better research so that sampling errors can be reduced. They could be armed with better tools, sharper strategies, and the necessary wherewithal to get things done.
Types of Sampling Errors
Let us look at the different types of sampling errors and how you can prevent each of them from surfacing in your research.
1. Sample Frame Error
This type of error occurs when the sample is selected from the wrong population data. In cases such as this, the sample frame is not representative of the population of interest that the researcher wants to sample. The sample frame error includes targeting the wrong population segments or missing out on a variety of demographics, even though they could be within the segments considered.
How to avoid sample frame errors?
If you want to conduct a huge survey by email, and if the sample frame is taken from all the email companies, then those who do not use emails will miss out.
So, what do you do in such a scenario?
You have to conduct a small pre-survey to see if your sampling method is correct and you are not committing a sampling frame error.
2. Selection Error
When participants opt out of a study, you will only end up with information from those who are interested to participate in the survey. If the researchers end up overlooking the respondents who didn’t respond initially, the study might not be reflective of the target market. For the outcome to change, the researchers need to follow up with respondents who didn’t show interest to participate in the study.
How to avoid selection errors?
The best way to avoid selection error is to employ randomization. Also, you can go the extra mile by persuading the non-participants to respond. You can increase the participation rate by planning for the survey in advance and educating the respondents about the importance of the survey. Taking regular follow-ups and creating an attractive survey design also helps. In-person interviews are even more successful though it can be difficult to convince people.
3. Non-Response Error
If you fail to get responses from every unit in the selected sample group, then it is called a non-response error. When there is a decrease in the sample size or even in the amount of information that is collected, it will result in a larger standard error. There is also an introduction of bias since the non-respondents might have differing opinions from the respondents who are a part of the selected sample.
There could be a number of reasons attributed to this type of error. One example could be that the non-respondents might not be in a position to use the channel in which the survey was conducted. The survey might not have been conducted in a language of their choice. Even though the reasons could be minor, they will have a great impact on the final outcome.
How do avoid non-response errors?
The magnitude of the non-response error can be avoided by employing follow-up surveys to ensure that you get a response. Alternatively, ensure that your target population is adequately responded to with the help of alternate responders.
4. Population Specific Error
This type of error is caused when the researcher does not have a clear idea of who they want to include in the survey. This error also happens when there is not a deep understanding of the target population. If the researcher is not confident about the defined target population, they could end up selecting inappropriate elements in their sample population. The population-specific error is caused by a lack of knowledge of the groups that would be most relevant to the study.
How do avoid population-specific errors?
Let’s assume that the surveyor selects a sample that includes people from the ages of 15 to 35 for a subscription platform. Many of the people in this category won’t have the purchasing power to buy the subscription platform. But if you consider only the ones who can afford the subscription, you will realize that some of them will not be using the service at all.
In this scenario, you need to be aware of the objective and make the choice of the population accordingly. Since the survey is specific, you might as well consider only questions to the age group that is users of the subscription platform.
5. Sampling Error
The errors that are a result of the variations in the number or representativeness of the sample that responds to the survey is called sampling error. It usually happens when the researcher does not plan the sample carefully.
How to avoid sampling error?
If the researcher cares to create a careful sample design, sampling errors can be controlled and eliminated to a large extent. Another method to reduce sampling errors is to have a large sample to reflect the entire population. Businesses can even use online samples to collect responses to reduce the chances of sampling errors.
6. Undercoverage bias:
This type of sampling bias usually occurs when a part of data/information is not taken into account. For example, assume that you’re surveying a group of people who are working in IT. Some people might be working in the sector for the past 10 years, some for the past 5, and some for just 2 years. If this data is unaccounted for, then that type of sampling bias is referred to as undercoverage bias.
Important things surveyors should keep in mind about their sample
Surveyors, aka researchers, should be careful about how they identify and select a group from the population. It is pivotal that they can be considered representative of the entire population. Do remember that researchers are not interested in the individuals responding to the survey, instead, it is about what they can infer to be the characteristics of the population.
Here are a few things that they should be mindful of…
Being transparent:
There are a number of factors that determine the structure and size of the population. Researchers have to discuss these limitations and maintain transparency with respect to the methods they followed to select the sample so that the results can be considered to be right.
Diversity:
The respondents in the survey should not be of the same characteristics. For a sample to be truly representative, the selected set of people should represent the diversity that is inherent within the population.
Regularity:
The survey respondents should be checked for consistency. To make that happen, it is best to have a test survey to see how they respond. You can compare the individuals of the sample with the whole to see if they represent the parent population’s traits.
Examples of sampling errors
If company ABC wants to find out how much is the viewership of a program that airs at 12 PM on weekdays, then sampling is an excellent way to find the results. The company needs to determine who are the various type of viewers. You have to consider attributes such as age, gender, job, location, etc.
Stay-at-home dads and moms, freelancers working from home, and students will be a significant contributors to the viewership. You need to draw the sample taking all of this into consideration so that it represents the real population. Statistical analysts use analytical methods to find out the variation in results caused by sampling errors.
FAQs
How to control sampling error?
Statistical theories have a great role in controlling the probability of sampling errors. Most researchers use it to control the errors in the sample size and population. That’s why the size of the sampling error is proportional to the sample size that the researcher has taken under consideration.
If the sample size is huge, then there will be fewer errors, on the other hand, if the sample size is small, then the rate of error will be high. To evaluate the range of the error, researchers employ a metric referred to as “margin of error.” For the output to be accepted, the confidence level is expected to be 95% and above.
How to estimate sampling errors?
The margin of errors that researchers see in their survey results is because of sampling error. Here’s the formula to calculate sampling error:
Sampling error= Z x (σ/n)
Z = It is the score value based on the confidence interval (~1.96)
σ = Population standard deviation
n = Size of the sample under consideration
How to dodge the sampling errors?
Sampling errors are inevitable, but as we mentioned earlier, you can always reduce the size of the error. For that, you need to ensure that the survey campaign is on the right track as you have errors and biases to overcome. To set up your survey for success, you have to equip yourself with the right survey tool. The online survey tool is pivotal to the success of your market research campaigns.
To collect data for your research, an online survey tool is required. But you also need to have a clear strategy and come up with the right questions. You can also try to incentivize the respondents, and use the right tool that has significant features and functionalities.
The online survey platform that you choose should have multiple features that will help reduce sampling errors. For example, you can segment your respondents based on multiple factors that serve your interests. It is pivotal that you invest in an online survey tool such as SurveySparrow that has every feature that you would want in a survey platform.
Wrapping up…
Samples are important in psychology research as it allows businesses to find out a demographic’s behavior. A variety of samples are collected based on what the researchers are studying. As a researcher, you need to be careful about the sample population that you consider as your final result totally hinges on it.
Sampling allows businesses to get detailed and comprehensive information from a limited sample. Researchers get more time for data collection. It is easy to collect information from many individuals and cost-effective to collect data from a part of the population.
If you are looking for an online survey tool to gather data for your sampling exercise, look no further than SurveySparrow. It is one of the most robust and powerful market research software that can cater to all your data sampling needs.
