Survey Bias: Sampling Bias, Types, & Preventive Techniques

There are tons of online articles available on general rules for preventing survey bias and maximizing survey feedback. And they all are great resources if you want an initial understanding of what is survey bias and general tips on how to avoid it.

But if you are a professional a bit far ahead in your journey and are looking for specific answers on how to avoid particular forms of survey bias in different contexts, stay with me to the end.

In this article, we will talk about 3 distinct types of survey biases that further morph into several different subcategories.

It’s important to understand each of them and adopt ways to keep them out of your surveys as much as possible.

As we’ll talk about the subject, you’ll realize that it’s a vast topic. For optimum comprehension, we have decided to divide the article into a series of three parts. Each part will devote itself to a particular type of survey bias, and unpack it in detail.

What is Survey Bias?

Bias, in most traditional terms, points to instances where one person, thing, or goal is being supported or opposed unfairly due to personal reasons and opinions.

In surveys and research, bias refers to any deviation from the actual truth. Any act or decision that can potentially influence the survey results and drive them away from the actual picture is known as survey bias.

Different Types of Survey Biases:

Survey bias manifests itself in three unique ways:

1. Sampling bias: When you choose a sample that fails to accurately represent all strata of the target population.
2. Response bias: When respondents answer questions inaccurately or unfaithfully or under different types of influences or assumptions.
EVERYTHING ABOUT RESPONSE BIAS
3. Interviewer bias: When survey creators or interviews consciously or unconsciously try to influence the survey process, resulting in skewed findings.

In today’s article, we’ll focus our attention on Sampling Bias, and learn how to prevent it and minimize its impact on our surveys and research.

Important Disclaimer:

Before we dive into the discussion, it’s crucial to reiterate here that survey biases cannot be eliminated from the equation. External factors can play a role where you cannot tightly control all aspects of a survey design.

The goal, therefore, is to strive for objectivity and accuracy as much as possible and aim to minimize survey biases as much as you can.

Sampling Bias

Sampling bias occurs when the process of selecting respondents for a survey or study systematically favors some individuals or groups over others. It is extremely harmful to the generalizability of the research findings because the results can then only be applied to populations that share the very limited characteristics of the sample.

Sampling bias has several types, based on different factors that cause it.

• Pre-screening bias

This bias occurs during the screening phase of the process when researchers are selecting participants for the study.

Example: In a survey to understand smartphone usage in a population, the researchers choose respondents from only a certain area code.

By implementing these techniques, researchers can access respondents that represent the most diverse characteristics of the population, ensuring maximum generalizability of survey results.

• Exclusion bias

Exclusion bias arises when members of certain groups or subgroups are excluded from the target population.

Example: If your survey is in English, members of your target population with a language barrier may not be represented accurately in your study, thus skewing its results.

• Survivorship bias

It occurs when the sampling criteria only include subjects that have passed or endured a certain objective and ignore everybody else who may have failed or produced null findings.

Example: To study the ease of doing business in a certain economic climate, we only include successful businesses and ignore the ones that have failed.

• Self-selection bias

It is also called volunteer bias and refers to sampling situations when respondents participate in a survey due to their particular interests or motivation related to the research topic. This bias results in a sample with strong opinions about a topic or where participants share too many unique characteristics.

Example: In a survey about hot topics like abortion, people with strong opinions will be more likely to take part compared to those on the fence or with limited awareness.

• Undercoverage bias

It is also called representation bias and is closely connected with exclusion bias. It occurs when the sampling process underrepresents certain individuals or groups from the sample.

Example: A study that wants to understand high school students’ reading habits only uses an online survey to get the data. It’ll exclude or under-represent students who like to read but have limited internet access and cannot take part in the online survey.

How to avoid sampling bias?

1. Random sampling: Sampling methods such as simple random sampling or stratified random sampling ensure that every member of the target population has an equal chance of being selected.
2. Diverse recruitment sources: Utilize multiple channels to recruit respondents, including online platforms, community centers, and public spaces to reach an inclusive and diverse sample.
3. Neutral and limited pre-screening questions: Minimize pre-screening questions and use neutral language to ensure respondents can answer without fear or bias.
4. Objective inclusion criteria: Define your inclusion criteria clearly, ensuring it aligns perfectly with the goals of the research.
5. Monitor and track non-responses: Track and address any gaps between the sample of your initial survey and the follow-up. If the gaps are significant, post-stratification weighting can be applied to address potential biases.
6. Encourage participation: Design mobile-friendly surveys, include better questions, and employ better follow-up to improve survey participation rates. A good introduction to the survey that clearly outlines its objectives also encourages respondents to take part in the study.
7. Address language and cultural barriers: Ensure cultural sensitivity in your surveys. Remove language barriers by providing translated materials, and collaborate with bilingual survey creators and data collectors to prevent this bias.
8. Pilot testing: It refers to researching a small subgroup of your survey sample to spot any potential biases. On the results of pilot testing, adjust the study design to better create a survey sample that truly represents your survey population.

Conclusion

While it may be difficult to create and perform a survey free of all kinds of biases, your job is to strive and aim for such a goal.

By implementing these techniques, we hope that researchers and survey creators can access respondents that represent the most diverse characteristics of the population, ensuring maximum generalizability of survey results.