You can’t always eliminate bias, but you can be aware of it
By Alli Milne, Manager of Digital Learning Content at Alchemer
Research and sampling are foundational aspects of data collection. They enable organizations to draw meaningful conclusions from a subset of a larger population. This process is essential for understanding trends and making informed decisions based on the data gathered.
To ensure data validity, every member of the studied group must have an equal opportunity to participate in the survey. Additionally, researchers must remain mindful of potential biases in surveys. This dual focus helps guarantee that the results accurately reflect the entire population.
However, this task is fraught with challenges. Researchers often do not realize they have inadvertently created a biased survey. This recognition typically occurs only after they analyze the results.
Survey creators must be vigilant about their own biases. They should also be aware of the biases present in the individuals responding to their surveys. This awareness is essential for collecting accurate and reliable data.
Bias in surveys can manifest in numerous forms, impacting both the design of the survey and the responses it garners. While it is impossible to eliminate all bias completely, researchers can take steps to minimize its impact significantly.
Being aware of the various types of bias is a crucial first step. This understanding allows researchers to implement strategies that reduce bias in their surveys. This awareness can lead to more accurate data collection and, consequently, better decision-making based on the findings.
Types of Bias in Surveys
Bias can enter the survey process in multiple ways. Understanding these types of bias is the first step toward mitigating their effects.
Below, we explore several types of bias that can occur during the survey creation and response phases. We also provide examples to illustrate how each bias can skew results. Understanding these biases is essential for improving the accuracy of survey data.
Hidden Bias
Hidden bias is often the most challenging type of bias to identify. It arises from the wording used in survey questions or answer options. This subtle influence can significantly affect respondents’ choices and the overall results of the survey. This bias can unintentionally (or intentionally) steer respondents toward specific answers.
Adjectives play a significant role in this type of bias, as they can evoke strong emotional reactions. They can dramatically influence the results of the survey. The choice of adjectives can shape how respondents perceive the product and their overall feedback.
Example: Imagine a question that offers the following choices: “awful,” “acceptable,” “good,” and “amazing.” The presence of the extreme terms “awful” and “amazing” can lead respondents to select one of those options rather than the more neutral “acceptable.” Similarly, if a color list includes “British Racing Green” instead of just “dark green,” respondents may choose it based on its appealing name, leading to skewed results.
To mitigate hidden bias, survey creators should strive for neutrality in their language. Avoiding emotionally charged words and opting for more neutral descriptions can help create a fairer survey environment. Read more about hidden bias here.
Confirmation Bias
Confirmation bias occurs when survey designers inadvertently phrase questions to support a preconceived hypothesis or belief. This bias can significantly skew results and lead to faulty conclusions. For instance, instead of asking, “Do you prefer Product A or B?” a biased survey might ask, “Is Product A better than B?” This wording leads respondents to prefer Product A, matching the designer’s expectations.
Example: In a customer satisfaction survey, a question like, “How satisfied are you with our excellent customer service?” presumes satisfaction. This phrasing can lead respondents to answer positively, even if they had a negative experience. Such biased wording skews the results and does not accurately reflect customer sentiment.
To counteract confirmation bias, survey designers should ensure their questions are neutral. Open-ended questions are particularly effective, as they allow respondents to express their true feelings. This approach prevents leading respondents toward a specific conclusion and encourages more honest feedback. You can read more here.
Irrational Escalation
Irrational escalation can emerge from a survey that is heavily laden with confirmation bias. This phenomenon occurs when stakeholders believe the research is flawed if it does not support their pre-existing beliefs or investments.
Consequently, they may dismiss the findings without proper consideration. This can lead to significant missteps for companies. They might ignore critical market shifts because they fail to acknowledge data that contradicts their assumptions. Such oversight can hinder their ability to adapt and respond effectively to changing market conditions.
Example: The shift from DVD rentals to streaming services illustrates how companies can miss crucial market changes. If stakeholders believe DVD rentals are better, they may ignore evidence showing a shift in consumer preferences to streaming. This refusal to acknowledge change can lead to lost opportunities. Ultimately, it results in decreased market relevance for the company.
To combat irrational escalation, it is essential to foster an organizational culture that values objective data. This culture should encourage stakeholders to embrace insights, even when those insights challenge established beliefs. By prioritizing data over assumptions, organizations can make more informed decisions. Read more here.
Gender bias
Gender bias is a prevalent issue in survey design, often manifesting as a binary choice between male and female options. This simplistic view overlooks the complexity of gender identity and may alienate respondents who don’t fit within these categories. Conversely, listing an exhaustive array of gender options can overwhelm respondents, leading to survey fatigue.
Example: The U.S. Department of Education recognizes over 25 gender options. Presenting such a lengthy list in a survey can discourage participation. This may lead to skewed results and a lack of accurate representation. Researchers must consider which criteria are essential for their survey and strive for inclusivity without overwhelming respondents.
Addressing Gender Bias in Survey Design
To address gender bias, survey creators should prioritize including options that reflect a broad spectrum of identities. At the same time, they should keep the selection manageable for respondents.
This balance ensures that all individuals feel represented and makes the survey easier to complete. Providing an “Other” option with a text box for respondents to self-identify can also be beneficial. You can read more here.
Sampling Bias
Sampling bias occurs when a survey does not reach a representative sample of the target population. This bias can arise from various factors, such as the method of survey distribution.
For instance, providing a survey exclusively via a QR code may inadvertently exclude some individuals. This can happen if those individuals lack mobile devices or do not have internet access. As a result, the survey may not reach a representative sample of the population.
Example: Imagine a scenario where a company seeks feedback from a diverse customer base. However, it only distributes the survey through a mobile app. This approach may limit participation from customers who do not use the app, leading to a less representative sample. This approach could alienate older adults or those in low-income brackets who may not have access to the latest technology.
At Alchemer, we collaborate with our Panels team to ensure equitable representation across demographics. This often involves using multiple distribution methods to reach a wider audience, thereby reducing the risk of sampling bias. You can read more here.
Cultural Bias
Cultural bias occurs when researchers make assumptions based on their own cultural backgrounds. This often leads to neglecting the diverse perspectives that exist within a population. Such biases can result in misunderstandings and skewed data, ultimately affecting the quality of research findings. This bias can cause misinterpretations and oversights about how people from different cultures understand questions.
Example: Using terms like “Coke,” “soda,” or “pop” to refer to soft drinks can confuse respondents from varying regions. Similarly, misnaming food items, such as using “crisps” instead of “chips,” can lead to misunderstandings.
To reduce cultural bias, researchers should strive to use language that is culturally neutral and inclusive. Adding an “Other” option with a text box allows respondents to share their views without limiting them to predefined choices. You can learn more here.
Question-Order Bias
Question-order bias occurs when the sequence of questions influences how respondents answer subsequent items. This bias can lead to skewed results, as earlier questions may prime respondents to think about specific concepts or ideas.
Example: If a survey starts with questions about elephants, it can influence later responses. When the survey asks respondents to name gray animals, they are likely to mention elephants. This effect occurs because the earlier context prompts them to think of elephants first.
To counteract question-order bias, researchers can take several approaches. One effective method is to randomize the order of questions. Another option is to break the survey into separate sections. These strategies help reduce the influence of prior questions on respondents’ answers. Read more here.
Recency Bias
Recency bias affects respondents’ recall, leading them to favor more recent or memorable experiences over older ones. This bias can skew survey results, particularly in assessments of products, services, or events.
Example: In a survey about the greatest songs, respondents often mention songs from their youth and overlook earlier classics. To mitigate recency bias, researchers can use several strategies. One approach is to provide a broader timeframe for respondents to consider. Additionally, they can ask for a mix of recent and past favorites. These methods encourage respondents to think beyond their most recent experiences. You can read more here.
Extreme or Neutral Response Bias
Extreme or neutral response bias occurs when some respondents tend to select extreme options, such as “strongly agree.” In contrast, other respondents often gravitate toward neutral responses. This variation in response styles can skew survey results and impact the overall data interpretation. Certain cultural contexts can particularly amplify this bias.
Example: In Eastern Europe, respondents may be less inclined to rate services as “excellent.” As a result, this tendency can lead to an underrepresentation of higher scores on satisfaction surveys. Consequently, the overall data may not accurately reflect customer satisfaction in the region. Understanding cultural norms surrounding rating scales can help researchers interpret results more accurately.
To counter extreme or neutral response bias, survey designers can implement several strategies. One effective approach is to offer balanced response options.
Additionally, designers should encourage respondents to reflect on their experiences before answering. These methods can help elicit more accurate and thoughtful responses. You can read more here.
Desirability Bias
Desirability bias occurs when respondents provide answers they believe are socially acceptable or favorable, rather than expressing their true feelings. This phenomenon is particularly common in face-to-face surveys, where individuals may alter their responses to impress the surveyor.
Example: In a survey about lifestyle choices, respondents might overstate their engagement in healthy behaviors. For example, they may claim to exercise regularly. This tendency often arises from a desire to conform to societal expectations. As a result, the survey data may not accurately reflect their true habits.
Offering anonymous surveys online can help mitigate desirability bias, allowing respondents to answer more honestly without fear of judgment. You can read more here.
Response Bias
Individuals can alter their answers based on how they frame or perceive the questions, leading to response bias. This bias can result in inaccurate data, ultimately undermining the integrity of the research.
Example: A question such as, “How much do you enjoy our product?” can create pressure for respondents. They may feel compelled to provide a positive answer.
This pressure can skew the results and lead to misleading data about customer satisfaction. Clear and neutral wording can help reduce response bias and encourage more accurate responses.
Acquiescence Bias
Acquiescence bias refers to the tendency of respondents to agree with statements presented to them, regardless of their actual opinions. This bias can distort survey results and create a false sense of consensus.
Example: In a customer satisfaction survey, respondents may agree with positive statements about a product without critically evaluating their experiences. Offering balanced response options and phrasing statements in both positive and negative formats can help minimize acquiescence bias.
Test First to Minimize Bias
Creating a survey without bias presents a significant challenge. Many biases operate on a subconscious level, making them difficult to identify. Most people are often unaware of their cultural biases, recency or nostalgia biases, and confirmation bias.
This lack of awareness can lead to unintended influences on survey results. Testing your survey with individuals from various regions and demographics is essential. This practice can help you identify potential biases before launching the survey to thousands of participants. By addressing these biases early, you can avoid making business decisions based on flawed data.
Alchemer provides self-service panels for market researchers. These panels enable researchers to test their surveys on a diverse yet small audience. This service is available for just a couple of hundred dollars, making it an affordable option for effective survey testing.
This approach helps individuals avoid the financial consequences of conducting a flawed survey. The expenses include not only the cost of the panel and results but also the more significant cost of making potentially erroneous business decisions. By testing surveys beforehand, researchers can mitigate these risks effectively.
For more information on reducing bias in your surveys, read Leading Practices: Understanding and Reducing Bias in Your Surveys.
