When designing survey for online respondents, there are many factors to consider that have the potential to prompt bias answers. In this module, you'll learn:
- Common types of survey bias
- Modifications you can make to question designs that avoid biased responses
- How to plan your survey to collect more reliable data from it
This is a 8 minute on-demand training session.
What is Survey Bias?
Survey bias refers to the systematic deviation of survey results from the true population parameters due to errors or distortions introduced during the survey process. It occurs when the design, implementation, or analysis of a survey favors certain responses over others, leading to a misrepresentation of the target population's views, opinions, or characteristics. Bias can manifest in various forms, such as sampling bias, where the sample is not representative of the entire population, or response bias, where respondents provide inaccurate answers influenced by social desirability or wording of questions. To ensure the validity and reliability of survey findings, it is crucial to eliminate bias as much as possible.
Eliminating survey bias is paramount for several reasons. Firstly, bias compromises the accuracy of the collected data, leading to incorrect conclusions and misinformed decision-making. Decision-makers, researchers, and policymakers rely on survey data to gain insights into public opinions, market trends, or social behaviors. If the data is biased, the resulting decisions and policies may not align with the actual needs and preferences of the population, potentially leading to wasted resources or unintended negative consequences. Secondly, biased survey results can perpetuate inequalities and unfairness.
When certain groups are underrepresented or misrepresented in surveys due to bias, their voices and concerns may be overlooked or undervalued, perpetuating existing disparities and hindering efforts to address societal issues effectively. By striving to minimise bias, surveyors can aim for more inclusive and equitable data collection, ensuring that diverse perspectives are accounted for and respected. Ultimately, by eliminating bias, surveys can fulfill their true purpose of providing accurate and reliable information to guide informed decision-making and foster positive societal change.
The Types of Bias in Surveys
- Acquiescence Bias: This bias explains that people are more likely to choose agree options over disagree options.
- Reading Order Bias: The order we see options can influence how we view them. For example, in this form of bias, respondents are more likely to pick the first or last option in a list.
- Central Tendency Bias: This bias explains the fact that people tend to think a midpoint in a list of numbers is the average when this is not usually true.
- Left to Right Bias: This bias is the phenomenon where people think options that are further right on a list are higher, stronger, or more positive than those on the left.
- Top to Bottom Bias: The top-to-bottom bias is very similar left to right bias but flipped. In this bias, it shows how people tend to think the option at the top of a list is the “best” or “good.
Understanding the Impact of Survey Bias
Recognizing the potential consequences of flawed data enables researchers to take corrective measures, improve survey methodologies, and promote evidence-based decision-making.
Implications for Decision Making
Decision-makers rely on survey data to formulate strategies, policies, and business plans. However, if the data is biased, the decisions made based on it can lead to inefficiencies, incorrect resource allocation, or ineffective solutions. It becomes imperative for stakeholders to comprehend the potential negative impact of biased data to make informed choices.
Misleading Product Strategies
If market researchers fail to account for bias in their surveys, they might misinterpret consumer preferences and needs, leading to the launch of products that do not resonate with the target market. As a result, investments in product development and marketing efforts may not yield the expected returns, causing financial losses.
Mitigating Bias in Surveys
Mitigating survey bias is crucial to obtaining accurate insights into the target population and ensuring that survey results inform decision-making accurately.
By incorporating the following strategies and techniques into survey design and implementation, researchers can work towards minimising survey bias:
Random sampling is a fundamental method used to create a representative sample of the target population. By ensuring that each member of the population has an equal chance of being selected for the survey, random sampling minimises the risk of sampling bias, where certain groups are overrepresented or underrepresented.
Techniques like simple random sampling, stratified sampling, and cluster sampling should be employed to increase the accuracy of survey results and provide a clear understanding of the entire population's views and characteristics.
Encouraging Honesty in Survey Responses
Anonymity and confidentiality are crucial in creating a safe environment where participants feel comfortable sharing their true opinions without fear of judgment or repercussions. Additionally, using neutral and non-leading questions can help prevent participants from feeling compelled to respond in a certain way.
Keeping Survey Simple, Clear, and Neutral
To minimise survey bias, questions should carefully crafted using clear and unambiguous language. Neutral wording ensures that questions do not steer respondents towards a particular viewpoint, allowing for more accurate and unbiased responses.
Engaging Respondents with Different Types of Questions
When surveys include a mix of question formats, such as multiple-choice, open-ended, and Likert scale questions, it encourages participants to think critically and provide diverse responses. Multiple-choice questions allow for straightforward and structured answers, while open-ended questions provide an opportunity for respondents to express their thoughts in their own words, yielding richer and more nuanced insights. Additionally, incorporating Likert scale questions enables the measurement of attitudes and opinions on a scale, offering a more granular understanding of respondents' perceptions. By diversifying question types, respondents are less likely to feel constrained or influenced, leading to more authentic and unbiased responses.