Language is the foundation of human communication, shaping how we express thoughts, emotions, and ideas. Whether spoken, written, or signed, language provides structure and clarity, ensuring that messages are understood as intended. Its importance extends beyond daily conversations, playing a crucial role in education, business, politics, and culture, making it one of the most powerful tools in human society.
Words have the power to inform, persuade, and inspire. In professional settings, effective language use ensures that instructions, negotiations, and collaborations run smoothly. It also acts as a bridge, fostering understanding and unity despite differences in background and beliefs.
When writing a feedback survey, your use of language is crucial to ensuring clarity, engagement and accuracy in responses. Your question set should help you gather useful data and as such needs to appeal to your audience. In this article we explore some of the language considerations you should make to ensure you get the best results from your survey.
Writing with simplicity
Your language should be clear, simple, concise and straightforward to avoid confusion. Technical terms, or overly complex wording may be difficult for employees to understand and questions should be kept short and direct to improve readability and response rates. The tone should be friendly yet professional, encouraging honest feedback without sounding too formal or intimidating. A conversational tone may help employees feel more comfortable but you must remain respectful and professional in your tone.
Writing without bias
By minimising bias in survey design, you can collect more objective, meaningful, and actionable feedback, leading to better decision-making and more accurate insights. Biased questions can lead to skewed results, misinterpretations, and decisions based on incomplete or misleading information.
Your questions must use neutral or unbiased wording so that your employees don’t lean towards a particular answer. Three languager pitfalls in survey design are 'loaded questions', 'leading questions' and 'social desirability' bias-
A leading question is a question that is phrased in a way that subtly influences respondents toward a particular answer, rather than allowing for an unbiased response. These questions can create skewed results by encouraging positive or negative responses based on their wording. An example in an employee engagement survey could be - "How much do you appreciate the excellent leadership of your manager?" Vs “How would you rate your manager’s leadership?”. The first question assumes that the manager's leadership is excellent, which may pressure employees into giving a more favourable response. The second question, however, remains neutral, allowing employees to provide an honest evaluation.
A loaded question is a question that contains an assumption that may not be true, forcing respondents into a specific perspective or making it difficult for them to answer honestly. These questions can create bias by making respondents feel pressured to agree with the assumption. An example in an employee engagement survey could be - “What do you think about the lack of growth opportunities in this company?" Vs “How would you describe the growth opportunities available at this company?”. The first question assumes that there is a lack of growth opportunities, which may not be the case for all employees. The second question removes the assumption, allowing respondents to share their genuine experiences, whether positive or negative.
Social desirability bias is the tendency for respondents to answer survey questions in a way that they believe is socially acceptable or favourable, rather than providing their true thoughts or behaviours. This often happens when people feel pressured to present themselves in a positive light. In workplace feedback, employees may hesitate to criticise their employer for fear of negative consequences, even in anonymous surveys. This bias can lead to inaccurate data, making it difficult to draw meaningful conclusions. To minimise social desirability bias, surveys should ensure anonymity, use neutral wording, and frame questions in a way that normalises all possible responses. Indirect questioning—such as asking about general behaviours rather than personal ones—can also reduce pressure and encourage honesty.
Writing for different generations
When designing survey questions for different generations, it's important to consider language style, tone, and familiarity with terminology. For older generations, questions should be clear, formal, and avoid slang or modern digital references they may not recognise. For example, instead of asking, "How do you vibe with our organisational goals?”, a more suitable question would be, "How satisfied are you with our organisational goals?". Younger generations, particularly Gen Z and Millennials, may respond better to a more conversational or engaging tone, incorporating relatable language or digital references. Additionally, while older generations may prefer traditional rating scales, younger respondents might engage more with interactive formats, such as emojis or slider scales. Tailoring the wording and format of survey questions ensures clarity and relevance for all age groups, leading to more accurate and useful responses.
Writing in the correct language
It may seem obvious, but writing your surveys in the correct language for your audience has a significant effect on completion rates. In many businesses, English is the most commonly used language, however geography and local populations play a far more critical role in blue-collar industries such as warehousing. Hiring from the local population ensures a steady supply of workers who can fill roles quickly. Warehouses often require a large number of employees for tasks such as inventory management, packing, and logistics. Local workers can fill these positions with minimal relocation challenges, reducing delays in staffing. However in some locations these populations may be comprised of people where English is not their first language. Having the ability to deliver your feedback survey in multiple languages allows you gather more meaningful responses from a wider section of your workforce.
Writing for Inclusivity and Accessibility
Ensure language is inclusive and respectful of diverse backgrounds, avoiding gendered terms, culturally specific references, or assumptions about the respondent’s experiences. Also, consider accessibility—using plain language can help people with varying literacy levels or non-native speakers participate effectively.
Interpreting language - Using AI to reduce bias
Once you’ve collected all your responses, you need be able to analyse the data and the language thats been used. Artificial intelligence (AI) plays a crucial role in analysing survey results while minimising human bias. Traditional survey analysis often involves subjective interpretation, where personal opinions or expectations may influence the conclusions drawn. AI, on the other hand, can process large volumes of data objectively, ensuring that insights are based on factual patterns rather than preconceived notions.
One of the biggest challenges in survey analysis is the potential for researchers to unintentionally influence results based on their expectations or preferences. AI algorithms analyse responses using consistent criteria, ensuring that every answer is treated equally. Unlike humans, AI does not have personal biases, emotions, or assumptions that could distort data interpretation.
AI can mitigate social desirability bias by detecting patterns in responses and flagging inconsistencies. Additionally, AI-powered sentiment analysis can assess the tone and emotion behind responses, providing a deeper understanding of how people truly feel.
AI ensures consistency in data analysis by applying the same methodology across all responses. While human analysts might unintentionally weigh certain feedback more heavily, AI assigns equal importance to all data points including language. This helps produce fair and balanced insights that are not influenced by individual preferences.
If the topics in this article have been of interest, speak to our customer success team about how Ten Space can help you with surveys in different languages and the reduction of bias in your data analysis.
