How can you avoid biases when measuring Employee Engagement?

One way to avoid biases in how you measure employee engagement is to ensure that the questions asked in a survey are worded clearly, without jargon, and only ask about one thing. Ensure that everyone who reads the questions will interpret them in the same way. If you are unsure when drafting a question, ask a few people to see how they would interpret it.

Biases can affect the quality and usefulness of employee engagement data, leading to inaccurate results. For this reason, it would be appropriate to conduct surveys and employee experience studies more frequently and at more accurate timing. Evaluating the results well may be the most important thing.

A key starting point is to recognise that you cannot avoid bias, but you can minimise it and account for it. In terms of minimising bias, you can use validated questions and be mindful of the timing. In terms of accounting for bias, one effective approach is to use external Norms when you analyse the results. These can help you judge how typical or how unusual a response is.

The challenge with biases is that we don't usually know they exist until we've been made aware. This is why they matter. Biases affect our ability to be truly objective because they are "systematic errors or deviations from the true value of a measurement"...yes. And they're problematic because most of the time we don't even know they're having an affect! That means that we have the illusion that we're finding the 'truth' when we're not. And that's a big problem. Identifying biases is challenging - and it's a task that's never over. Definitely an important thing to always have one eye out for!

Biases are subtle distortions that can skew employee engagement data, leading to inaccurate insights. They are important because they can lead to wrong decisions. If we want to make sure that the data accurately represents the experiences of the employees, we must acknowledge and deal with these biases.

To avoid the biases, it's important to avoid leading questions. Leading questions can inadvertently influence respondents to answer in a way that aligns with the question's bias rather than providing their genuine opinions. To steer clear of this, ensure that your questions are neutral and free from any implicit suggestions. Also, make sure that the ordering of questions does not influence the respondent to nudge in a particular direction. Instead of asking leading questions like, "Don't you agree that our company provides excellent opportunities for career growth?" opt for a more neutral tone such as, "How would you rate the opportunities for career growth within our company?"

I’m a big fan of employee engagement surveys that simply make statements and ask the employee whether they “strongly agree” or “strongly disagree” on a scale. For example, “I believe my manager supports my career development.” Agree or disagree? In this regard, you avoid making yes or no questions that require the question itself to be unbiased.

The wording of engagement survey questions (or statements) is so important. Leverage in-house expertise - or seek out the help of an expert business psychologist to help you draft or review survey questions. While you can find lists of questions pretty easily online, you need to be sure you measure what matters most for your people and business, and that the wording of questions is balanced (not leading), measuring a single 'construct' and is actionable.

When phrasing your questions, make sure to use neutral, clear language to minimize biases. Avoid leading phrases that might influence responses. Ensure each question focuses on a single topic. This facilitates the collection of sincere, objective employee feedback.

Instead of putting open-ended answers in the surveys, it would be correct to put options that the employee will answer clearly. For example, I don't know, I don't understand, I don't want to answer.

One way we've found to avoid bias in our Engagement surveys is to ensure there's an option to provide verbatims. this type of context in combination wth a scoring system proves invaluable. For instance, you could ask employees to rank how much they trust company leadership. In some cases, employees will read this as their direct managers, others may assume the leaders at the very top. Yes, you could include specifically worded questions to identify for each - but the verbatims will identify it anyway and you're likely to learn more about why they do or or do not trust their leaders - which I think is much more valuable.

One often overlooked type of bias in survey question design is self-assessment bias (e.g., questions that start with "I know..." or "I understand...") for which the answers will be artificially inflated. This is because respondents are more likely to tell you they know or understand something even if they really don't because there's a judgment aspect to the question. One way to address this is to put the onus on an accountable party instead. For example, turn a question like "I understand the new policy" into something like "The new policy has been clearly communicated."

To avoid response biases, offer clear and specific options instead of open-ended answers. Include verbatim options to capture context and deeper insights. Be cautious of self-assessment questions, as they can lead to inflated responses. Reframe such questions to focus on objective measures.

Also consider the timing of surveys and pulses to inform key planning and decision making schedules. For example, many clients have FY planning in late summer and fall in North America, so be sure to have refreshed results in place to help make data-based decisions regarding investments in workforce dev, talent management, workforce planning, etc.

To avoid timing biases in measuring employee engagement, mix up when you ask for feedback, consider different schedules and time zones, watch response trends, and stress the importance of everyone's input. This way, you get a clearer view of how engaged employees really are.

To avoid timing biases, I ensure that feedback is collected at consistent intervals and avoid times of high stress or major changes. This method helps in gathering more trustworthy and accurate information, giving a genuine picture of employee engagement all year round.

The AI guidance says "You should also aim for a high response rate and follow up with the non-respondents." You should NOT strive for 100% response rates. Some people will not want to respond for a variety of reasons - and that's OK. Hopefully "following up with non-respondents" means sending out an automated reminder or two to complete a survey and NOT following up more often than that or directly with non-respondents who might feel like you're hounding them to reply and increase their skepticism around the promised confidentiality and/or anonymity of your surveys.

I make sure a varied mix of employees is included in surveys, not just those who are most likely to reply, to prevent sampling biases. In addition, I politely remind non-respondents, but I never push too hard because I respect their decision to participate or not.

To keep biases out when analyzing Employee Engagement data, here's the deal: Have a few different folks look at the data. Make sure everyone knows the rules. Keep things anonymous, so nobody's swayed by who's who. Use tools that crunch numbers fairly. Get input from different people with different backgrounds. And write down why you're making certain decisions. These steps help you see how engaged your team really is, and no biases.

I engage different perspectives and maintain transparency in my data review process to prevent biases in analysis. I protect privacy to avoid outside influences and use impartial methods to interpret data. This method helps in preserving accuracy and fairness when assessing employee involvement.

To ensure that the analyses and analysts are unbiased, a transparent process must be established from the beginning. It is essential that each stage of the analysis process is monitored. To ensure the reliability of the analysts, conducting reference checks is also beneficial.

When measuring employee engagement, it's important to go beyond general feelings and delve into observable behaviors. Here's why it's crucial and how to incorporate it "Satisfied" can mean different things to different people. One person might feel "satisfied" with a heavy workload as long as they feel challenged and valued. Another might feel overwhelmed and disengaged with the same workload. Example: Instead of: "Are you satisfied with your workload?" (general feeling) Ask: "Do you feel you have enough time to complete your assigned tasks efficiently?" (observable behavior) This revised question focuses on an employee's ability to manage their workload, a more objective measure of their engagement.

So much of the biases mentioned here are addressed and mitigated by using a employee experience partner and solution that has minimized bias in item-working, category structure, KPIs, dashboards, visualization, algorithm design/testing, etc. Additional benefits of these partnerships are benchmarking, expert interpretation of results and practical consulting on taking action and improving to close experience gaps.

Keep the questions neutral and diverse. Pilot tests to catch biases early and use statistical methods to adjust results. Continuous review after each survey helps refine future editions, making them more accurate. I appreciate it's challenging, but I truly believe more non-anonymous surveys will provide more honest feedback, if not the first time, when you can show you deliver on feedback!

These are very personal and when directly asked, it gives always a positive reply. so better to move from being person to non-personal. survey and polls are some significant steps in this direction. Also, 'tell your story' can be asked from employees which can even be published in newsletter.