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When Analyzing Employee Survey Data, Avoid These Common Logic Biases

When Analyzing Employee Survey Data, Avoid These Common Logic Biases

When reviewing employee survey data, it's easy to fall prey to logical fallacies. These errors in reasoning can lead to misguided decisions that may not effectively address the concerns or sentiments expressed by employees. Identifying and understanding these fallacies is crucial for making informed decisions based on your employee survey data. Here are five common logical fallacies that can derail leaders’ responses, along with some tips to avoid them.

1. Confirmation Bias

This is the tendency to search for, interpret, favor, and recall information in a way that confirms one’s preexisting beliefs or hypotheses. An executive might focus only on survey responses that suggest high engagement and confidence in the future of the company while ignoring signs of discontent because they believe their leadership has been highly effective. 

To combat confirmation bias, actively seek out and consider data that contradicts your beliefs. Encourage diverse perspectives in data analysis and decision-making to challenge and broaden your understanding. 

2. Overgeneralization

This involves making broad generalizations from a small sample of evidence or isolated instances. Ensuring sufficient sample size with quantitative data can help protect against this bias, but it can be a challenge when reading responses to open-ended questions or comments. Reading a few comments about burnout and then concluding the entire workforce is on the brink of collapse would be an example of overgeneralization. 

To protect against this fallacy, recognize the limitations of your data and avoid drawing sweeping conclusions from limited responses. Use a variety of data sources and employee listening methods — Perceptyx can provide engagement, point-in-time, onboarding, and exit surveys, as well as crowdsourcing, through our People Insights Platform — to ensure that your data is representative of the entire organization. 

3. False Cause (Post Hoc Ergo Propter Hoc)

Determining that because one thing follows another, the first thing is the cause of the second is a false cause fallacy. When reviewing survey data, an executive might attribute an increase in employee engagement solely to a recent corporate communication, ignoring other factors internal and external to the organization, that could also affect engagement levels.

To protect against this fallacy, look for multiple potential causes before drawing conclusions about causality. Remember that factors external to the organization also have an impact on employees’ experiences and engagement within the organization. Use analytical methods to test relationships between variables, and consider temporal sequences and external influences. 

4. Anchoring Bias

When a consumer of data relies too heavily on the first piece of information encountered (the “anchor”) to make a decision, this is an anchoring bias. This bias frequently emerges with executive presentations. If an early data point like survey participation is given more significant weight, it can influence their interpretation of subsequent data.

Protect against anchoring bias by delaying judgment until reviewing more data. Be open to adjusting initial assessments of the results as more information becomes available. With open-ended comment feedback, reviewing a thematic analysis first can help avoid anchoring on the first few comments reviewed.

5. Bandwagon Effect

This is the tendency to believe something because many other people also believe it. Adopting “best practices” can be an example of the bandwagon effect, but when reviewing survey data, repeatedly hearing from a vocal minority can cause an executive to adopt a belief, assuming the view is universally held, even if broader data does not support the conclusion. 

To protect against the bandwagon effect, leaders should leverage comprehensive quantitative data analysis rather than simply relying on the loudest voices. Ensure that decisions are based on carefully weighted evidence and that minority opinions are considered, but not given undue influence. 

Leaders with great intentions and access to reliable data can still fall prey to logical fallacies when analyzing, interpreting, and acting on survey results. By being aware of the five logical fallacies above, leaders can use holistic and comprehensive employee listening data to make informed decisions with speed and confidence. 

Perceptyx Can Help You Discover All the Insights in Your Data

By partnering with Perceptyx, leaders gain access to advanced analytics and expert guidance, ensuring decisions are informed by a thorough understanding of their organization's unique dynamics. Avoid the pitfalls of logical fallacies and embrace a data-driven approach to improving employee experience (EX). Schedule a meeting to learn how we can help you harness the full power of your employee feedback, drive meaningful change, and foster a culture of engagement and excellence.

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