Why survey smileys disrupt your data quality

Smileys and emojis are increasingly used in surveys and employee research. They seem like a simple, accessible way to collect feedback, especially when organizations want to gauge the general mood of their employees quickly. But despite their popularity, smileys can seriously distort data quality. Especially in HR surveys, where reliable and objective data is essential, smileys can skew results and lead to poor decisions. 

In this blog, we discuss why smileys are a bad idea for your surveys, how they affect data quality, and which alternatives work better for collecting valuable HR data.

Chapter 1: What is data quality and why does it matter in HR?

The term data quality refers to the extent to which collected data is accurate, complete and consistent. This is especially important for HR data, where organizations rely on data to make strategic decisions about workforce management, productivity and employee satisfaction. Without high data quality, there is a real chance that wrong insights lead to wrong decisions. 

Data quality can be measured using four factors: 

• Accuracy: Does the data correctly reflect reality?

• Completeness: Has all relevant data been collected?

• Consistency: Is the data collected and processed in a uniform way?

• Relevance: Does the data help you make informed decisions?

In HR, data quality plays a crucial role in interpreting HR metrics such as staff turnover, absenteeism and employee satisfaction. These figures are often presented in human resource analytics dashboards, but if the data quality is not in order, the analyses can be misleading.

Chapter 2: What is HR data and how is it used in HR analytics?

HR data, or human resource data, covers all the data an organization collects about its employees and HR processes. This can range from employee demographics to performance statistics, absence reports and feedback scores.

In modern HR departments, this data is often analyzed using HR analytics. This is the process of using HRIS data (Human Resource Information Systems) to identify trends, such as the relationship between job satisfaction and staff turnover. A well-functioning HR analytics program depends on the quality of the data that is collected and processed.  

Smileys in surveys make the collected data less reliable, which makes HR analytics less valuable. This makes it harder to take strategic HR decisions. 

What can you do with data in HR analytics?

Predicting staff turnover

By analyzing trends in feedback, job satisfaction and performance, HR teams can identify employees who may be planning to leave the company.

Assessing training effectiveness

Data on employee performance before and after a training program can be analyzed to measure the effectiveness of training.

Measuring employee satisfaction

HR analytics helps evaluate employee satisfaction, but only if the data is accurate and not skewed by smiley-based feedback.

Chapter 3: How smileys and emojis can skew your survey data

Although smileys and emojis seem like a simple way to get feedback, they carry a major risk: data bias. Smileys are subjective, which means people interpret them differently depending on their emotional state or personal experience. This leads to inconsistent answers, where one employee sees a smiling face as neutral while another interprets it as positive.

Smileys are also poor at capturing nuances in emotions and experiences. A simple smile or frown says little about the actual causes of satisfaction or dissatisfaction. This makes it difficult to analyze the feedback in depth and gain actionable insights.

Impact on HR analytics

The bias caused by smileys directly affects the accuracy of the HRIS data that HR teams use for analysis. As a result, important trends or problems can be overlooked. For example, if smiley-based feedback suggests that employees are generally satisfied, HR teams may wrongly conclude that there are no deeper problems in the team, such as workload or poor communication. 

Chapter 4: Consequences for data validity and reliability

The validity of data refers to the extent to which the data measures what it is supposed to measure. Smileys in surveys often lead to lower validity, because they cannot capture the complexity of experiences and opinions. The result is data that is hard to interpret and analyze. 

Another problem is the reliability of the data. When smileys are used, there is a greater chance of bias. Employees may give impulsive answers without really thinking, which results in inconsistencies. This reduces the reliability of the collected data and ultimately leads to misleading HR data. 

Example of how smileys affect data quality

HR surveys that use smileys to collect reasons for absence cannot distinguish between different reasons, such as health problems, workload or personal circumstances. As a result, valuable insights go unused and it becomes harder for HR to implement targeted solutions.

Chapter 5: Alternatives to smileys: how to improve the quality of your surveys

Fortunately, there are better alternatives for collecting reliable feedback from employees without the problems smileys cause. By using more structured and objective answer options, data quality improves and HR teams gain more meaningful insights. 

Recommended alternatives

Likert scales

These methods offer multiple answer options, such as “very satisfied” to “very dissatisfied”, which provide more nuance than smileys.

Open questions

Give employees the chance to explain their answers in their own words. This adds depth to the answers and helps HR better understand the causes of problems.

Multiple-choice questions

Offer structured choices, such as “Workload”, “Lack of development opportunities” or “Poor communication”, to get clearer insight into the reasons behind feedback.

Focus on HR

By using more structured data, such as Likert scales and open questions, HR teams can get more detailed feedback, which leads to better HR analytics and valuable insights. This not only improves the quality of data, but also helps organizations support their employees better. 

Conclusion

Smileys in surveys may seem accessible and attractive, but they often lead to poor data quality and less reliable HR insights. For HR teams looking for sound, reliable feedback, it is crucial to choose more objective methods that deliver in-depth, usable data. 

By using alternatives such as Likert scales, open questions and more structured answer options, you can significantly improve the data quality of your surveys and make better decisions based on solid HR data. 

Would you like to improve the data quality of your HR surveys? Discover how our tools help you collect reliable and actionable HR data without bias. Request a demo today and make the difference!

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