How do you draw the right conclusions from open-ended responses from employees?
This insurer conducted a qualitative study in which employees were asked questions on various topics such as communication, workload, engagement, and enthusiasm.
Within the company, ‘critical job families’ have been defined. These are roles critical to the continuity of business operations. The organization is interested in understanding the topics and needs that employees within these critical job families have, in comparison to non-critical job families. Another question is whether there is a difference between different business units, so they can actively implement policies accordingly. The goal is to gain insight into the current situation and focus on areas for improvement.
The research consists of a quantitative component supplemented with qualitative results. The questions are based on a 0-10 Likert scale, allowing employees to provide explanations for the scores they give.
The analysis of the results proved to be a significant challenge for this organization, much like many other companies. They struggled with processing and interpreting open-ended responses from employees in the surveys. Reading through and manually analyzing each response is not a viable option given the large volumes. Therefore, this valuable source of information often remains underutilized. This is where AnalitiQs comes in to assist.
The structured analysis approach of Highberg
To interpret the results, AnalitiQs conducted several steps and analyses. The goal was to compare and cluster topics that emerged from the qualitative input and then analyze them in detail. We naturally choose the most appropriate analysis methods for this purpose.
- Distinguishing between positive and negative responses
We examined differences between comments from employees who scored questions positively (a 9 or 10) and negatively (a 6 or lower).
- Descriptive analysis
A descriptive analysis is a high-level examination of the number of comments, including both quantitative and qualitative responses per question.
- Cluster analysis
We applied the K-means clustering algorithm to extract topics from the open-ended responses. This method characterizes and clusters themes and subjects relevant to the participants in the survey.
- Word count analysis
In this analysis, the words from responses to each question and theme were combined. Subsequently, we analyzed how frequently a word or combinations of words appeared in a positive or negative context.
Analysis techniques help determine next steps.
The analysis proved to be highly valuable for this organization, not only because it now has a clear focus on improving employee satisfaction but also because it is numerically substantiated. Without the extensive and varied analysis techniques, this wouldn’t have been the case.
Insight
By analyzing open-ended responses from employees, it’s now known precisely what satisfied employees appreciate and which areas the organization needs to improve. One of those themes is self-development. Based on the findings, the organization has implemented new policies to facilitate self-development. The same applies to themes such as workload and collaboration with managers. Aspects that can be extrapolated from open-ended responses are often overlooked. With AnalitiQs’ approach, this valuable source of information is well-utilized.
Continuous improvement
By conducting ongoing Voice of the Employee Surveys, it becomes quickly apparent which interventions have achieved the desired results. This allows for continuous improvement, rapidly bringing any issues and challenges to light.
