Interpreting the Right Data to Effectively Understand Employee Engagement – Pulse on the Industry Research
In this series of articles, Kantar Employee Insights is exploring the challenges HR Professionals encounter when attempting to use data to inform employee engagement initiatives.
There are many ways to interpret the results from a survey, ranging from basic descriptive statistics to complex inference statistics. With all the techniques out there, how do you know what is most effective for your data? In this article, we will explore common interpretation techniques and help you understand the pros and challenges associated with each.
What are some techniques for interpreting data?
Highest and Lowest Scores: A simple and easy to understand interpretation technique is to select the highest and lowest scores.
- The pro: is it is very simple for a manager to sort the data and discuss with the team.
- The con: the highest and lowest scores are not always the most important or impactful scores. For example, pay items tend to be the lowest scoring questions. However, research shows that giving all employees a raise will only provide a short-term gain in Employee Engagement scores.
Internal Norms: Many of our clients establish internal norms at the 50th percentile and Best-In-Class Norm (average of the groups scoring in the 70-80 percentiles), so managers can see how they compare to the rest of the organization.
- The pro: managers can understand how they benchmark compared to other groups across the organization and continue to push to achieve Best-In-Class.
- The con: small groups can screw the data at the top.
Normative Benchmarks: Comparing employee survey scores to other organizations can provide strong insights.
- The pro: organizations can benchmark their data by industry, region, etc. and narrow their action-planning items.
- The con: each organization is unique, and norms don’t consider this uniqueness. For example, we were recently conducting an Executive Presentation to a healthcare organization in the Southwest Region. They conducted a benchmark survey and had some of lowest engagement scores compared to our normative database. One of the senior leaders challenged the consultant on every organization in the normative database. The consultant responded with a basic question.
- Consultant: Tell me about your organization?
- Executive Response: Well we are a unique organization?
- Consultant: Then who do you want to be compared too?
Normative benchmarks are great starting points. However, there is always more to the equation needed to narrow key items that will have the biggest impact on Employee Engagement.
Changing Scores Year over Year: Starting with a strong benchmark survey followed with repeat measures is a great way to show gains within the organization.
- The pro: organizations can show gains and hold both management and employees accountable.
- The con: if too much time passes between surveys, employees can often forget the actions that have been implemented. Consider a nonprofit organization in the Midwest Region that conducted its first survey and then waited three years to conduct the follow-up survey. On their repeat survey the scores had minimal improvements, even though the organization had each manager create action plans in the system and held them accountable to follow through on the plans. After conducting focus groups, it was clear employees had forgotten a lot of changes that were implemented – highlighting the importance of regular measurement. For first time clients, Kantar encourages conducting a benchmark survey followed by a repeat survey in 12-18 months.
Key Driver Analysis: Conducting a key driver analysis can help pinpoint key items that will have the biggest impact on Employee Engagement.
- The pro: this analysis helps managerssimplify the data to understand which items to focus on during the action planning process.
- The con: the analysis can be complex and difficult to understand. Many survey vendors have predeveloped these analyses, and can provide custom analyses upon request. For example, Kantar developed an algorithm factoring the favorability scores, normative benchmarks, compared to the rest of the organization scores, and regression to determine key items.
How do I select the right data interpretation technique for my organization?
Determining the right data interpretation technique is a difficult task. Consider the pros and cons under each technique listed above. You can also ask your survey vendor what they believe the most appropriate technique is for what you are trying to accomplish. It’s important to always keep your end goal in mind. When you know what you are trying to accomplish, you will be more prepared to select the right interpretation technique to achieve this goal.