A Dynamic Approach to Measuring performance in HR analytics

Employee performance is one of the key outcomes that we try to predict in HR analytics. In this article, we will explain why comparing average performance does not always give us the full picture. We will also present several methods that can be used to examine and understand changes in employees’ performance and show how they can help boost productivity. 

Introduction

Collecting data on employees’ performance is an important part of HR practice. Traditionally, performance has been approached from a between-person perspective in which levels of performance between employees or teams were compared. For example, you can compare the KPIs of different employees to determine who is performing better or worse than others. This approach is often used to determine work incentives and bonuses, as well as identify which employees need additional training and development.

While this between-person approach is useful to know what general tendencies of your employees are – such as who tends to be a high performer – it does not give us a full picture (McCormick, Reeves, Downes, Li, & Ilies, 2018). Employees’ performance changes over time and it depends on many different factors. 

Examples that influence performance are how the person feels physically and mentally on a given day, the environment and situations the person encounters, people around, or what kind of tasks and responsibilities they have. In other words, performance is determined by more than just employees’ skills and knowledge. Using HR analytics to identify factors and situations that trigger changes in performance can help to create an environment where employees and companies thrive.

Changes in performance

Imagine you’re looking at data from a sales company. There are two employees you’re looking at, Kate and Emily. They both had the highest number of sales last week – 48 in total, and the same average – 9.6 sales per day. Now you want to look at their performance more closely and here is what you see:

Although Kate and Emily had the same total number of sales last week, their patterns across the entire week are completely different. Kate had a very similar number of sales every day, ranging from 8 to 11. Emily had a very wide range of sales, going from 3 sales on Thursday to 21 sales on Tuesday. If you look only at their total sales or average per day, Emily’s and Kate’s performance is the same. If, however, you look at the patterns of their performance, they are very different. 

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What does it mean? That looking beyond total or average numbers can give us valuable information (Podsakoff, Spoelma, Chawla, & Gabriel, 2019). Some people have very stable levels, of performance, some don’t. Identifying these patterns helps to understand better when and why employees thrive and perform on their most optimal level. In what follows, we will talk you through how to: (1) identify which employees have stable levels of performance; (2) how to determine whether changes in performance are good for your employees.

How to identify stability in performance

As you can see in the example, some people, such as Kate, have very stable levels of performance, while others, such as Emily, vary in their performance levels a lot. 

To identify which employees have stable patterns of performance, you need a repeated measure of performance. So, having a yearly evaluation of performance will not give you information about the patterns that emerge over time. Instead, you need to evaluate performance more frequently and repeatedly. 

The frequency with which you collect the data depends on the type of job or industry you are looking at. For example, in a typical sales job, you can access objective data (i.e., number of sales) from every working day or a week. Then, you can simply calculate a standard deviation for each person. A standard deviation tells you how close the scores are to the mean. A low standard deviation indicates that the values are close to the mean, for example, Kate’s standard deviation is 1.14. A high standard deviation, on the other hand, indicates that the scores are not close to the mean, for example, Emily’s standard deviation is 8.32. 

Although you can calculate standard deviation by hand using the formula below, most programs used for analysis have that function available (Excel, R, SPSS, MATLAB).

However, for jobs that do not have clear, objective performance data available, the frequency of the data collection will be lower, as you do not have data already available. To capture the stability of employees’ performance, you can simply change the format of the questions that are used in the evaluation. 

In supervisory ratings of performance, for example, supervisors are asked to indicate to what extent items in the questionnaire describe the employee (e.g., ‘He/she performs tasks on time’), with answers ranging from ‘strongly agree’ to ‘strongly disagree’. Instead, you can ask supervisors to indicate how often that statement is true (Kane, 1986, 2000), for example – 60% of the time the employee performs tasks on time:

Such a format allows us to tap into the stability of performance, and it is more accurate and cognitively easier than traditional scales (Kane & Wohr, 2006).

How to identify the causes of instability

Once you determine that an employee has unstable patterns of performance, it is important to determine the cause of the instability. This can be done during a traditional performance evaluation meeting or via a questionnaire that employees can fill out. 

You can ask employees questions such as: ‘In what situations do you feel like you can perform best/ worst?’. It is also important to ask for examples of such situations, preferably from the past month, so that the employee can recall the details. Then, you can look into what the factors are that differentiate between situations where the person performed best vs. worst. For example, it might be related to the task (e.g., there are tasks and responsibilities that the person is naturally good at), related to the job characteristics (e.g., some people perform better when they have a high level of autonomy) or related to their personal life or well-being.

Once you have more information, you can look into what can be done to help the employee maximize their potential, such as changing some tasks and responsibilities, providing additional training, or offering mental support.

In conclusion, employees’ performance goes beyond an average level that can be compared between people. To better understand when and why employees thrive, it is essential to look at their patterns of performance and the reasons behind changes in these patterns. In this article, we walked you through how you can identify the stability of performance and how to determine why people perform well on some, but not all days. The key message here is that performance is a result of many different factors, and looking at the changes in performance can help to understand your employees better and create an environment where they can thrive.