## 20 Metrics in 20 Days- Day 7: Median Time in Role

Day 7 in our series of 20 consecutive posts on HR metrics: Median Time in Role

Median Time in Role tells you the midpoint of the distribution for employee time in role. By definition, half of the employees will fall above the median, half below.

Note: The average Time in Role may be preferred in some organizations and is perfectly acceptable although it may be substantially impacted by outliers. I would suggest calculating both and also plotting the entire distribution to get a sense of whether the median or the average (mean) makes the most sense. In practice, the difference is likely small.

Having a single number like the median is very helpful but as always, I strongly suggest you also plot your data using histograms and boxplots like the following:

set.seed(42)
exp <- rnorm(100, 5, 1.5)
hist(exp, breaks = 20, border = F, col = 'red3', main = 'Sample Distribution of Time in Role')
abline(v = median(exp),lwd =3) # adding line for median

library(ggplot2)
set.seed(42)
area <- c(rep('HR',100), rep('Sales', 100))
exp <- c(rnorm(100, mean = 3, sd = .5), rnorm(100, mean = 8, sd = 2) )
boxplot(exp ~ area, col = 'red3', main = 'Sample Time in Role X Area')

To further illustrate the value of ploting your data, let’s take a look at a distribution with a median of 3.5. We’ll show both a histogram and a density plot just to highlight the easy flexibility of R.

### Using the Base R plots
set.seed(42)
exp <- c(rnorm(100, mean = 2, sd = .5), rnorm(100, mean = 8, sd = 2))
hist(exp, border = F, col = 'red3', breaks = 20, xlab = 'Years in Role')
abline(v= median(exp), lwd= 3)

## Density plot of the same data using ggplot2
library(ggplot2)
ggplot(as.data.frame(exp), aes(x = exp)) + geom_density(fill = '#00b233', alpha = .7) + xlab(label = 'Years in Role')

In this case we see that we actually have two distinct groups of experience levels, groups that we would never detect if we looked at the median or mean alone.

The lesson here? Use plots to get a better feel for the data and make sure your summary measures like median are telling what you think they’re telling you.

## Why You Should Care

If the vast majority of your employees have been in their role for less than a year, it’s a safe bet that they are still making their way along the learning curve. Things may go well in the long run, but in the short run managers and leaders should be aware that some growing pains are ahead. This may mean additional one-on-one time for supplemental training or even increased turnover if the workforce is both inexperienced and younger.

Conversely, if you find that your workforce is extremely experienced, it could signal the need for an influx of new talent with new perspectives or some additional internal movement to keep your talent challenged.

## Actions

There are no magic numbers for the best mix of “new to the role” and established competence. But looking at your distribution will help you identify possible issues with the experience structure of your workforce.

If there are structural issues, some actions to consider the following:

• Modifying your hiring patterns to achieve your optimum balance of experience in the role
• Increasing/ decreasing internal movement to balance established competence with professional development
• Establishing deliberate managerial practices that reflect the workforce needs (esp. growth and development) for those areas with a particularly high concentration of inexperience.