Employee turnover refers to the number of employees that leave an organization within a specified period and is expressed as a percentage. The exit could be due to resignations (both voluntary and involuntary), dismissals, retirements or non-certifications. These aspects are incorporated in the calculation of turnovers. However, internal events such as transfers and promotions are not included . Although monthly employee turnover may be used in some instances, most organizations prefer to use the annual employee turnover. This is because a longer period is required for the number of leaving employees to become big enough for most organizations . The increased numbers are also vital because they result in more meaningful patterns. The formula for calculating this is shown below;
Annual Employee Turnover (%) = Number of employees who left in a year × 100
(Beginning + end year number of employees ) / 2
Several statistics are useful in estimating the annual employee turnover. These include discrete variables, random variables, and the Poisson probability distribution. A discrete variable refers to a variable whose value is derived from counting. This statistic will be used because employees are countable at any time of the year. A random variable, on the other hand, refers to a variable whose value is numerical and is as a result of a random occurrence. The use of this statistic is justifiable since events such as resignations, dismissals, early retirements, and non-certifications, which contribute to employee turnover, are mostly random. Thus, a discrete random variable X, whose number of possible values is countable, will be used. The Poisson probability distribution is a discrete random variable distribution representing events that take place in integral points of time or space. This probability distribution is very useful in estimating. Since the exit of employees in the organization takes place over a period (yearly in this case), Poisson probability distribution is applicable. The statistical formula is as follows;
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ƒ(x) = µ x e -µ
x!
Where:
ƒ(x) = probability of x occurrences in an interval
µ = mean number of occurrences in an interval
e = 2.71828
To determine the estimates, additional data may also be useful. This would include the number of new employees, who the leaving employees are (either top performers or low performers) when the employees leave (timing), and the reasons for leaving. This data is crucial in not only understanding employee turnover but also making the necessary adjustments in the organization so as to reduce the turnover.
