Analytics case description.
Background to the study
Most people obsessively keep checking their emails or stay connected even when they are off the official working hours and this negatively affects their personal lives. There are different research on this subject that tries to authenticate the negatives effect of email checking on off-hours has in relationships with people, health, and overall well-being. From various research, the expectations to be available at work during non-office hours has adverse effects on the employee. Among the negative adverse effects are anxiety, distractedness, and stress among others. Additionally, a significant rise in stress levels can be caused by anticipation or wait for a work-related email. The anticipation brings anxiety where one will constantly monitor notifications on their devices if it is the one they are expecting and this hurts relationships. The XYZ company is revisiting its policy regarding expecting their employees to check their email outside business hours. In this paper, we intend to make a justification that relates stress levels to expectations of checking the email off working hours XYZ employees.
In this analysis XYZ questions or surveys to the subject are on a scale of 1-7. At 1 the email checking frequency is low and ultimately low-stress levels (NEVER) and 7 is the high frequency that is always checking emails. The variables here are email checking frequency and perceived stress levels and bellow is the graphical representation of the findings.
When the variables are graphically plotted, a cluster of distribution that is much concentration along the mean regions is witnessed. The mean regions are approximated at around 4 for the frequency of checking email and 6 for the approximation of mean for stress levels. From the graph, we can evaluate that, there is a direct relationship between the frequency of email checking and stress levels. At 4 which marks the highest frequency of checking the email, the stress level is at a maximum which is at 7. From the mean of data sampled among 200 employees the mean of both checking email frequency and stress levels was 3.51 and 5.51 consecutively which justifies the direct relation of the variables.
From the data analysis, the policy of XYZ Company regarding expecting its employees to check their work email outside business hours has far-reaching effects and shouldn’t be implemented. This is because it increases the stress experienced by employees that hurt their relationship with their families and affected their general wellbeing.
The observable outliers are 5.75 and 6.89 from the frequency of checking the email, and 9.00 and 10.00 for stress levels. These outliers can exist as a result of an error in data sampling, or part of the natural part of the population that exists. The best way of handling these outliers is by including them in the data analysis to give as the informative aspect of variables that are not captured.