Statistical Analysis of Employee Satisfaction Levels

BUSINESS
STATISTICS
STUDENT ID:
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Introduction
The given report relate to a global company which is facing issues related to low satisfaction
level of employees. This is a issue which the company wants to address and hence has
conducted a survey amongst 300 employees resulting in sample data capturing the responses
of these employees. This survey data pertains a varied information which aims to measure the
satisfaction level of employees through various parameters besides capturing demographic
details. Thus, the objective of the given report is to conduct a statistical analysis in relation to
key elements related to satisfaction and providing recommendations for improvement of
employee satisfaction levels.
Problem Definition
The pivotal issue faced by the company is the waning levels of employee satisfaction. In
order to reverse this trend, the company has initiated various measures such as training
sessions. However, it is possible that for various actions taken by the company, there may be
negative effect. As a result, the company aims to understand the needs of their employees
better by considering the sample data so that appropriate measures may be taken in the future.
The primary focus is on the inferential statistics use which can provide useful conclusions
about the population of all employees based on the sample responses. However, some
descriptive statistics techniques have also been deployed so as to understand the relationship
between variables. The key descriptive techniques that have been used on the numerical data
are correlation analysis and regression analysis. But in case of categorical variables, the use
of inferential statistical technique particularly pertaining to hypothesis testing is more
common. The key objective behind statistical analysis is to outline the expected impact of the
changes in different variables on the overall job satisfaction of employees which would allow
for useful recommendations be made.
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Variance/Standard Deviation
The requisite calculations are based in Excel with the requisite output being illustrated here.
The variance in the context of satisfaction scores registered before training and after training
exhibits difference. Even though the post training average satisfaction score is higher but the
increasing variance implies that the impact of training on all employee is not the same
thereby resulting in differential improvement across employees (Eriksson & Kovalainen,
2015). However, there is no denying that training has led to improvement in satisfaction
across employees.
Covariance
The COVAR(Array1, Array2) function available in excel has been used for computing the
covariance between satisfaction score and happiness score visible in the sample data before
training has been organised. The covariance comes out as 0.23 which implies that the
underlying variables have a positive relationship. Besides, this also leads to the conclusion
that a higher satisfaction at job would also lead to higher happiness in life which implies that
satisfaction with job is critical (Flick, 2015).
Z score
Age
In order to facilitate the z score for the given variable, information from sample is required
which has been computed using Excel and summarised as follows.
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