Quantitative Data Analysis Report on Staff Turnover in UK

This report provides a quantitative analysis of the growing issue of high employee turnover in the United Kingdom. It includes a literature review, identification of dataset, visualisation of data, and findings and conclusions. The report highlights the impact of Covid-19 on employee turnover and the need for effective learning and rewards framework for employees.

Project 1 – Quantitative
Data Analysis Report

Table of Contents
1. Introduction to business research problem..................................................................................1
2. Literature review..........................................................................................................................1
3. Identification of Dataset...............................................................................................................2
4. Quantitative analysis of the dataset..............................................................................................2
5. Visualisation of data....................................................................................................................3
Summary of labour market statistics......................................................................................3
Labour Force Survey weekly estimates..................................................................................4
Coronavirus and the impact on payroll employment: experimental analysis.........................5
Unemployment rate by nationality (16+) - UK national: %...................................................6
Rising issue of Staff retention in public sector of UK............................................................7
6. Findings and conclusion..............................................................................................................7
REFERENCES................................................................................................................................9

1. Introduction to business research problem
The current research is grounded on the growing issue and challenge of staff turnover, which is
defined as an increase in the number of workers in the workplace who leave a business over a set
period of time (Conroy and et. al., 2021). It has been observed that the current marketing
uncertainty, combined with the worldwide epidemic of covid-19, has created issues and
challenges related to employee motivation and satisfaction, as well as ineffective employee
retention policies in the workplace, resulting in high employees turnover. As a result, the current
problem-based research project and quantitative analysis are centred on the growing issue of
high employee turnover in the United Kingdom.
2. Literature review
According to Juvitayapun, T., 2021, the phenomenon of employees ’ turnover is linked and
connected with the percentage of jobs and employees who leave and stop functioning within a
firm for personal or professional reasons. This has also been studied that the concept and actual
problem of staff turnover is linked to and correlated with greater operating costs for businesses,
as well as obstructions and hindrances to their development and profitability. Aside from that,
the main adverse effects and disadvantage of employees ’ turnover is a decrement in the overall
level of value of the employees. Communication issues has been observed to lead to workplace
chaos and confusion, resulting in feelings of demotivation and dissatisfaction, which leads to
higher employees ’ turnover. As a result, unhealthy company cultures premised on disparities, as
well as a lack of adequate retention programmes, including inadequate staff training, are the
primary reasons and processes that influence to staff turnover (Nguyen and Springer, 2021).
Besides that, it was observed that the increase in the company's business cost is a significant
impact and component of high staff turnover, as a result of rising expenditure on new staff
recruitment, as well as costs associated with job vacancy advertisements, awareness and
induction of new staff members, and so on, all of which contribute to an increase in the overall
operating cost (Scott, Waite and Reede, 2021). Furthermore, the primary cause of high employee
turnover is a communication gap and motivation, as well as an unhealthy employer brand and
inefficient consolidation and commitment policies.
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3. Identification of Dataset
This same steps that a researcher takes to gather and collect a piece of data which could be used
for additional analysis and review to gain a better understanding of the topic area and principle
are referred to as identification of data. The primary and secondary sources of data are the two
main sources from which the characterization of the publications can be made. It has been
witnessed that combining primary data from respondents is a difficult and thing process that
requires data collection directly from respondents (Wang and Zhi, 2021). As a result, the only
second sample of information set was used in the latest study. The use of key words has already
been required to ensure better selection of datasets based on the selected topic for cognitive
domain of the data set from the ONS site. Employee turnover, labour force, unemployment
numbers, staff retention, the impact of coronavirus, reason for turnover, and other key words
were used to select and identify the range of data. Using these key words helps and leads to a
better selecting of datasets that effectively meet and ensure the summary of the existing research
issue premised on employees ’ turnover (Donkor, Appienti and Achiaah, 2022).
4. Quantitative analysis of the dataset
There seem to be two types of data sets: quantitative and qualitative. Quantitative datasets are
numeric values, whereas qualitative datasets contain comprehensive as well as in information. As
a result, using a quantitative dataset tends to support improved fact group of people who work
and analysis. Additionally, the crystalline form of the data - set supports good use of tools and
techniques in the form of charts and graphs, tables, and other testing of hypotheses in order to
have good interpretation and implications of the results (Van Linden, Vandenhaute and
Zimmerman, 2021). The use of graphs and tables for the overview of the defined graphs that are
recognised and picked from the ONS website has been made in line with the existing research
project. Furthermore, the use of charts and graphs is legitimate and appropriate because it
endorses and facilitates a more thorough and extremely easy methodological approach and
review based on efficient and authentic lecture of data. Aside from that, the use of visualisations
tends to support and have a better visualisation of the identified facts in order to have a better
understanding and knowledge generation. The current review provides an evaluation of the key
defined dataset from ONS website, as well as the submit of an allowed to escape template that
mirrors an authentic set of data for clearer view and implications on the growing phenomenon of
employees ’ turnover (Marquardt, Manegold and Brown, 2021). The following is the labelled
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numeric dataset, which has been provided and reviewed in the new work via a spread sheet
report:
1. A01: Summary of labour market statistics
2. X07: Labour Force Survey weekly estimates
3. Coronavirus and the impact on payroll employment: experimental analysis
4. Unemployment rate by nationality (16+) - UK national: %
5. Is staff retention an issue in the public sector?
5. Visualisation of data
Use of the charts and interpretation of facts has been made to have better presentation and
visualisation of the selected and identified data set:
Summary of labour market statistics
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Interpretation:
On the basis of the above tables, it can be seen and deduced that the UK employment rate has
risen by 1.1 percent to 75.6 percent during the quarter (Main figures: Employment and
unemployment rate, 2021). During the most recent three-month period, full-time employees were
the driving force behind the raise in the employment rate. While the number of part-time staff
members fell sharply as during coronavirus (COVID-19) superbug, those weathered 16 to 24
years old seemed to be largely due to the increase in industrial inactivity comparison to the prior
three months. From December 2021 to February 2022, the job market reached a new high of
1,318,000.
Labour Force Survey weekly estimates
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Interpretation:
This graph shows that during the coronavirus pandemic, those aged 5 to 17 years old were
primarily due to increases in economic inactivity relative to the earlier three period. But even so,
since early 2021, the count of discouraged workers people aged 16 to 24 has been shrinking, with
some of those aged 50 to 64 cruising attract media attention in institutional inactivity.
Furthermore, it has been observed and viewed that the UK employment in 2018 was between
75.6 percent, but following the Brexit issues and the outbreak of the Coronavirusvirus, the jobs
rate is expected to slow to 71.4 percent in 2019 and 70.2 percent in 2020. (Conroy and et. al.,
2021).
Coronavirus and the impact on payroll employment: experimental analysis
Interpretation:
In light of the above-mentioned bar chart, it has been determined that the coronavirus has an
important influence and impact on UK labour market statistics. According to the above
experimentally based data analysis, following the strike and scatter of the Covid-19, a decrease
in the average employee rate, as well as a negative in payroll, is seen after 2019, indicating a
staff shortage and supporting a higher level of employee turnover in the UK (Ali and Anwar,
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2021). It was also discovered that from August to October 2020, there was a total drop of 0.6
percent in UK nationals on RTI, as well as a 9.1 percent increase in foreign foreigners on RTI.
Unemployment rate by nationality (16+) - UK national: %
Interpretation:
It was discovered that there was an increase in the unemployment rate for individuals aged 16 to
24 in the United Kingdom, based on a comparison of over 297000 people who were
unemployed. Similarly, a rise in the unemployment rate for students aged 16 to 22 years was
observed in the United Kingdom, rising from 14.4% to 19.3%, reflecting the prevailing issue of
high employee turnover due to a lack of commitment and performance in employees (Scott,
Waite and Reede, 2021).
Rising issue of Staff retention in public sector of UK
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Interpretation:
The main reasons and factors that have been reviewed and found to be responsible for creating as
well as resulting the issue of high employee turnover are based on a lack of motivation, as well
as a lack of employee retention and engagement policies (Nguyen and Springer, 2021). As a
result, there was no correct strategy and implementation seen in the public sector that supports
employee training and development, as well as a lack of incentives, which is the primary reason
and cause of the rising issue of turnover rates. Based on use of the Annual population survey, the
above bar chart tends to reflect the rate and degree of employee retention inside this UK public
sector, which includes and includes teaching, nursing, social work, and other governmental
work. It has been discovered and discovered that from every 9-10 workers in within public sector
are based on continuing to work on a long term contract, while only a minority and small
paragraph of them are premised on having to work on a temporary contract.
6. Findings and conclusion
Based on current study and quantitative, it has been determined that the UK is experiencing a
higher level of employee turnover, which is manifested in the form of rising unemployment rates
and increased lost productivity due to covid-19 issues. Other than that, this has also been studied
and summarised based on current research that the covid-19 has resulted in a higher level of
unhappiness and dissatisfaction among employees, which is causing and supporting the dilemma
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of increased employee turnover. Finally, higher demotivation has been observed in employees
aged 16 to 24, which is exacerbating the problem of high employee turnover. As a result, having
an effective learning and rewards framework for employees is becoming increasingly important
and advised for businesses to combat high employee turnover. According to the above labour
static charts, the number increased to 3.9 percent during the coronavirus (COVID-19) pandemic,
while the economic inactivity rate increased by 0.1 percentage points to 21.3 percent. In addition,
the number of job vacancies increased to a new high of 1,318,000 from December 2021 to
February 2022, indicating a higher level of employee revenue.
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REFERENCES
Books and journal
Ali, B.J. and Anwar, G., 2021. Employee Turnover Intention and Job Satisfaction. Ali, BJ, &
Anwar, G.(2021). Employee Turnover Intention and Job Satisfaction. International
Journal of Advanced Engineering, Management and Science, 7(6), pp.22-30.
Conroy, S.A. and et. al., 2021. Pay volatility and employee turnover in the trucking
industry. Journal of Management, p.01492063211019651.
Donkor, F., Appienti, W.A. and Achiaah, E., 2022. The Impact of Transformational Leadership
Style on Employee Turnover Intention in State-Owned Enterprises in Ghana. The
Mediating Role of Organisational Commitment. Public Organization Review, 22(1),
pp.1-17.
Juvitayapun, T., 2021, January. Employee Turnover Prediction: The impact of employee event
features on interpretable machine learning methods. In 2021 13th international
conference on knowledge and smart technology (kst) (pp. 181-185). IEEE.
Marquardt, D.J., Manegold, J. and Brown, L.W., 2021. Integrating relational systems theory with
ethical leadership: how ethical leadership relates to employee turnover
intentions. Leadership & Organization Development Journal.
Nguyen, T.D. and Springer, M.G., 2021. A conceptual framework of teacher turnover: a
systematic review of the empirical international literature and insights from the employee
turnover literature. Educational Review, pp.1-36.
Scott, J., Waite, S. and Reede, D., 2021. Voluntary Employee Turnover: A Literature Review
and Evidence-Based, User-Centered Strategies to Improve Retention. Journal of the
American College of Radiology, 18(3), pp.442-450.
Van Linden, C., Vandenhaute, M.L. and Zimmerman, A., 2021. Audit Firm Employee Turnover
and Audit Quality. Available at SSRN 3775116.
Wang, X. and Zhi, J., 2021. A machine learning-based analytical framework for employee
turnover prediction. Journal of Management Analytics, 8(3), pp.351-370.
Online:
Main figures: Employment and unemployment rate, 2021. [Online] Available Through:<
https://www.ons.gov.uk/ >.
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