Data Analysis Assignment sample

Introduction

The United Kingdom (UK) imposed its first national lockdown on March 23, 2020, in reaction to the fast spread of COVID-19. According to the most recent publicly accessible evidence, this resulted in the situation deteriorating substantially one year later. After COVID-19 was able to escape and the neighboring area was put under lockdown, many people’s lives were irreparably impacted. Because of the demand to remain at home and keep a physical distance from one another, the lives of millions of people were badly impacted. Many individuals were unable to attend work or school for months at a time as a consequence of the storm’s affects, and many were forced to spend their time alone or with family and friends. As reported by the BBC, many people were startled to learn that the United Kingdom conducted three national lockdowns in the course of a single year. After the first closure, a considerable number of people’s lives were significantly disrupted; the virus is still far from being under control. –

Dataset and implications for strategic leadership

Following the results of a recent investigation, it has been discovered that this pandemic, coupled with countermeasures such as physical distance or company closures, have varying consequences on diverse social groups. According to research done in the United Kingdom, women and parents, for example, have been found to have larger declines in subjective well-being. Compared to other groups of immigrants, BAME immigrants (Black, Asian, and Minority Ethnic) were more likely than other groups to face economic hardship as soon as the first national lockdown was enforced. According to a research performed in the United Kingdom [4, the mortality rate among individuals with COVID-19 was greater among persons of color than it was among white people. This is precisely what the poet Damian Barr is alluding to in his poem, which argues that “we are all in the same storm, but we are not all in the same boat,” which is a play on words that implies “we are all in the same boat.”

The most current research on COVID-19 and COVID-induced metrics will be examined as a starting point, with a special emphasis on three dimensions of social inequality: gender, race/ethnicity, and educational success. We shall next explore the effects of COVID-19 and COVID-induced measures on people’s life. We will perform research to better understand how the COVID-19 pandemic is evolving in the United Kingdom, as well as how the government is implementing lockdown precautions, over the period March 2020 to April 2021. Following that, we will go over the data and its longitudinal design, which will allow us to make comparisons between information on the same individuals before and after the virus originally began spreading. In this post, we’ll display and explain our findings from a fixed-effect regression analysis that we completed. (Egbert, J2019)

What will be the long-term effects of the breakout of the COVID-19 virus?

The COVID-19 epidemic has gotten worse and worse over the course of the past year and half. COVID-19 waves have been found on a number of occasions in a variety of different nations. When COVID-19-induced behavior are exhibited, the ultimate objective of these behaviors is to prevent the virus from being transferred from one person to another (Cha,2018). This is accomplished by reducing direct human-to-human contact to the maximum degree practical. Others, on the other hand, may have a more substantial long-term influence on people’s choices and behaviors as a consequence of their specific attributes and features. It is simply a few instances of how people’s work habits have evolved over time that we may cite. Working from home and shutting down companies are two examples (Babbie,2022). A number of nations, including Australia, the United Kingdom, and the United States (US), have enacted lockdown measures that have resulting in large cutbacks in paid employment hours and profits. When enterprises collapse or individuals risk contracting COVID-19, people’s ability to outsource domestic labour falls, leading in an increase in the amount of time they spend performing unpaid domestic labour. As a consequence, individuals spend more time performing unpaid household tasks (Pallant,2020).

Data Analysis and visualization of the data set

People’s emotions were affected as a consequence of their engagement in the event. It is typical to exhibit indications of COVID-19 infection, including high fever, frequent coughing, and a persistent smell or taste that does not shift with the passage of time. Intensive care unit hospitalisation and potentially death are possible implications for patients in the most extreme conditions. According to the World Health Organization, the case fatality rate in the United Kingdom  is 2.1 percent (Liang,2019).  The growing number of new illnesses and fatalities that are publicised in the news on a daily basis is producing greater fear about the general public’s health and safety, which is driving more individuals to voice concern.

Many factors have been proven to have a detrimental affect on mental health, including job loss, financial difficulties, and social isolation. Shortly after the onset of the influenza pandemic, Australia , the United Kingdom [2, 16, 17, and, and the United States  all observed a reduction in subjective well-being, which was consistent with predictions (Purwanto,2021). The decrease in the rate of daily rise in COVID-19 occurrences, together with the relaxation of lockdown constraints, has resulted in a considerable improvement in the subjective well-being of individuals who have experienced them. As reported by Pierce et al.(2019), who evaluated the first five waves of the same UKHLS COVID research data as those used in this analysis, “between April and October 2020, the majority of people in the United Kingdom remained robust or recovered to pre-pandemic levels,” according to their findings. According to the findings of the study, about one-ninth of the population had “declining or chronically poor” mental health, which was determined by the researchers (Wiedermann,2018).

Women, men, people of colour, and non-degree holders will all endure childcare time shifts that are equivalent to those experienced by other groups of individuals in April 2020, according to our study findings. Mothers and those with higher levels of education seem to have had a bigger decline in childcare time in September, which should have been connected with pupils returning to school following summer break. This might imply that women and individuals with higher levels of education may have spent more time at home caring for children during the summer months (Denis,2019).

Further precise information about the outcomes may be found in Tables S2–S5, which also offer more information on the findings. When it comes to projecting wages, subjective well-being, cleaning time, and childcare time, the within-individual R-squares are quite tiny compared to the overall population. Similar concerns apply when measuring time spent with kids. Using fixed-effect regression to predict cleaning time and subjective well-being  has been utilised very seldom, with moderate within-individual R-squares being notably unusual.

 Even though only a small fraction of adults have altered their relationship and parental status as a result of the announcement of these data, their outcome variables—earnings, time utilisation, and subjective well-being—have changed substantially over the previous year. Additionally, the incorporation of new time-varying variables may have the potential to increase the explanatory power of the model. This could include information such as whether or not the person was furloughed, whether or not they engaged in the job retention plan, and whether or not they returned to their prior place of employment or finished their study. However, rather than concentrating on a particular policy or the spread of COVID-19, the objective of this article is to demonstrate an overall net effect of COVID-19 and its supporting measures on a single person. The time-varying factors listed above are not included in our study as we are interested in the trajectory of wages, time consumption, and subjective well-being during the various stages of the pandemic.

The extended pandemic and concurrent restraints on virus control that happened during the preceding year, according to our data, have resulted in long-term negative implications for incomes, working habits, and subjective well-being as a result of the viral control restrictions. In the course of the spread of COVID-19 and the associated statewide lockdowns, a multiplicity of patterns and measures arose, with the ramifications for labour wages, time utilisation, and subjective well-being changing depending on where you were and when the epidemic started.

Despite the fact that time use habits were less sensitive to consecutive rounds of pandemic, repeated lockdowns culminated in a new high point in anxiety levels among those who participated. Because of the elimination of the lockdown constraints, it was found that the effect of the lockdown measures on women and girls was no longer judged to be considerable. Increasing income gaps have evolved over time in particular socioeconomic categories, such as those of colour and those of white, and between those who have and those who have not received a bachelor’s degree.

Findings

This is why we need more exact and up-to-date studies on the progression of these health inequities during the course of the prior calendar year. This is particularly critical as the COVID-19 epidemic is continuously developing despite the installation of different containment devices. With respect to the current UK Household Longitudinal Survey (UKHLS), we looked at data collected before the first lockdown in March 2020, during the first lockdown from April to June 2020, during the first lockdown’s gradual easing (from June to September 2020), and during the second and third lockdowns in March and April of the following year (November 2020, and from January 2021 to March 2021). (November 2020, and from January 2021 to March 2021). We are able to give a more dynamic picture of how people’s work earnings, time utilisation patterns, and overall well-being altered as a consequence of the pandemic owing to our study, which is a contribution to the COVID-19 project. Furthermore, in order to establish whether and how these inequalities have changed over the previous year, further research was undertaken on a range of criteria, including race/ethnicity, educational attainment, and other aspects (Astivia,2019). However, despite the fact that past research has demonstrated that COVID-19 and related measures have drastically diverse affects on individuals from various socioeconomic backgrounds, the long-term repercussions of COVID-19 and analogous measures are still unknown. As of right now, we have no means of knowing if or not the implications of the first lockout have altered since it was implemented. More than a year after the commencement of the COVID-19 epidemic, the United Kingdom has witnessed three statewide lockdowns to tackle the illness. Researchers were confined in their analysis as they started with data that only spanned two time periods, such as the period immediately before and after the initial lockdown announcement. No one knows how COVID-19’s disproportionate societal effects express themselves in the community, despite the fact that the virus has been kept under lockdown on multiple times since its debut in 2009. A variety of social rules, including physical distance measurements and working from home, have changed on a weekly or even daily basis, with the consequence that people’s lives have been badly impacted as a result of the changes. In order to aid policymakers in their preparations for any future waves or pandemics, as well as in recognising the implications of the epidemic, it is crucial that we grasp the ramifications of this pandemic’s COVID-19 and the measures it developed.

Conclusion

When COVID-19 and its supporting indicators are extensively utilised, people from a broad range of social groups experience a variety of negative repercussions, with the quantity and speed of these consequences varying over time. These social disparities must be explored in order to identify how variables such as gender, educational level, and ethnic minority status have contributed to them, as well as how they may remain or even deteriorate in the long run as a consequence of these processes.

References

Astivia, O.L.O. and Zumbo, B.D., 2019. Heteroskedasticity in Multiple Regression Analysis: What it is, How to Detect it and How to Solve it with Applications in R and SPSS. Practical Assessment, Research, and Evaluation, 24(1), p.1.

Babbie, E., Wagner III, W.E. and Zaino, J., 2022. Adventures in social research: Data analysis using IBM SPSS statistics. Sage Publications.

Cha, J. and Kim, J., 2018. Analysis of fine dust correlation between air quality and meteorological factors using SPSS. Journal of the Korea Institute of Information and Communication Engineering, 22(5), pp.722-727.

Denis, D.J., 2018. SPSS data analysis for univariate, bivariate, and multivariate statistics. John Wiley & Sons.

Egbert, J. and Staples, S., 2019. Doing multi-dimensional analysis in SPSS, SAS, and R. Multi-dimensional analysis: Research methods and current issues, pp.125-144.

Liang, G., Fu, W. and Wang, K., 2019. Analysis of t-test misuses and SPSS operations in medical research papers. Burns & trauma, 7.

Pallant, J., 2020. SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.

Purwanto, A., Asbari, M. and Santoso, T.I., 2021. Education Management Research Data Analysis: Comparison of Results between Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS For Small Samples. Nidhomul Haq: Jurnal Manajemen Pendidikan Islam.

Wiedermann, W. and Li, X., 2018. Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS. Behavior Research Methods, 50(4), pp.1581-1601.

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