Statistical Data Analysis Assignment: 3
Analysis of Mental Health and Socioeconomic
Outcomes
Aakanksha Chauhan (s4071881)
Last updated: 30 October, 2024
Introduction and Overview
Introduction This series of visual analyses provides
an in-depth look at the interaction between mental illness, employment,
income distribution, and the prevalence of various mental health issues
within a sampled population. The goal is to understand how mental health
affects various aspects of life, including economic stability,
employment status, and common mental health struggles.
Data Collection Methodology Data was gathered
through a paid survey on Survey Monkey using general population
sampling, avoiding demographic targeting to prevent biases. The dataset
includes 334 responses, with 80 individuals identifying as having a
mental illness, reflecting the estimated prevalence in the general
population.
Plot Summaries
General and Regional Prevalence of Mental Illness: The initial
plots reveal a consistent prevalence of mental illness, aligning with
general population estimates. However, regional variations suggest that
location-specific factors may influence mental health.
Employment Status Related to Mental Illness: Data indicates that
individuals with mental illness are employed at significant rates, but
employment consistency varies compared to those without mental illness,
emphasizing the impact of mental health on job stability.
Age and Gender Distribution Among Mentally Ill: Young adults,
especially males, show higher rates of mental illness. This demographic
might benefit most from targeted mental health interventions.
Educational Attainment and Employment: Higher educational levels
correlate with better employment rates, even among those with mental
illness, highlighting education as a protective factor against
unemployment.
Resume Gap Lengths by Mental Illness Status: Individuals with
mental illness do not necessarily experience longer unemployment gaps
than their healthier counterparts, but variability exists, pointing to
potential episodes of ill health impacting employment.
Income Distribution by Mental Illness and Employment Status:
Mental illness, coupled with unemployment, tends to correlate with lower
income levels, suggesting that employment is a crucial factor for
economic stability regardless of mental health status.
Household Income Variations: This detailed breakdown illustrates
how mental illness and unemployment can significantly impact income
levels, with mentally ill unemployed individuals predominantly in the
lowest income categories.
Prevalence of Various Mental Health Issues: Anxiety, depression,
and tiredness are the most common issues, affecting a substantial
portion of the population and highlighting areas needing the most urgent
mental health interventions.
Regional and Overall Prevalence of Mental Health Issues Among Survey
Respondents
Distribution of Mental Illness Among Survey
Respondents
Figure Description - The pie chart illustrates the
distribution of survey respondents based on their self-identification
regarding mental illness. Of the total respondents, 24% identify as
having a mental illness, while the remaining 76% do not. This
visualization clearly delineates the proportion of individuals within
the surveyed population who recognize themselves as mentally ill versus
those who do not.
Analysis - This chart reflects the sampling design of the
survey which aimed to capture a representative cross-section of the
general population. The percentage of respondents identifying as
mentally ill (24%) aligns closely with the general population estimates
of mental illness prevalence, typically cited as 20-25%. This
consistency validates the effectiveness of the survey’s general
population sampling approach and the reliability of the data for further
analysis on employment impacts.
Regional Distribution of Mental Health Issues
Figure Description - The bar chart presents the percentage
of survey respondents who identify as having mental health issues across
various regions. The highest percentages are observed in the East North
Central (approximately 38%) and Pacific regions (around 32%), while the
lowest is seen in the New England region (about 18%).
Analysis - The visualization highlights significant
regional variations in the prevalence of self-reported mental health
issues. Regions like East North Central and Pacific show notably higher
percentages, which could suggest regional factors influencing mental
health or variability in awareness and self-reporting rates. Conversely,
regions like New England exhibit lower percentages, potentially
indicating better mental health outcomes or differing social stigmas
that affect self-reporting.
## Status Percentage
## 1 Mentally Ill 23.9521
## 0 Not Mentally Ill 76.0479
Demographic Insights: Employment and Age/Gender Distribution Among
Mentally Ill Individuals
Employment Outcomes Relative to Mental Health
Status
Figure Description - The mosaic plot visualizes the
relationship between mental illness status and employment, segmented
into four categories. The largest segment represents individuals who are
not mentally ill and employed (59%), followed by not mentally ill and
unemployed (15.3%). Among those identified as mentally ill, 17.1% are
employed, while 8.7% are unemployed.
Analysis - The visual highlights that a higher percentage
of individuals with mental illness are employed (17.1%) compared to
those who are unemployed (8.7%). This indicates that, while there is
certainly an impact of mental illness on employment, a significant
portion of those with mental illness are still maintaining employment.
Notably, the unemployment rate for individuals with mental illness
(8.7%) is lower than that for individuals without mental illness
(15.3%), suggesting factors other than mental illness might also
significantly impact employment status.
Age and Gender Distribution Among Mentally Ill
Individuals
Figure Description - The population pyramid illustrates the
distribution of mentally ill individuals by age and gender. The age
groups are segmented from under 18 to over 45 years, with counts
displayed for both males (dark green) and females (light green). The
most populous age group for both genders is 30-44, followed closely by
18-29. Notably, there are more males than females in the 18-29 and 30-44
age groups.
Analysis - This visualization effectively demonstrates the
age and gender distribution among individuals who identify as mentally
ill. The age groups of 18-29 and 30-44 are the most represented,
suggesting a significant burden of mental illness in younger to
mid-adulthood. The larger numbers in these age groups might reflect
higher stress levels or better self-reporting awareness among younger
adults. Additionally, the higher count of males in the most represented
age groups could point to specific socio-economic or cultural factors
affecting mental health perceptions or diagnosis rates among
genders.
Influence of Educational Attainment on Employment Among Individuals
with Mental Illness
Figure Description - The bar chart presents the proportion
of employment status categorized by educational levels among individuals
with mental illness. Each bar is divided into two segments, representing
the employed (dark green) and unemployed (light green) within each
educational category. The categories range from less formal education
(Some High School) to advanced degrees (Completed PhD).
Analysis - The visualization reveals a significant variance
in employment status across different educational levels for those with
mental illness. Notably, individuals with completed higher education
degrees (Masters and PhD) have a higher proportion of employment
compared to those with some college or less. The categories “Completed
Masters” and “Completed PhD” show a substantial majority being employed,
indicating that higher educational attainment might correlate with
better employment outcomes among the mentally ill.
Conversely, lower levels of education (e.g., Some High School, High
School or GED) exhibit a more considerable proportion of unemployment.
This trend underscores the potential barriers to employment that may be
exacerbated by lower educational attainment combined with mental health
challenges.
Resume Gap Length Comparison by Mental Illness Status
Figure Description - This boxplot illustrates the
distribution of resume gap lengths in months among individuals
categorized by their mental illness status. The two categories shown are
“Not Mentally Ill” and “Mentally Ill.” Each boxplot displays the median,
interquartile range, and potential outliers, indicating the variability
and central tendency of gap lengths within each group.
Analysis - The median gap length for both groups appears
similar, situated around the 25-month mark. However, the interquartile
range (the middle 50% of data) for those with mental illness is slightly
broader, indicating more variability in gap lengths within this group.
Both categories show outliers, with a few extreme cases showing gaps as
long as 100 months, particularly in the “Not Mentally Ill” group.
Socioeconomic Impact Analysis: Income Variations by Mental Health
and Employment Status
Analyzing Income by Employment and Mental Health
Status
Figure Description - This violin plot displays the annual
income distribution for individuals categorized by mental illness status
(Mentally Ill vs. Not Mentally Ill) and employment status (Employed
vs. Unemployed). The plot uses dark green to represent employed
individuals and light green for unemployed individuals, offering a
detailed look at income distribution patterns across these groups.
Analysis - The plot illustrates several key
observations:
Not Mentally Ill Individuals: The income distribution for
employed individuals without mental illness shows a wider range,
extending higher than their unemployed counterparts. The unemployed
group, however, shows a narrower distribution, indicating lower and more
uniform income levels.
Mentally Ill Individuals: For those identified as mentally ill,
the employed group exhibits a similarly broad distribution as seen in
the not mentally ill group, albeit slightly lower on the income scale.
The unemployed mentally ill group shows a very distinct and narrow
distribution at the lower end of the income spectrum, much lower than
any other group.
Household Income Variations Across Mental Health and
Employment Statuses
Figure Description - This histogram presents the
distribution of household income for individuals segmented by mental
illness status (0 for not mentally ill, 1 for mentally ill) and
employment status (0 for employed, 1 for unemployed). Each segment’s
x-axis is categorized by income ranges, allowing a comparative view
across different demographics.
Analysis - The histograms reveal several notable
patterns:
Income Range Popularity: The income distribution shows a
higher concentration in the lower income ranges across all categories,
particularly between $25,000 and $49,999. This suggests that a
significant portion of the surveyed individuals, regardless of mental
illness or employment status, falls within this income bracket.
Differences by Mental Illness and Employment Status:
- Not Mentally Ill and Employed: This group shows a broad distribution
with a prominent peak at $25,000-$49,999. There’s a substantial
representation across all income brackets, indicating a diverse economic
status.
- Not Mentally Ill and Unemployed: Peaks are much lower, showing
limited income levels primarily clustered in the lowest income
brackets.
- Mentally Ill and Employed: Similar to the not mentally ill employed,
this group also has a diverse distribution but with slightly less
representation in the higher income brackets.
- Mentally Ill and Unemployed: This group shows the most significant
concentration in the lower income ranges, with very few individuals in
the higher income brackets.
Prevalence of Various Mental Health Issues
Figure Description - The bubble chart displays the
prevalence of different mental health issues among a sample population,
represented as percentages. The size of each bubble correlates with the
percentage, providing a visual representation of the most common issues
within the group. The chart includes issues such as Anxiety, Depression,
Tiredness, and others, with Anxiety and Tiredness having the highest
prevalence at approximately 30%.
Analysis - High Prevalence Issues: Anxiety and Tiredness
both peak at 29.94%, indicating these are common among the sampled
individuals. Depression follows closely at 25.75%. These three
conditions notably impact a significant portion of the group, suggesting
they are critical areas for mental health interventions. Moderate to Low
Prevalence Issues: Panic attacks, lack of concentration, and obsessive
thinking show moderate prevalence, ranging from 12.57% to 15.27%.
Compulsive behavior and mood swings are less common but still noteworthy
at 8.68% and 11.38%, respectively.
## Lack.of.concentration Anxiety Depression Obsessive.thinking
## Total 51.00000 100.00000 86.0000 42.00000
## Percentage 15.26946 29.94012 25.7485 12.57485
## Mood.swings Panic.attacks Compulsive.behavior Tiredness
## Total 38.00000 49.00000 29.000000 100.00000
## Percentage 11.37725 14.67066 8.682635 29.94012
Conclusion
The data presented builds a compelling narrative about the challenges
faced by individuals with mental illness in maintaining economic
stability and securing employment. It underscores the critical need for
targeted interventions that address both mental health and economic
factors. Educational attainment emerges as a key protective factor,
suggesting that access to education could be vital in improving outcomes
for those with mental health issues.
The story told by these analyses emphasizes the necessity for a
holistic approach in addressing mental health. By understanding the
varied impacts of mental illness on employment, income, and daily
living, policymakers, healthcare providers, and community leaders can
better design and implement strategies that not only support mental
health but also promote economic stability and improve quality of life
for affected individuals. This comprehensive view fosters a deeper
understanding of the interconnectedness of mental health with
socio-economic factors and highlights the need for supportive policies
that are responsive to the needs of the mentally ill population.
Reference
The reference to the original data visualisation choose, the data
source used for the reconstruction and any other sources used for this
assignment are as follows: