ANL501 – Data Visualisation and Storytelling – Activities & Well Being Insights

Question 1

In recent years, growing policy interest has focused on how creative and physical activities, such as art, dance, music, and sport, can enhance the well-being of older adults. While formal lifelong learning has received extensive attention, emerging evidence highlights the importance of non-academic activities in supporting vitality, emotional health, and social connectedness. In ageing urban societies like Singapore, community-based programmes are increasingly seen as a means to mitigate loneliness and psychological vulnerability in later life.

You are a data analyst at a non-profit coalition tasked with providing evidence-based guidance on which types of activities, especially creative and physical ones, be most associated with improved well-being. The dataset, ANL501_Activity_Wellbeing_Seniors.xlsx, comprises survey responses from adults aged 60 and above, who provided information on their activity participation, attitudes, mental outlook, and subjective well-being.

This assignment uses a large-scale cross-sectional survey, modelled after validated international instruments, to evaluate how participation in different types of leisure and cultural activities relates to well-being. To capture well-being comprehensively, the dataset includes three validated constructs, each offering a distinct perspective:

Quality of Life (CASP-19)

Based on the CASP-19 scale, this measure assesses four domains central to ageing: Control, Autonomy, Self-realisation, and Pleasure. Respondents rated 19 life situations (e.g., “I feel that life is full of opportunities”) on a 4-point scale: Often, Sometimes, Not often, Never. The scale captures emotional, social, and existential dimensions of overall life quality.

2. Satisfaction with Life

This measure provides a cognitive evaluation of overall life circumstances and is widely used to benchmark life satisfaction across social and demographic groups. It includes five items covering current living conditions, optimism about the future, and past regrets. Respondents rated each item on a 7-point scale from Strongly disagree (1) to Strongly agree (7).

3.Psychological Well-Being (WHO-5 Index)

This index measures short-term emotional and psychological health, rated on 6-point scale from All of the time (1) to At no time (6), using five items such as:

“I felt calm and relaxed”
- “I felt cheerful and in good spirits”

Respondents rated each item on a 6-point scale: All of the time (1) to At no time (6).

Your task is to investigate how participation in selected creative and physical activities (e.g., dancing, singing, gym attendance, volunteering, group sports) relates to these well-being outcomes. Consider whether some activities are more strongly associated with particular well- being dimensions, and how these relationships vary by gender, mobility, or household structure.

Before you begin your analysis, assess the dataset carefully. The appropriateness of a data visualisation depends on the type of variables being represented. For example, although technically possible, plotting a line chart for a string variable is not meaningful. In R, variables may be stored as numeric (integer or double), strings, Booleans, or factors. It is therefore important to verify that each variable is correctly typed before proceeding.

Ensure that the dataset is structured properly. While data are often organised in a long format, you may need to pivot variables longer or wider to suit specific visualisation objectives. These transformations depend on how ggplot2 interprets variable structures in generating plots. Refer to lecture slides and R documentation to understand the expectations of different plot types.

Once the data have been imported and filtered, perform a sanity check to assess data quality. This includes reviewing the number of rows and columns, inspecting variable types and coding, and identifying missing or inconsistent values.

After confirming data readiness, you will develop visualisations to explore which types of activities are associated with different well-being outcomes. Based on your findings, you will write an op-ed titled:

“What Makes a Senior Happy? Exploring the Impact of Activities on Well-Being”

The op-ed, not exceeding 2,000 words, should be written for an informed local audience. The tone should be accessible but grounded in rigorous data analysis, similar to pieces published in The Conversation or The Straits Times Insight section. The 2,000-word guideline is flexible, but avoid unnecessary length.

Your submission must include:

A sanity check and data cleaning section, addressing missing data, incorrect inputs, and overall data integrity.
Clear and well-labelled visualisations (e.g., boxplots, bar charts, faceted plots) that examine:
1. How various activities relate to well-being indicators
1. How these relationships differ across demographics (e.g., gender, education, mobility, social connectedness)
An op-ed narrative, interpreting your findings and offering commentary for policymakers.
A Data Appendix, which must document:
1. Data cleaning and preprocessing steps
1. Variable generation and coding decisions
1. Explanation of visualisation strategies, with selected code snippets

Expert Answers on Above Questions on Data Visualisation

Sanity check and data preparation

Data Structure check: The data structure is checked by performing verification of the number of rows which suggest respondents and columns which indicates activities, demographics and well being scales.
Variable types: The activities are factors/ binary whereas the wellbeing scale is numeric and the demographic such as gender, mobility, household type are factors.
Scale handling: Higher values are considered the same as better well being and data related to aggregate CASP-19, satisfaction with life and WHO-5 are considered into composite scores.
Missing data: The focus was to inspect on missing NA patterns and perform deletion listwise.
Outliers and consistency: The focus was to perform a check of scales to match instrument specification and remove any kind of implausible responses.

Visualisation strategy

The ways in which various activities related to well being indicators are specified below with well labelled visualisations:
Boxplots: Activity participation compared to composite well being scores.
Barcharts: Mean well-being by activity type.
Faceted plots: Activity well being relationships are separated by gender, mobility level or household structure.
Key comparisons: The key comparisons are carried out in relation to creative activities such as art, music, singing as against psychological wellbeing, physical activities such as gym, sports and dance as against quality of life, and social activities like group supports, volunteering against life satisfaction and loneliness.

Key findings

The findings from the analysis shows creative activities are strongly related with psychological wellbeing and pleasure and self realisation. Physical activities show stronger linkages with overall quality of life and higher autonomy whereas social activities show positive connection with all well being measures.
Argument: What makes seniors happy? Exploring the impact of activities on well being.
Core message: Formal education alone is not responsible for well-being in later life, and the role of creative, physical and social participation is different whereas group based and socially embedded activities are the most protective against loneliness.
Policy implication: The policy should focus on funding the community arts and low impact physical programmes. With respect to mobility limited seniors, it is important to include design inclusive activities, and group oriented interventions should be prioritised over individualised programs.

This model answer is reviewed by Ms Siyu Chen, having technical expertise in fact and data based task.
Disclaimer: This answer is a model for study and reference purposes only. Please do not submit it as your own work.

Want a Full Worked Out Answer with References?

Tasks that include data visualisation and technical analysis require good analytical skills and understanding of appropriate techniques for better outcomes. With data analytics experts’ guidance, the task to analyse complex data becomes easy as they guide with each and every step from data cleaning to analysis and interpretation. Consult a professional data analytics expert from Singapore assignment help who can provide accurate and relevant guidance to improve your learning and data analytics skills to assist you in completing your task with confidence.