Monday, October 13, 2025

Beyond Brick and Mortar: Why Development is Empty Without Equity, Inclusiveness, and Human Rights

For decades, the story of "development" was often told in concrete and steel. It was measured in GDP growth, kilometers of roads paved, and megawatts of electricity generated. While these metrics are important, a more profound question has emerged: Development for whom?


A new skyscraper means little if it casts a shadow over a slum it helped displace. A booming national economy is an empty success if its wealth is hoarded by a tiny elite. True, lasting progress isn’t just about building infrastructure; it’s about building dignity, opportunity, and agency for every single person.


This is the essential paradigm shift: placing Equity, Inclusiveness, and Human Rights at the very core of development.


What Do We Really Mean?


Let's break down these powerful concepts beyond the buzzwords:


  •    Equity vs. Equality: Equality is giving everyone the same pair of shoes. Equity is giving everyone a pair of shoes that fits. It’s about recognizing that we start from different places and that overcoming historical and systemic disadvantages requires targeted support. It means actively prioritizing the needs of the most marginalized—women, people with disabilities, ethnic minorities, LGBTQ+ communities, and the ultra-poor.


  •   Inclusiveness: This is the practice of ensuring that all people, regardless of their identity or background, can participate fully and meaningfully in the development process. It’s not about inviting marginalized groups to the table as a token gesture; it’s about ensuring they have a real voice in designing the menu, cooking the meal, and sharing it fairly. An inclusive project asks: "Whose voice is missing?"


  •    Human Rights-Based Approach (HRBA): This is the framework that binds it all together. It asserts that development is not a charity but a right. Every person is entitled to the benefits of development—be it clean water, education, or a livelihood—not as a beneficiary, but as a rights-holder. This shifts the power dynamic, making governments and institutions accountable for upholding these rights.


 Why This Trifecta is Non-Negotiable


Ignoring these principles doesn't just make development unjust; it makes it ineffective and unsustainable.


1.  Equity is the Engine of Stability: When development is inequitable, it deepens social fractures, fuels resentment, and can lead to conflict. A society where a young person from a minority group has no access to quality education or a fair job is not just an unfair society; it's an unstable one. Equitable development, conversely, builds social cohesion and creates a more resilient foundation for lasting peace and prosperity.


2.  Inclusiveness Unlocks Hidden Potential: Excluding people isn't just a moral failure; it's a strategic one. When you fail to include women, you ignore half the world's talent and perspective. When you ignore people with disabilities, you design cities and services that are inaccessible to all. Inclusiveness is the ultimate innovation catalyst—it brings diverse problems and solutions to the forefront, leading to better, more creative outcomes for everyone.


3.  Human Rights Provide the Roadmap and Guardrails: A human-rights based approach provides a clear, legally grounded framework. It moves beyond vague goals like "improving lives" to specific entitlements like "the right to adequate housing" or "the right to participate in cultural life." This clarity allows communities to claim their rights and holds powerful actors accountable, preventing development projects from causing harm, like forced evictions or environmental degradation.


 From Theory to Practice: What Does This Look Like?


This isn't just abstract theory. It’s a practical guide to action:


  •    In Education: It’s not just about building more schools. It's about ensuring girls can attend safely, children with disabilities have accessible classrooms and materials, and curricula respect and reflect indigenous cultures and languages.


  •    In Urban Planning: It’s not just about building a new bus rapid transit system. It's about consulting with informal settlement residents on its route, ensuring stations are accessible for wheelchair users, and setting fares that are affordable for the city's poorest workers.


  •  In Economic Development: It’s not just about attracting foreign investment. It's about enacting laws that protect the land rights of small-scale farmers, ensuring women have equal rights to own property and access credit, and guaranteeing safe working conditions and a living wage for all.


The Path Forward: A Call for Conscious Development


The challenge ahead is to relentlessly ask the difficult questions of every policy, program, and project:


  •    Who is this for? Who might be left behind?
  •    Who decided this? Whose voices were included in the planning?
  •    What rights are at stake? Could this project inadvertently violate someone's right to housing, food, or a healthy environment?


Moving beyond the brick-and-mortar definition of development is our collective task. The goal is not just a world with less poverty, but a world with more justice. A world where development isn't something that happens to people, but something they shape and own—a process that honors their inherent dignity and empowers them to claim their rightful place in a shared future.


The true measure of our development is not in the height of our buildings, but in the depth of our commitment to one another.

Monday, April 28, 2025

Karl Pearson Correlation and Regression

 Karl Pearson Correlation Coefficient


 Simply known as Pearson's r, it is a statistical measure used to calculate the strength and direction of the linear relationship between two variables, usually denoted as x and y.


WEATHER CONDITIONS DATA

  • The correlation is very weak (close to 0). 

  • The negative sign means that as x increases,  tends to decrease slightly, but the relationship is extremely weak.


    REGRESSION

  • Regression is a statistical method that models and analyzes the relationship between a dependent variable and one or more independent variables .

     Linear regression is the relationship between one independent variable  x and one dependent variable y.



    Conclusion 

    In this data, there is a very weak, negative relationship between
    x
     and y. As increases, decreases slightly, but the relationship is not strong enough to make confident predictions.






    Wednesday, April 9, 2025

    Standard Deviation

     When analysing student spending habits, it’s not enough to just look at the average. Some students spend way more, others far less—so how spread out is this spending? That’s where standard deviation comes in. It helps us measure how much individual spending amounts differ from the average, giving a clearer picture of financial behavior on campus.


    What is Standard Deviation?



    Standard deviation is a measure of how spread out or dispersed a set of values is from the mean (average). A low standard deviation means that most values are close to the mean, while a high standard deviation indicates greater variability.


    Standard Deviation for Grouped Data



    We’ll use the standard deviation formula for grouped data:


    Where:


    • f = frequency
    • x = midpoint of the class
    • X = mean
    • N = total frequency
    • f(x - x})2 = squared deviation multiplied by frequency



    By calculating the standard deviation of student spending, we discovered just how varied their habits are. This measure adds depth to our understanding beyond just the average, helping campus businesses and planners see the bigger picture. In short, standard deviation tells us not just what students spend—but how differently they spend.


    Positional Avarages

    Breaking Down Student Spending: A Look Through Quartiles, Deciles & Percentiles

     

    In an effort to understand how students spend money on campus, we conducted a survey focused on their weekly expenditure. With the data collected, we applied various statistical tools—not just averages, but also quartiles, deciles, and percentiles—to better interpret the spread and behavior of the data. These measures help reveal patterns that a simple average might miss, such as how spending varies across different groups of students.



    University Survey Data For Student Total Expenditure




    Quartile
     

    A quartile divides data into four equal parts.


    • Q1 (First Quartile): 25% of the data lies below this point.
    • Q2 (Second Quartile): This is the median (50% of the data below it).
    • Q3 (Third Quartile): 75% of the data lies below this point.




     Decile

    A decile divides the data into ten equal parts.

    • For example, D1 marks the point below which 10% of data lies,
      D5 is the same as the 50th percentile or median,
      D9 marks 90% of the data.


     Percentile

    A percentile splits data into 100 equal parts.

    • For example, the 90th percentile (P90) means 90% of the data lies below that value.

    Use: Often used in test scores, rankings, and performance comparisons.

    By using quartiles, deciles, and percentiles, we gained a deeper understanding of student spending habits. For instance, the 90th percentile highlighted the top spenders, while the first quartile showed where the lighter spenders stood. These insights are vital for campus vendors, policymakers, or even students themselves, to better plan and respond to spending trends.




    Wednesday, March 12, 2025

    Survey Sampling Techniques

     Are Campus Businesses Struggling?


    By loycedashingdetails |13 March 2025


    Sampling refers to the process of selecting a subset of individuals or items from a larger population to gather information and make inferences about the entire population. Since it’s often impractical or impossible to collect data from an entire population, sampling allows researchers to work with a manageable group while still drawing accurate conclusions.


    Types of Sampling Techniques


    Sampling techniques are broadly classified into two categories: probability sampling and non-probability sampling.


    A. Probability Sampling


    In probability sampling, every member of the population has a known, non-zero chance of being selected. This ensures that the sample is more representative of the population, reducing bias.

    1) Simple Random Sampling

    Every member of the population has an equal chance of being selected.

    Example: Selecting 100 students from a university by drawing names from a list.


    2) Stratified Sampling

    The population is divided into subgroups (strata) based on specific characteristics (e.g., gender, age, income), and a sample is randomly taken from each stratum.


    3)  Systematic Sampling

    A starting point is chosen at random, and every nth member of the population is selected.


    4) Cluster Sampling

    The population is divided into clusters (e.g., neighborhoods or classrooms), and a random sample of clusters is selected. All individuals within the selected clusters .


    B. Non-Probability Sampling


    In non-probability sampling, not all members of the population have an equal chance of being selected. This can lead to sampling bias but is often quicker and more practical.


    1) Convenience Sampling

    Participants are selected based on their availability and willingness to participate.


    2) Purposive (Judgmental) Sampling

    Participants are selected based on the researcher’s judgment about who would provide the most useful


    3) Snowball Sampling

    Existing participants recruit new participants, creating a chain-like sample.


    4) Quota Sampling

    The population is divided into groups, and a fixed number of participants is selected from each group based on specific characteristics.


    🎯 Sampling Techniques – How We Collected the Data


    To ensure that the data accurately reflected the campus business environment, I used two key sampling techniques:


    1. Stratified Sampling


    Since campus businesses include various categories such as cafés, bookstores, and service providers, we divided the population into distinct strata (business types). I then randomly selected participants from each group to ensure all business types were proportionally represented. This approach provided a balanced and more accurate reflection of the campus business landscape.


    ✔️ Advantages of Stratified Sampling:

    Ensures that all groups within the population are represented.
    Improves the accuracy of results by reducing sampling error.
    Allows for meaningful comparisons between different business types.


    2. Convenience Sampling


    I also used convenience sampling by approaching business owners who were readily available during business hours. While this method helped me gather data quickly, combining it with stratified sampling ensured that the sample remained diverse and representative.


    ✔️ Advantages of Convenience Sampling:

    Quick and easy to implement, saving time and resources.

    Useful when a full list of the population is unavailable.

    Provides a practical way to gather initial insights.


    💭In conclusion, using stratified sampling ensured a balanced representation of diverse campus businesses, while convenience sampling enabled rapid data collection. Together, they provided clear insights into student spending and business revenue trends. These findings can guide strategic improvements for campus businesses to better adapt to evolving market conditions more effectively.










    Wednesday, February 26, 2025

    Survey Analysis Using Scales of Measurement


    Are Campus Businesses Struggling Economically ?


    By loycedashingdetails | 26 February 2025


    University businesses are a crucial part of campus life, but with shifting student spending habits and rising costs, many are struggling to stay afloat. To understand the situation better, we conducted a survey among campus business operators and students. Our findings revealed key trends, and we analyzed them using the four scales of measurement: nominal, ordinal, interval, and ratio.


    📊 Business Types (Nominal Data) – Who’s Selling What?


    The businesses surveyed included cafés, bookstores, clothing shops, food stalls, and service providers like printing and barbershops. This is an example of nominal data, where the categories represent different types of businesses, but there’s no inherent ranking between them. A café isn’t “greater” than a bookstore—each just falls into a different group.


    💰 Student Spending Habits (Ordinal Data) – How Much Do They Spend?


    We asked business owners about the average amount students spend per visit, and they reported the following:

    Less than ₹100

    ₹200–₹300

    ₹300–₹500

    ₹500–₹1000

    More than ₹1000


    This is ordinal data because the categories have a meaningful order (higher amounts indicate more spending), but the difference between categories isn’t necessarily equal. The gap between ₹200 and ₹300 may not reflect the same financial jump as between ₹500 and ₹1000.


    📉 Change in Sales Over Time (Interval Data) – Business Performance Trends


    We also asked business owners to compare their current monthly revenue to last year’s. The responses were recorded as percentage changes, such as:

    -10% (decline in sales)

    0% (no change)

    +25% (growth in sales)


    This is interval data because the differences between values are meaningful (e.g., a -10% drop is larger than a -5% drop), but there’s no true zero point—a 0% change doesn’t mean “no business at all,” just that sales remained the same.


    🛍️ Business Revenue (Ratio Data) – How Much Are They Earning?


    Business owners also shared their exact monthly revenue, ranging from ₹50,000 to ₹500,000 per month. This is ratio data because it has a true zero—a business making ₹0 means it has no revenue at all. Additionally, mathematical operations like ratios and percentages are meaningful (e.g., a shop earning ₹200,000 makes twice as much as one earning ₹100,000).


    🔮 What’s Next for Campus Businesses?


    From our analysis, we can see that student spending is shifting (ordinal data), different business categories are affected differently (nominal data), sales trends fluctuate (interval data), and total revenue changes significantly (ratio data). Understanding these measurements helps us better interpret the challenges campus businesses face.


    Moving forward, could lowering operational costs or offering student discounts help struggling businesses? Should the university provide more financial support? The numbers tell part of the story, but the next steps depend on how students and businesses respond to these changes.


    💬 What do you think? 

    Are student spending habits changing for good?  Drop your thoughts in the comments!



    Beyond Brick and Mortar: Why Development is Empty Without Equity, Inclusiveness, and Human Rights

    For decades, the story of "development" was often told in concrete and steel. It was measured in GDP growth, kilometers of roads p...