A note on intersectionality and multidimensional data analysis in Housing Studies research

A note on intersectionality and multidimensional data analysis in Housing Studies research (information from perplexity.ai)

My question 1: "What is intersectionality all about in the context of housing studies research?"

The reply: "Intersectionality in housing studies research is about understanding how various social identities (such as race, gender, class, age, disability, and more) intersect to produce unique experiences and forms of housing insecurity or housing inequality. It emphasizes that housing issues cannot be fully understood by looking at single factors in isolation because people’s housing experiences and vulnerabilities are shaped by multiple and overlapping systems of oppression and discrimination. This approach helps to reveal differentiated housing needs, how structural inequalities combine, and the specific contexts in which housing insecurity takes place.

Researchers applying an intersectional lens in housing studies focus on the everyday lived experiences of those facing housing insecurity, recognizing how interlocking forms of oppression (like racism, sexism, ageism, ableism) create varied impacts on housing access and quality. Intersectionality also challenges one-size-fits-all housing policies by highlighting diverse individual and community needs, encouraging tailored and trauma-informed responses. It thus serves as a critical framework for addressing the complexity and multi-scaled nature of housing insecurity and for promoting more equitable housing security policies and programs.

In practice, intersectional housing research often employs community-based participatory approaches, involving both academic and community partners to reflect on and integrate intersectionality throughout the research process. This leads to deeper insights into how different populations, such as Indigenous peoples, women-led households, immigrants, older adults experiencing homelessness, and persons with disabilities, are differently affected by housing precarity and systemic inequities.

Overall, intersectionality in housing studies pushes scholars and policymakers to consider the social structures and power relations that produce and sustain housing problems, aiming toward more comprehensive and just housing solutions that recognize and respond to the complex realities of marginalized groups."

My question 2: "Is the multidimensional data analysis using MS Excel useful to study the intersectionality of the Hong Kong citizens and their home purchase confidence?"

The reply: "Yes, multidimensional data analysis using MS Excel can be very useful to study the intersectionality of Hong Kong citizens and their home purchase confidence. Excel’s pivot tables and data filtering features allow researchers to organize and analyze complex survey or census data by multiple dimensions such as age, gender, income, ethnicity, and housing tenure simultaneously. This capacity aligns well with intersectionality’s focus on how overlapping social identities and structural factors affect individuals differently.

By using Excel to analyze multidimensional data, one can explore patterns and relationships between different social categories and home purchase confidence levels, revealing nuanced insights about which demographic segments face unique challenges or show distinctive confidence trends. For example, it can help differentiate how gender intersects with income or ethnicity to influence housing confidence, helping to uncover disparities that could be masked by unidimensional analyses.

While MS Excel is accessible and widely used, it does have limitations for very large datasets or more advanced intersectionality modeling requiring complex statistical techniques. However, for many practical research projects on housing confidence in Hong Kong, Excel’s multifunctional analytic tools provide a powerful means to investigate intersectional effects and support evidence-based housing policy or academic research.

Thus, MS Excel’s multidimensional data analysis capabilities offer a useful, user-friendly approach to empirical intersectionality studies related to home purchase confidence among Hong Kong citizens, especially when supported by well-designed survey data or secondary datasets with rich demographic variables."

A collection of blog notes on using chatgpt for research purpose.