Unraveling Fast Fashion: A Field Investigation

An on-the-ground, data-driven inquiry into the retail landscape of fast fashion — from shop floors in New York and New Jersey to the supply chains that stretch across the globe.

FORMAT: Group Fieldwork (4 members)

LOCATION: Any metro area

TOTAL POINTS: 100 pts

COMPONENTS: Field + Analysis + Presentation + Report

Fast fashion is one of the defining economic and environmental phenomena of our time. Clothing that once took months to move from design sketch to store shelf now appears in weeks — and is discarded almost as quickly. Behind the convenience and low price tags lie complex global supply chains, labor practices that vary dramatically across borders, and mounting environmental costs that rarely appear on any price label.

This project places us directly inside that system. Rather than reading about the fast fashion industry from a distance, we step into the stores themselves — observing, measuring, and questioning what we find on the racks. By comparing a premium department store with a discount retailer, we surface the contradictions and continuities that define how clothing is priced, sourced, and sold across different market tiers.

Through quantitative data collection, statistical analysis, and critical reflection, this project asks: What do the numbers on a clothing label — price, country of origin, material composition — actually tell us? And what story emerges when we read them together?

Learning Goals

By the end of this project, you will have developed skills and perspectives across three interconnected domains — quantitative, critical, and civic.

QUANTITATIVE LITERACY	CRITICAL GRAPH & DATA LITERACY	SOCIAL JUSTICE MATHEMATICS
Collect, organize, and analyze real-world data Design and execute a structured field data collection protocol	Identify how pricing, labeling, and marketing can obscure as much as they reveal	Examine the human cost behind price tags — wages, working conditions, labor law
Apply spreadsheet tools (SORT, COUNTIF, AVERAGE) to derive meaningful statistics	Question what a dataset includes — and what it leaves out	Analyze how country of origin and material choices reflect global inequality
Construct and interpret charts, graphs, and summary tables	Present visualizations that are accurate, clear, and free from misleading design choices	Develop evidence-based proposals for more ethical and sustainable retail practices
Recognize patterns, outliers, and limitations within a dataset	Situate local findings within broader industry statistics from external sources	Practice using quantitative reasoning as a tool for civic and ethical argument

Project Structure:

Data Collection

Working in groups of four, each team visits a minimum of two stores — one from each tier — to capture a cross-section of the NY–NJ retail fast fashion landscape. At least 100 items must be documented, spanning a minimum of five clothing types.

Store Selection

📸 Don’t forget to capture a group photo during the visit — it documents the fieldwork and adds a human dimension to your data.

What to Record

For each clothing item, collect the following details:

• Clothing type & category — e.g., shirt, pants, dress; casual, formal, athletic, luxury
• Brand information — brand name, local vs. global origin, prevalence across the floor
• Made-in labels — country of manufacture
• Material composition — cotton, polyester, blends, sustainable fabrics
• Pricing — listed price, discount status, seasonal trends
• Promotions & sales strategies — BOGO offers, flash sales, visible marketing
• Target audience — inferred demographics (age, gender, socio-economic background)
• Store layout & inventory — organization by season, style, or gender; variety and stock volume
• Additional contextual data — store location, date and time, % of items on sale, crowding level, high-footfall sections, and staffing

Feel free to record any additional quantitative observations that illuminate the financial or operational dynamics of the store. There is no ceiling on curiosity.

Data Analysis

Once the data is collected, move into Excel or Google Sheets to apply formulas (SORT, COUNTIF, SUM, AVERAGE, and others) and produce clear visualizations. The goal is to transform raw observations into interpretable findings.

Use graphs, charts, and pivot tables to make your findings visually accessible. Every visualization should serve the story you’re building — not just fill space.

In-Class Presentation

The presentation is where data becomes argument. Rather than reciting numbers, weave them into a coherent narrative about fast fashion — its scale, its geography, and its human cost.

Your narrative should address:

• The fast fashion industry in the country identified as the largest contributor in your dataset — its scale, growth, and economic role
• The quality of life for garment workers in that country, including wage levels and working conditions
• How local labor laws shape (or fail to shape) the conditions of fast fashion production
• Concrete strategies for greater sustainability and ethical accountability in the industry

Your presentation must draw on your own dataset and at least two external sources to situate local findings within the global picture.