This page documents how Fomofiles collects, analyzes, and publishes behavioral data. It exists to make the system auditable, replicable, and accountable.
This research operates as independent survey research with built-in ethics review mechanisms. As anonymous survey research collecting non-sensitive behavioral data from adults (18-25), this project qualifies for exemption under standard research ethics guidelines for minimal-risk studies.
Ethics Review Process:
Ethics Inquiries: ethics@fomofiles.in
For institutional collaborations requiring formal IRB approval, we provide complete documentation packages including methodology, consent procedures, and data governance protocols.
To prevent authority concentration and maintain structural integrity, Fomofiles operates through three independent layers:
Holds raw survey responses. Cannot interpret or publish. Enforces anonymization and access controls. Provides aggregate data only to Layer B.
Analyzes aggregate data. Cannot access individual responses. Documents methodology and limitations. Submits insights to Layer C with full context.
Publishes insights. Cannot modify analysis. Enforces citation guidelines and monitors for misuse. Maintains revision history.
No single person or role spans multiple layers. This structural firewall prevents data manipulation, interpretation bias, and authority creep.
Fomofiles uses repeated cross-sectional surveys to track behavioral patterns among Indian Gen Z (ages 18–25). Data is collected monthly, analyzed in aggregate, and published with full methodological transparency.
Target Population: Indian residents, ages 18–25
Sample Size: Target 500+ responses per wave
Frequency: Monthly waves (open for 2 weeks each)
Distribution: Organic reach, no paid promotion
Participation: Voluntary, anonymous, uncompensated
We do not use scraped data, purchased panels, or third-party datasets. All insights derive from direct survey responses.
Core questions remain unchanged across waves to enable longitudinal comparison. New questions may be added but never replace existing ones mid-series.
Questions avoid leading language, emotional triggers, or brand references. Phrasing tested for clarity across education levels and English proficiency.
Multiple choice for categorical data, numeric scales (1–10) for subjective measures, optional open-ended field for qualitative context. No forced responses.
Survey collection may be paused or revised if questions yield problematic responses that indicate harm, confusion, or ethical concerns.
When a survey is paused mid-wave:
Historical Record: All survey pauses, revisions, and retirements are documented in Revision History with timestamps and justifications.
This Data Management Plan is reviewed annually and updated as infrastructure or regulations change. All changes documented in Revision History.
Descriptive statistics (means, medians, distributions), cross-tabulation by demographics, time-series comparison across waves. No predictive modeling or individual-level inference.
Open-ended responses coded thematically. Multiple coders review for consistency. Participant language preserved in quotes. Analyst interpretation clearly separated.
Charts use muted academic colors, clear labels, and sample size notes. No decorative elements. Same variables maintain consistent colors across all charts.
Current tooling is deliberately simple and manual. This section documents known limitations and mitigation strategies.
Any use of automated or AI-assisted tools must be disclosed:
Triggers for moving beyond current tools:
Philosophy: Simple tools are acceptable for slow-growth research if risks are known, documented, and actively mitigated. Complexity is added only when simplicity becomes a liability.
For the project to endure and scale gradually, team processes and data practices must be robust. This section documents collaboration protocols, versioning strategies, and continuity safeguards.
Google Sheets and Datawrapper support live editing, enabling peer review and workload sharing. However, collaborative spreadsheets demand strict coordination to prevent data corruption.
Manual cleaning and coding (e.g., categorizing open-ended responses) is error-prone. Strict logging and dual verification mitigate risks.
Current toolset lacks built-in version control. Without audit trails, tracing or reversing mistakes is difficult. Explicit versioning and backup policies are mandatory.
Survey_Feb2025_v1.xlsxWritten SOPs for each major task ensure consistency and reduce onboarding friction. Even bullet-point docs on shared drive can prevent coordination chaos.
Current setup is sustainable for small, slow-paced initiative. As volumes rise, manual steps will bottleneck the team.
Survivability Principle: The system can function long-term only with disciplined teamwork. Regular communication, documented procedures, and conservative safeguards ensure reliability as the project grows.
Without overhauling the manual-first stack, carefully tested light automations can reduce error risk and labor burden. These are optional enhancements, not requirements. All automation must be documented, tested, and reversible.
Automation is added only when:
Core Principle: Add process and documentation, not flashy tech. Preserve the project's ethos of silence and careful growth.
These are potential automations to consider when manual processes become bottlenecks. None are implemented in Phase 1.
Before deploying any automation:
Some processes must remain manual to preserve research integrity:
No automation currently deployed. All processes are manual with documented procedures. This section exists to guide future decisions if/when manual workflows become unsustainable.
Any future automation will be announced in Revision History with full documentation of what changed and why.
Guiding Principle: Automation serves the research, not the other way around. Light, tested, documented enhancements are acceptable. Complex systems that obscure methodology or introduce new failure modes are not.
Every published insight includes documentation of how raw responses became conclusions. This audit trail ensures replicability and prevents cherry-picking.
Analysis logs are maintained internally and available upon request for academic verification. See Research page for published papers with full methodology appendices.
All analysis undergoes multi-researcher review to catch bias, errors, and unsupported conclusions before publication.
Peer checks occur at multiple stages:
When peer reviewers disagree on interpretation:
No Single-Analyst Publication: Insights analyzed and verified by one person only are never published. Minimum two-researcher review is mandatory.
All research has constraints. We document ours openly to prevent overinterpretation and guide appropriate use of insights.
Sample overrepresents Tier 1 and Tier 2 cities. Rural Gen Z perspectives underrepresented. Insights may not generalize to non-urban populations.
Survey conducted in English. Excludes non-English speakers. May miss cultural nuances expressed in regional languages.
Participants self-select by visiting this site. May attract more digitally engaged, research-interested individuals than general population.
All data is self-reported. Subject to recall bias, social desirability bias, and honest misreporting. Cannot verify accuracy of spending or mood claims.
For complete list of known gaps, see Known Unknowns.
No personal identifiers collected (no names, emails, IP addresses, device IDs). Responses cannot be linked to individuals. Anonymity is structural, not procedural.
Only aggregate data published. Minimum cell size of 30 responses before publishing any demographic breakdown. Individual quotes anonymized and contextualized.
Any team member may immediately halt activities that violate ethics, methodology, or data integrity. This authority supersedes all other priorities.
Activities That May Be Stopped:
Conditions for Stop:
Process:
Team may choose not to publish findings if publication risks harm, misinterpretation, or violates participant trust. Silence is documented with reasoning in internal logs.
Before accepting partnerships, funding, or platform changes, team evaluates compatibility with core principles. Growth opportunities that compromise methodology or independence are declined.
Fomofiles operates under the principle that behavioral research must serve public understanding, not commercial exploitation. We commit to:
For documented ethical decisions and dilemmas, see Data Ethics Case Archive.
Before participating, all respondents review comprehensive consent information covering all required elements for ethical research.
✓ Study Purpose: Understanding Gen Z behavioral patterns in India
✓ Procedures: 10-question survey, 2-3 minutes, anonymous responses
✓ Risks: Minimal risk (no personal identifiers collected, no sensitive topics)
✓ Benefits: Contributes to public research record, informs policy and education
✓ Confidentiality: Anonymous by design, no tracking, aggregate publication only
✓ Voluntary Nature: Can refuse participation, skip questions, or close survey anytime
✓ Contact Information: ethics@fomofiles.in for questions or concerns
✓ Rights Statement: Explicit participant rights listed below
You have the right to:
Due to anonymous design:
Consent is obtained through scroll-to-proceed architecture: participants must scroll through all consent information before accessing survey questions. Proceeding to survey constitutes informed consent.
Researchers, journalists, and educators may cite Fomofiles insights with proper attribution and context. For complete guidelines on proper use and prohibited misuse, see Citation & Misuse Guidelines.
This methodology document is versioned and changes are tracked publicly. For complete revision history including rationale for changes, see Changelog.
For methodology questions: research@fomofiles.in
For ethics inquiries: ethics@fomofiles.in
For data access requests: Include research proposal, intended use, and institutional affiliation