Mixed Measures

A local-first desktop workspace for mixed-methods research — qualitative and quantitative data in one project, with shared participants, codes, and memos.

Qualitative coding	Quantitative data	Integration canvas

---

The problem

Researchers who work across both interviews and surveys end up living in two disconnected tools: a qualitative coding app for transcripts and documents, and a statistics package for the numbers. The integration — the part that actually makes it mixed methods — happens by hand, in a separate document, with no shared participants, no shared codebook, and no through-line from a survey item to the quote that explains it.

Mixed Measures keeps both kinds of data in a single project. The same participant can be a survey respondent and an interview speaker. A code applies to a transcript segment and to an open-ended survey response. And an integration workspace (the Canvas) lets you write up findings with live excerpts, memos, and analysis results embedded inline.

It is built for independent consultants, program evaluators, and applied researchers who need rigor without an enterprise license — and who want their data to stay on their own machine.

Install

Recommended: download the latest signed installer for Windows or macOS from the Releases page and run it — no setup required. (Installers are attached to each v1.0+ release tag; if Releases looks empty during the initial launch, the first signed build is on the way.) Prefer to build it yourself? See Running from source.

First launch — a security prompt is expected. The installers are signed (and notarized on macOS), so the verified publisher is George Chavez. Because Mixed Measures is a new, independent app, your operating system may still show a first-run warning until the app builds up download reputation — this is normal and does not mean anything is wrong:

• Windows — if you see "Windows protected your PC" (SmartScreen), click More info → Run anyway, confirming the publisher reads George Chavez. (A code signature verifies the publisher and that the file hasn't been tampered with; it does not by itself suppress SmartScreen for a newly released app — that reputation is earned as more people download it.)
• macOS — the app is notarized, so it should open normally. If it doesn't, right-click the app → Open.

What it does

Bring data in

• Datasets — import survey/quantitative data from CSV, with automatic column-type detection (Likert/scale, numeric, percentage, binary, categorical), scale-pattern recognition, and N/A / refusal-label handling. Append additional rows from another CSV.
• Documents — import .docx, .pdf, and .txt files; they're auto-segmented (with page numbers and headings) for coding.
• Conversations — import transcripts as CSV (speaker- and timestamp-aware, e.g. exports from common transcription tools), with optional audio (.mp3, .m4a, .wav) attached for synchronized playback while you code.

Code and analyze qualitatively

• Three coding surfaces — for conversations, documents, and open-ended text columns in datasets — sharing one keyboard-driven coding layer.
• A structured codebook (codes, categories, universal codes), coded-segment tracking, excerpts and a Quote Board, memos, notes, and a quick-capture Scratchpad.
• Participants and speakers form a shared cross-source identity spine, so a person links across their survey record and their interview.
• Qualitative analysis: code frequencies, co-occurrence, a thematic saturation curve, group comparisons of code frequency, and a force-directed codebook network view.

Analyze quantitatively

• Descriptives (means, SDs, frequencies, proportions).
• Comparisons: Welch's t-test, one-way ANOVA with Tukey HSD post-hoc, and non-parametric Mann-Whitney U / Kruskal-Wallis H, with effect sizes (Cohen's d, η², ω², ε²).
• Correlation matrices (Pearson / Spearman) with scatter matrix and trendlines.
• Cross-tabulation with chi-square and Cramér's V.
• Reliability: Cronbach's alpha and split-half (Spearman-Brown corrected).
• Missing-data diagnostics: missingness summary, pattern view, and Little's MCAR test.
• Computed columns via a safe expression language ([Post] - [Pre], IF(...), MEAN(...), COALESCE(...), etc.).
• Crosswalk — harmonize variables that were measured differently across datasets into equivalence groups and analysis domains, then compute scale scores across instruments. This is the workspace's strongest differentiator for multi-instrument survey work.

Integrate

• The Canvas is a theme-based integration workspace with Writing and Spatial modes, rich-text prose (Tiptap), inline embeds of excerpts / materials / memos, typed relationships between themes, versioned snapshots, and a Convergence Matrix view for triangulating findings.

Export and reproduce

• CSV and Excel (.xlsx) of data and results.
• A runnable R script (.R) that reproduces the tool's own statistics in R (t-test, ANOVA + Tukey, correlations, chi²/Cramér's V, Mann-Whitney, Kruskal-Wallis, Cronbach's alpha, split-half, descriptives) — verified by a round-trip test that R's numbers match the app's.
• Canvas export to Word (.docx), HTML, and PDF, with charts embedded as images.
• .mmproject — a complete, database-agnostic project archive for moving a whole project between machines or instances.

What it is not

Being honest about scope:

• Not collaborative. A project is owned by exactly one researcher. There is no project sharing, no real-time co-editing, and no team workspace. For separate researchers on one shared computer, use separate operating-system accounts — each gets its own fully separate data. Multi-coder support with intercoder reliability (Cohen's kappa, Krippendorff's alpha) is on the roadmap.
• Not cloud-based. Everything runs locally against a local database. Moving a project between machines is a manual file transfer (.mmproject / backup).
• No AI in the product. The shipping tool currently has no AI/LLM features — it doesn't analyze your data, write your findings, or send anything anywhere. Every result comes from a conventional, documented method you can inspect and check (classical statistics via SciPy / statsmodels, not ML inference). AI assistance may be considered later, but not in v1.0. (AI coding tools did assist in building Mixed Measures — an honest note about development, distinct from what the product does.)
• Not a full statistical-modeling suite. Regression and factor analysis are not in v1.0; for analysis beyond the built-in descriptives and comparisons, export the R script and continue there.

Privacy & your data

• Fully local and offline. The app makes no outbound network calls — no telemetry, no analytics, no update checks, no external CDNs (fonts are self-hosted). The browser content-security-policy is locked to self.
• No accounts, no sign-in. The desktop app opens straight into your workspace as a single local researcher — there is no login screen or password. Your operating-system account is the security boundary; an optional inactivity timeout is available for shared computers.
• The database is encrypted at rest in the desktop app. Packaged builds encrypt the SQLite database with SQLCipher (AES-256), using a random per-install key held in your OS keychain (macOS Keychain / Windows DPAPI / Linux Secret Service) — a copied or synced database file is unreadable without it. If no OS keychain is available, the app says so plainly and runs unencrypted rather than storing a key insecurely. Two honest limits: inside a .mmbackup archive the database is ciphertext but documents/media are not, and encryption does not defend against software already running as your OS user — full-disk encryption (FileVault / BitLocker) is the answer there. Development builds run from source use a plaintext database for inspectability. See SECURITY.md for the full posture. If your data is sensitive, also store it on an encrypted disk / user profile and keep backups somewhere correspondingly protected.

Tech stack

Layer	Technology
Backend	FastAPI + SQLAlchemy 2.0 (Python 3.12)
Database	SQLite (Alembic migrations)
Frontend	React 19 + Vite + TypeScript
UI	shadcn/ui + Tailwind CSS v4
Data fetching	TanStack Query
Rich text	Tiptap
Charts	Recharts + d3-force
Statistics	SciPy + statsmodels (lazy-imported)
Parsing	python-docx, pdfminer.six, tinytag (all permissive)

Running from source

Most users should install the signed desktop build (see Install above). To develop, contribute, or build it yourself, run the backend and frontend together:

Prerequisites

• Python 3.12+
• Node.js 20+ (current LTS)

Backend (FastAPI, port 8000):

cd backend
python -m venv venv && source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt
alembic upgrade head        # create / migrate the local SQLite database
uvicorn app.main:app --reload --port 8000

Frontend (Vite dev server, port 5173, proxies /api to the backend):

cd frontend
npm ci
npm run dev

Open http://localhost:5173. The app opens straight into the workspace as a local researcher (no account setup), and you can create your first project.

Configuration is via environment variables (all optional; sensible local defaults). Common ones — see backend/app/config.py for the full list:

Variable	Default	Purpose
MM_DATABASE_PATH	dev.db	SQLite database file
MM_DATA_DIR	data	Parent of documents/ and media/
MM_BACKUP_DIR	backups	Backup storage
INACTIVITY_TIMEOUT_MINUTES	0 (off)	Auto-logout on a shared machine (e.g. 30)
COOKIE_SECURE	false	Set true when serving over HTTPS

Tests

# Backend
cd backend && source venv/bin/activate
pip install -r requirements-dev.txt
python -m pytest tests/

# Frontend
cd frontend && npm test

See CONTRIBUTING.md for the full development workflow, migration guidance, and dependency policy.

Backups & data safety

Qualitative coding is irreplaceable manual work, so the app keeps several backup mechanisms: automatic pre-migration backups, periodic auto-backups, and user-triggered .mmbackup archives (database + documents + media) with a validate-and-preview restore flow. Back up regularly, and keep a copy off the working machine.

License

Licensed under the Apache License, Version 2.0 — see LICENSE and NOTICE. Copyright © 2026 George Chavez.

Mixed Measures is provided "as is", without warranty of any kind (see the license for the full disclaimer). It is a research aid, not a certified statistical authority: verify analyses against your own judgment and, where it matters, an independent tool before relying on them in deliverables or decisions.

"Mixed Measures" is used as a common-law trademark of the project author.

Contributing & security

• Development setup and conventions: CONTRIBUTING.md
• Reporting a vulnerability: SECURITY.md