Self-Complexity Research Network Get Involved
Foundations, methods, and open science

Start Here

If you are new to self-complexity research, this is the fastest way to get oriented. Self-complexity examines how people organize identity across roles, attributes, and contexts — and how that structure relates to resilience, stress, and functioning.

Explore the Tools Read the Reboot Project
Orientation

A field that matters, with methods that need rebuilding.

This platform extends self-complexity research by standardizing measurement, enabling network-based representations, and supporting longitudinal and cohort analysis in a unified ecosystem.

  • Foundational work
    The conceptual and methodological anchors that explain where the field began.
  • Measurement and methods
    A growing specification layer for formulas, assumptions, and interpretation.
  • Tools and data workflow
    Browser-based systems for collection, analysis, and publication-ready output.
Foundational work

Core papers to start with

These works form the conceptual foundation of modern self-complexity research. The Reboot Project builds on and extends this framework.

Core paper

Linville (1985)

Self-complexity and affective extremity. A central starting point for understanding why self-structure might buffer or magnify emotional consequences.

Core paper

Linville (1987)

Self-complexity as a cognitive buffer. A classic extension linking structure of the self to stress-related outcomes.

Measurement critique

Rafaeli-Mor et al. (1999)

The meaning and measurement of self-complexity. Critical for decomposing quantity and overlap rather than over-relying on one legacy index.

Spatial and systems approach

Schleicher & McConnell (2005)

The complexity of self-complexity. A bridge toward spatial and structure-sensitive interpretations that align with contemporary visualization work.

Broader framework

McConnell (2011)

The Multiple Self-Aspects Framework. A broader conceptual scaffold for understanding organized identity across contexts.

Review context

Pilarska & Suchańska (2014)

A useful reminder that the field has often measured related but non-identical constructs under the same umbrella.

Measurement and methods

Standardized specification

A central limitation of self-complexity research has been inconsistent operationalization. The measurement layer addresses formulas, assumptions, interpretations, and versioning more directly.

Open Measurement Specification
Tools and data

From mapping to output

The ecosystem is built to move from browser-based identity mapping to cohort analysis, longitudinal summaries, and publication-oriented exports without requiring installation.

See the Toolchain
Open workflow

Transparent and reproducible direction

Planned components include shared specifications, reproducible analytic workflows, open datasets when appropriate, and future project-level version tracking.

Contribute to the Network
Tools and data

Fully browser-based tools

This platform provides connected tools for both data collection and analysis.

  • Self-Space App
    Map identity across time, define and rate self-aspects, and export structured JSON data.
  • Research Dashboard
    Compute self-complexity metrics, inspect longitudinal patterns, and generate publication-ready outputs.

No installation. No accounts. Fully local and privacy-preserving.

Launch App Open Dashboard
Open science commitment

Transparent and reproducible by design

This project is aligned with transparent, inspectable research practice. A dedicated project hub with version tracking, preregistration support, and shared resources can be layered in as the ecosystem grows.

Next layer: specifications, workflow notes, preprints, and future open-science links can all live here without changing the core structure of the site.
Study planning

How to use this in a study

A practical guide now explains study design positioning, sample flow, exports, methods language, and what to cite when using the platform.

Open the Study Guide
Methods language

Bridge tools to manuscripts

The study page is designed to help students and collaborators move from app use to cleaner methods sections, reproducible reporting, and stronger citations.

For new users

Start without guessing

Instead of piecing together assumptions from multiple pages, newcomers can now start with a concrete workflow for using the system in empirical research.

Where to contribute

We welcome contributions across theory development, measurement refinement, empirical studies, tool development, and cross-disciplinary applications.

Join the Research Network Open Measurement