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Study planning and methods workflow

How to Use This in a Study

A practical guide for moving from identity mapping to analyzable data, interpretable metrics, and citable methods language in empirical research.

Launch App Open Dashboard Measurement Specification
Big picture

What this platform contributes to a study

The ecosystem is built to support a connected workflow: participants complete structured identity mapping in the self-space app, researchers import exported JSON files into the dashboard, and the dashboard computes metrics, longitudinal summaries, and publication-oriented outputs.

  • Data collection
    Structured mapping of self-aspects and attributes across time.
  • Data export
    JSON outputs designed for downstream analytics.
  • Data analysis
    Profile-based metrics, identity visualizations, and cohort summaries.
  • Research outputs
    Tables, figures, supplemental exports, and methods-facing documentation.
Best for pilot, observational, longitudinal, and intervention studies
When to use it

Strong fit for studies where identity structure matters

This platform is especially useful when your questions involve role organization, adaptation, self-concept change, spillover across domains, or the relation between identity structure and behavioral or psychosocial outcomes.

Fields may include psychology, health and kinesiology, education, leadership, business, coaching, performing arts, rehabilitation, and other applied domains.

Recommended workflow

From participant session to manuscript draft

Step 1

Collect mapped identity data

Participants use the self-mapping app to define self-aspects, assign attributes, and provide ratings across relevant dimensions and time periods.

Step 2

Export structured JSON

Journey files can then be retained as the canonical exported format for dashboard import, quality inspection, and aggregation.

Step 3

Analyze in the dashboard

Import the exported journeys to compute metrics, inspect trajectories, compare groups, and generate publication-oriented outputs.

Study design options

Common ways to use the system

  • Cross-sectional: compare identity profiles across groups or roles.
  • Longitudinal: examine change in mapped identity structure over time.
  • Intervention: test whether identity structure shifts following a program, training, or treatment.
  • Mixed-methods: pair quantitative profiles with reflection prompts, interviews, or qualitative coding.
Data handling

What to save and report

  • Retain original JSON exports as the primary structured data source.
  • Document inclusion, exclusion, and any data-quality decisions.
  • Report metrics as a profile rather than collapsing them into one global score.
  • Report version numbers for both the measurement specification and platform when relevant.
Methods language

Starter wording for a manuscript

Participants completed a browser-based identity mapping task using the Everythingist self-space platform. They defined self-aspects across relevant temporal periods and assigned attributes with accompanying ratings. Exported JSON files were analyzed using the associated research dashboard, which computes profile-based self-complexity metrics, visualization outputs, and longitudinal summaries aligned with the Self-Complexity Measurement Specification.

This wording should be adapted to match your exact protocol, population, and design.

What to cite

Cite the methods layer and the platform

Measurement specification
Mullen, S. P. (2026). Self-complexity measurement specification (Version 2.1). Self-Complexity Research Network. https://selfcomplexityresearch.org

Platform
Mullen, S. P. (2026). Everythingist self-space platform and research dashboard. Self-Complexity Research Network. https://selfcomplexityresearch.org

Use the specification citation when discussing metrics or formulas. Use the platform citation when discussing the app and dashboard as tools or infrastructure.

Best practice

Pair tools with the specification

The strongest methods section is one that links app use, exported data, dashboard analytics, and the measurement specification from the start.

For students

Reduce ambiguity early

This guide is designed to help students and collaborators stop guessing how the workflow fits together and instead work from a coherent, citable structure.

Next layer

Build stronger studies over time

As the ecosystem grows, this page can expand with exemplar designs, sample analysis plans, preregistration language, and worked examples.

Ready to move from concept to study?

Start with the tools, anchor the methods with the specification, and use the network if you want collaborators or feedback.

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