Behavioral research infrastructure for a large-scale civic study in Greece
How Headway designed the platform, data quality framework, and analytical monitoring behind KMOP’s Gender–Career Implicit Association Test (IAT).
Project at a glance
- Client: KMOP – Social Action and Innovation Centre
- Study type: Gender–Career Implicit Association Test (IAT)
- Population: Greek-speaking adults
- Geographic scope: Greece (nationwide online sample)
- Duration: Jan 2026 – ongoing (Wave 1 completed; Wave 2 in progress)
- Data collection: Open online recruitment (media & organisational channels)
- Wave 1: N = 1,145 valid responses
- Wave 2: Ongoing (confirmatory phase)
- Outputs: Findings report, methodological working paper
The challenge
KMOP sought to implement a large-scale online study measuring implicit gender–career associations in Greece.
This required:
- deploying a research-grade IAT in a web-based environment,
- ensuring consistent performance across devices and participant conditions,
- and maintaining data quality and analytical reliability under open recruitment.
Unlike controlled experimental settings, online behavioral studies introduce variability in attention, timing precision, and participant composition — making quality assurance a central design challenge rather than a post-collection step.
What Headway delivered
Headway designed and implemented the full research infrastructure supporting the study, including:
- Web-based IAT platform
A browser-based reaction-time testing environment adapted for large-scale participation. - Greek-language test design
Localised stimuli and categorisation structure tailored to the Greek context. - Data quality framework
A modular system for monitoring and managing data quality throughout the study lifecycle. - Analytical monitoring and reporting pipeline
Continuous validation of incoming data and structured outputs for analysis and communication. - Recruitment-informed analysis design
Ongoing alignment between data collection and analytical requirements for subsequent phases.
Data quality framework
To support reliable online behavioral measurement, Headway developed and deployed a modular data quality framework operating across the full data lifecycle. The framework includes:
- Validity Gate
Applies predefined criteria to identify low-quality or invalid sessions and ensures reproducible data cleaning. - Phase Comparability Check
Tests whether pilot and live data can be combined without introducing systematic bias. - Tiered Precision Architecture
Classifies subgroup analyses based on statistical adequacy, constraining interpretation accordingly. - Temporal Stability Monitor
Tracks variation in results over time to detect recruitment-driven shifts in sample composition. - Recruitment Gap Analyzer
Identifies which participant groups are needed to improve analytical precision in subsequent phases.
This framework is designed to be applicable across different types of online behavioral studies beyond IATs.
Wave 1 findings
The first wave of the study included 1,145 valid responses from across Greece. Key observations include:
- An average implicit bias score (D-score) of 0.17, within the “slight bias” range
- Lower bias levels among women compared to men
- Lower bias levels among younger participants compared to older cohorts
These findings are reported with explicit consideration of subgroup precision and data quality constraints. Full findings available in the published report.
Outputs
- Wave 1 Findings Report
Summary of initial results from the first phase of data collection - Working Paper: Data Quality Framework
Methodological paper detailing the design and implementation of the framework - Ongoing Study (Wave 2)
Continued data collection supporting confirmatory analysis
Call to action
Need rigorous behavioral measurement for a study, evaluation, or Horizon Europe project? Headway designs and implements the platforms, data quality frameworks, and analytical workflows required to deliver reliable and defensible results.
Contact us to explore collaboration.