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Related Experiment Video

Updated: Jul 12, 2026

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

Working With Large Qualitative Datasets: Key Processes From an Implementation Study in a Complex Clinical Setting.

Marin Schmitt^1,2, Karen Kavanaugh^1,3, Karen Gralton¹

¹Department of Nursing Research and Evidence-Based Practice, Children's Wisconsin, Milwaukee, WI, USA.

International Journal of Qualitative Methods

|July 10, 2026

Summary

This study details managing large qualitative datasets from multiple sources in implementation science. Key strategies include structured data collection, adaptive procedures, iterative analysis, and dual analysis approaches for efficient insights.

Keywords:

case summary implementation science large datasets methodology qualitative

Related Experiment Videos

Last Updated: Jul 12, 2026

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

Area of Science:

Implementation Science
Qualitative Research Methods
Health Services Research

Background:

Collecting and analyzing large qualitative datasets is complex, with limited guidance for multi-source data.
Implementation science often involves complex qualitative data and analysis, facing challenges like multiple time points and rapid feedback needs.

Purpose of the Study:

To outline methods for collecting, managing, and analyzing over 400 qualitative data pieces from three sources in a large implementation study.
To discuss key processes that facilitated data management and analysis in a complex research setting.
To provide recommendations for future studies handling large, multi-source qualitative data.

Main Methods:

Utilized semi-structured interviews, meeting minutes, and open-ended survey responses from six neonatal intensive care units (NICUs).
Employed structure and consistency in data collection and management.
Implemented prompt responsive alterations to site-specific procedures.
Adopted an iterative analysis approach led by case summaries, incorporating both 'quick' and in-depth qualitative analysis.

Main Results:

Successfully managed and analyzed a large qualitative dataset (over 400 pieces) from diverse sources.
Four key processes (structure, responsiveness, iterative analysis, dual approaches) significantly aided data handling.
Demonstrated feasibility of rigorous qualitative data management in a complex implementation study.

Conclusions:

Effective management of large, multi-source qualitative data is achievable through structured, adaptive, and iterative methods.
The discussed strategies offer valuable insights for implementation science and other fields requiring complex qualitative analysis.
Recommendations are provided to guide future research involving extensive qualitative datasets.