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Background <p>Implementation science is moving from a proliferation of theories, models, and frameworks toward more rigorous empirical methods. However, quantitative implementation data remain highly heterogeneous, with non-standardized instruments, variable operationalizations, and context-specific adaptations that challenge comparison, replication, and cumulative knowledge building. While data harmonization has advanced in other disciplines, implementation science has yet to systematically adopt or adapt these methods, despite growing needs for cross-study synthesis.</p> Methods <p>This Methodology paper addresses the challenges of harmonizing quantitative implementation data across multiple studies. We outline categories of implementation research questions that would benefit from harmonization, characterize the complex nature of implementation data, and identify technical, conceptual, and analytic challenges important to the field. We then propose methodological strategies and solutions to address data harmonization issues in implementation science.</p> Results <p>Core challenges of harmonizing implementation data were identified: (1) construct validity; (2) measurement alignment; (3) psychometric gaps; (4) contextual heterogeneity; and (5) temporal differences in data collection. At the same time, harmonization offers substantial gains, including increased statistical power, the ability to test mediators and moderators, improved generalizability, and expanded capacity for mechanism-focused modeling. Potential solutions to these issues include the use of calibration datasets with multiple imputation and inverse probability weighting, latent variable and growth models to link non-equivalent instruments, time-to-event and stage-based approaches to address temporal misalignment, and the development of unified yet adaptable instruments and common data elements to support coordinated data collection across studies.</p> Conclusions <p>Data harmonization represents a critical pathway for advancing cumulative science in implementation research. Yet harmonization alone cannot resolve underlying conceptual inconsistencies; progress will require clearer construct boundaries, strengthened psychometrics, and flexible strategies that balance comparability with preservation of contextual meaning. Future efforts will benefit from implementation-specific harmonization methods, principled decisions about when harmonization is appropriate, and collaborative infrastructures to support data sharing and standardization across studies and settings.</p>

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Harmonizing quantitative implementation data across studies: challenges and proposed strategies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Jane Paik Kim,
  • Hannah Begna,
  • Lia Chin-Purcell,
  • Helene Chokron Garneau,
  • C. Hendricks Brown,
  • Mark McGovern

摘要

Background

Implementation science is moving from a proliferation of theories, models, and frameworks toward more rigorous empirical methods. However, quantitative implementation data remain highly heterogeneous, with non-standardized instruments, variable operationalizations, and context-specific adaptations that challenge comparison, replication, and cumulative knowledge building. While data harmonization has advanced in other disciplines, implementation science has yet to systematically adopt or adapt these methods, despite growing needs for cross-study synthesis.

Methods

This Methodology paper addresses the challenges of harmonizing quantitative implementation data across multiple studies. We outline categories of implementation research questions that would benefit from harmonization, characterize the complex nature of implementation data, and identify technical, conceptual, and analytic challenges important to the field. We then propose methodological strategies and solutions to address data harmonization issues in implementation science.

Results

Core challenges of harmonizing implementation data were identified: (1) construct validity; (2) measurement alignment; (3) psychometric gaps; (4) contextual heterogeneity; and (5) temporal differences in data collection. At the same time, harmonization offers substantial gains, including increased statistical power, the ability to test mediators and moderators, improved generalizability, and expanded capacity for mechanism-focused modeling. Potential solutions to these issues include the use of calibration datasets with multiple imputation and inverse probability weighting, latent variable and growth models to link non-equivalent instruments, time-to-event and stage-based approaches to address temporal misalignment, and the development of unified yet adaptable instruments and common data elements to support coordinated data collection across studies.

Conclusions

Data harmonization represents a critical pathway for advancing cumulative science in implementation research. Yet harmonization alone cannot resolve underlying conceptual inconsistencies; progress will require clearer construct boundaries, strengthened psychometrics, and flexible strategies that balance comparability with preservation of contextual meaning. Future efforts will benefit from implementation-specific harmonization methods, principled decisions about when harmonization is appropriate, and collaborative infrastructures to support data sharing and standardization across studies and settings.