Best Practices for Data Collection and Synthesis in a Scoping Review

Best Practices for Data Collection and Synthesis in a Scoping Review


Introduction

Scoping reviews are powerful tools for mapping the breadth of research in a given field. But to achieve accuracy, transparency, and reproducibility, you need a structured and consistent approach to data collection and synthesis.

This article outlines best practices that will help you gather data efficiently, minimize bias, and present your synthesis in a way that is clear and useful for decision-making.


1. Plan Your Data Collection Process in Advance

Data collection in a scoping review is also referred to as data charting. It’s more flexible than in a systematic review, but planning is still essential.

Best Practices:

  • Create a data charting form before starting.

  • Pilot test the form on 5–10 studies to refine it.

  • Align charting fields with your PCC framework (Population, Concept, Context).

Common Data Fields:

  • Author(s), year, country.

  • Study design.

  • Population characteristics.

  • Concept/intervention studied.

  • Context/setting.

  • Key findings.


2. Use Two Reviewers for Reliability

Having two independent reviewers:

  • Reduces extraction errors.

  • Ensures consistent interpretation of the data charting form.

  • Minimizes selection bias.

Tip: Resolve disagreements through discussion or involve a third reviewer if needed.


3. Select Appropriate Tools and Software

Manual charting in Excel works for small projects, but for larger scoping reviews, consider:

  • Covidence – streamlines screening and data extraction.

  • Rayyan – assists with collaborative screening.

  • EPPI-Reviewer – handles complex data extraction needs.

  • RevMan – good for integrating with later systematic reviews.


4. Collect Qualitative and Quantitative Data

Since scoping reviews often include diverse study types:

  • Quantitative studies → extract key metrics and outcomes.

  • Qualitative studies → extract themes, narratives, and conceptual frameworks.

  • Mixed-methods → capture both forms of data.


5. Ensure Transparency and Auditability

  • Keep a data extraction log documenting changes.

  • Clearly state reasons for modifying the charting form during the review.

  • Use PRISMA-ScR reporting to describe your data collection process.


6. Best Practices for Synthesis

Scoping reviews do not usually conduct meta-analyses, but synthesis is still crucial.

a. Descriptive Numerical Summary

  • Number of studies by year, country, and study design.

  • Distribution of topics or concepts.

b. Thematic or Conceptual Grouping

  • Group findings into themes (e.g., types of interventions, outcome domains).

  • Use coding frameworks for qualitative synthesis.

c. Visual Evidence Mapping

  • Use tables, charts, or heat maps to display the evidence landscape.

  • Show relationships between concepts, populations, and outcomes.


7. Address Data Limitations

When summarizing, be upfront about:

  • Missing or incomplete data.

  • Variations in study quality or definitions.

  • Potential biases introduced by data availability.


Conclusion

Effective data collection and synthesis in a scoping review depend on planning, consistency, and transparency. By using structured charting forms, employing multiple reviewers, and presenting results visually, you ensure that your scoping review not only maps the literature accurately but also serves as a practical resource for researchers and policymakers.


Meta Title: Best Practices for Data Collection and Synthesis in a Scoping Review
Meta Description: Learn how to collect and synthesize data in a scoping review effectively with structured charting, transparent methods, and visual evidence mapping.