Data Extraction Table in Systematic Review Dissertation | Best Practices for Data Extraction Forms For Included Studies

What is Data Extraction Table in a Systematic Review?

In a systematic review, a data extraction table is a crucial tool used to systematically collect, organise, and synthesise data from multiple studies. It is an essential component that helps reviewers and extractors gather consistent information for subsequent data analysis and meta-analysis. By using a data extraction table, researchers can ensure they are gathering relevant and reliable information, addressing the research question effectively. Below is a deeper exploration of this process.

Understanding the Data Extraction Process

• A data extraction table is central to the systematic review process, as it facilitates the organisation of key details across multiple studies.
• Extractors use the data extraction table to collect information from various sources, including databases and full-text articles, ensuring all necessary details are included.
• The table is often designed based on the research question, aiming to focus on specific data points that contribute to the synthesis of evidence.
• Cochrane reviews, in particular, require a well-structured data extraction table to capture the essential elements from included studies in a clear and uniform way.
• The data extraction table allows the reviewer to easily identify the eligibility of studies, ensuring that only those meeting the criteria are included in the review.
• The process of data extraction typically involves reviewing the literature review and existing systematic reviews on your topic, highlighting important data for the final analysis.
• A comprehensive data extraction table can support the generation of high-quality results, especially when preparing for meta-analysis.

Importance of Data Extraction Forms

• The data extraction table is part of a data extraction form, which ensures consistency across reviewers.
• The form is designed to capture the most relevant information to collect for each study included in the systematic review.
• Having a clear and standardised data extraction table ensures that all necessary fields are filled in, such as study design, sample size, and outcomes.
• The table also helps in recording data on study eligibility, making it easier to check if each study meets the predefined criteria.
• JBI and other organisations recommend the use of a robust data extraction table to help avoid errors during the process and provide a reliable structure for data collection.
• This form is vital to ensuring that all included studies are correctly evaluated, especially when synthesising results for meta-analysis or other forms of data analysis.
• The data extraction table can be used to guide the synthesis of results, summarising findings across studies for easier interpretation.

Common Challenges in Data Extraction

• One of the main challenges in using a data extraction table is the eligibility of studies. Sometimes, studies may not report the necessary information, leading to gaps in the data extraction process.
• Reviewers may face difficulty in interpreting the quality of data across different study designs. A well-structured data extraction table can help standardise this process, but it requires careful consideration.
• Database searches may not always return relevant studies, and finding the full text of articles can be challenging, potentially limiting the data extraction process.
• In some cases, information may be missing from the studies, which can affect the overall synthesis or meta-analysis. Extractors must carefully document such issues in the data extraction table.
• Ensuring all relevant fields are captured in the table can be tedious, especially when dealing with large volumes of data. A spreadsheet format often helps in managing and navigating complex datasets.
• Despite these challenges, the data extraction table remains a vital tool for providing clarity and consistency in the review process. It helps avoid errors and inconsistencies that might compromise the review’s quality and outcomes.

How to Create an Effective Data Extraction Form?

Creating an effective data extraction table is a critical step in ensuring that all necessary data is systematically collected during a systematic review or meta-analysis. An organised and comprehensive data extraction table helps in capturing relevant information, ensuring the validity of the results, and saving time during the process.

Key Components of a Data Extraction Template

• Form to ensure consistency: The data extraction table should be designed to capture essential information consistently across all included studies. This helps to avoid errors and ensures reliability.
• Inclusion and exclusion criteria: Ensure that the data extraction table includes columns to record whether a study meets the predefined eligibility criteria, which is crucial for assessing whether the study should be included or excluded.
• Descriptive information: The data extraction table should include basic details like study characteristics, including author, year of publication, and sample size.
• PICO framework: When designing the data extraction table, using the PICO (Population, Intervention, Comparison, and Outcome) framework helps to identify what information to collect for a specific review.
• Characteristics related to the topic: The data extraction table should be tailored to the specific review area (e.g., systematic reviews of interventions) and should include fields for recording important variables, such as dependent variables and effect sizes.
• Reliability measures: The data extraction table should allow for the recording of reliability measures, which help assess the quality of the evidence collected.

Best Practices for Designing Data Collection Forms

• Use existing systematic reviews: When designing the data extraction table, consider reviewing existing systematic reviews on your topic to see what data points are commonly collected and how the data extraction table is structured.
• Assess for eligibility: Make sure the data extraction table allows for checking study eligibility based on the inclusion and exclusion criteria, helping to avoid unnecessary data collection.
• Reduce the number of included studies: The data extraction table should help data extractors prioritise studies that meet the eligibility criteria, focusing on the most relevant studies for the review.
• At least two reviewers: To ensure the validity and reliability of data extraction, the data extraction table should be reviewed by at least two reviewers to reduce bias and increase consistency.
• Use the PRISMA flow: Incorporating the PRISMA flow into the data extraction table will help track the selection process, ensuring transparency and compliance with systematic review standards.
• Save time: A well-designed data extraction table can significantly save time for the review team by providing a clear structure and avoiding redundancy in data collection.

Tools for Developing Data Extraction Forms

• Systematic review toolbox: Tools like the systematic review toolbox can provide templates and resources to help develop a data extraction table that is aligned with current standards.
• Software tools: Software tools like Excel or specialised systematic review software can be used to create a data extraction table, helping to automate some aspects of the data collection process.
• LibGuides at university: Many universities offer LibGuides that provide useful templates and guidelines for creating data extraction forms, ensuring the quality and consistency of the process.
• Cochrane Handbook: The Cochrane Handbook provides valuable guidance on designing data extraction tables for Cochrane collaboration reviews, ensuring that the table captures all relevant data required for evidence-based conclusions.
• Bettany-Saltikov: The work of Bettany-Saltikov offers insights into improving the validity of the results by using well-structured data extraction tables to ensure accurate and reliable data collection.
• School of Medicine: Institutions like the School of Medicine often offer templates for data extraction forms, making it easier for extractors to follow best practices for systematic reviews and meta-analyses.

By following these practices and using the right tools, researchers can create a comprehensive data extraction table that enhances the quality of systematic reviews and ensures the validity of the results.

What Tools are Available for Data Extraction?

Effective data extraction is the process of collecting and organising data from studies in a systematic review or meta-analysis. Several tools are available for creating and managing a data extraction table, each offering unique features for researchers. Below is an overview of the most popular tools used for this purpose.

Reviewing Popular Data Extraction Tools

• Systematic review software: Various tools can help extract data efficiently and accurately, such as Covidence, RevMan, and Excel.
• Covidence: Covidence is a web-based tool commonly used in systematic reviews. It streamlines the data extraction process by offering easy-to-use templates for creating a data extraction table.
• RevMan: RevMan (Review Manager) is another widely used tool that integrates data extraction features, helping researchers manage the data extraction table and assisting in the synthesis of results.
• Excel: Excel is a more flexible, yet simple tool for creating data extraction tables. Many researchers choose Excel for its ability to customise the structure to their specific review area of interest, including scoping reviews and cohort studies.
• Review team on the extraction: The data extraction table can be customised by the review team to ensure it captures the relevant data specific to the review topic. Extractors may adjust the table to focus on various categories, such as study design, sample size, and outcomes.
• Forms are used: Data extraction tools typically include forms for easy data entry, helping the review team stay organised while collecting data from different studies.

Comparing Systematic Review Software: Covidence vs. RevMan

• Covidence:
  • Designed to simplify the data extraction process.
  • Allows at least two reviewers to collaborate on data extraction, ensuring the reliability of the process.
  • User-friendly interface that streamlines the creation of a data extraction table.
  • Particularly useful for larger teams working on systematic reviews and meta-analyses.
  • Extractors can easily track progress through the results section of the review.
• RevMan:
  • Developed by the Cochrane Collaboration, RevMan is a powerful tool specifically for systematic reviews and meta-analyses.
  • It provides templates for creating data extraction tables and is particularly effective for complex reviews involving large amounts of data.
  • However, it may require more training to use effectively compared to Covidence, especially when handling detailed categories of data.
  • Best suited for methods group that require in-depth statistical analysis and reporting.

Using Excel for Data Extraction: Pros and Cons

• Pros:
  • Excel offers high flexibility for designing a data extraction table, allowing the reviewer to adapt the table structure to suit the specific topic and categories of the review.
  • Extractors may prefer Excel for simpler reviews or when working with single reviewers.
  • Excel allows easy customisation of the data extraction table based on the inclusion and exclusion criteria, ensuring that only relevant studies are included.
  • Excel is widely accessible and does not require additional software or subscriptions, making it a popular choice for smaller review teams or individual extractors.
  • Custom formulas and pivot tables can be used to quickly analyse and summarise the collected data.
• Cons:
  • For larger review teams, Excel may become cumbersome, especially when trying to track revisions or ensure consistency across multiple users.
  • Excel lacks built-in integration for systematic review tasks such as randomisation or risk of bias assessments, which can make it less efficient compared to specialised software like Covidence or RevMan.
  • It requires careful management to ensure that at least two people are involved in the extraction process, reducing the risk of bias.

Selecting the right tool for your data extraction table depends on the scale of your review, the complexity of your data, and the resources available to your team. Whether using Covidence, RevMan, or Excel, each tool has its strengths, and the right choice will depend on the review team on the extraction categories and the review’s area of interest.

How to Ensure Reliability in Data Extraction?

Ensuring reliability in data extraction is crucial for the quality of a systematic review or meta-analysis. A well-constructed data extraction table can significantly contribute to the accuracy and consistency of the data collected. Below are strategies for ensuring reliability during the data extraction process.

Establishing a Review Team for Data Extraction

• Multiple extractors: To improve reliability, it is essential to have at least two people involved in data extraction. Having multiple extractors helps identify inconsistencies in the data extraction table and ensures that data entry is consistent across studies.
• Team on the extraction categories: A review team should work together to decide on the categories to be included in the data extraction table, based on the research question. This collaboration helps ensure that all team members are aligned in what data needs to be extracted.
• Clear roles: Clearly define roles for each team member, ensuring that responsibilities for checking different sections of the data extraction table are assigned. This will help ensure that the team remains focused and avoids errors.
• Training and guidelines: Provide training on how to fill in the data extraction table accurately. Refer to guidelines from authoritative sources, such as the Cochrane Handbook or libguides at university, to standardise the extraction process.

Methods to Minimize Data Entry Errors

• Standardised templates: Ensure that a clear, well-structured data extraction table is used, with predefined categories and variables to reduce the likelihood of errors in data entry. A systematic review toolbox or software like Covidence can help in creating these templates.
• Regular calibration: To minimise errors, extractors are using regular calibration sessions where they review the data extraction process together and resolve any issues before continuing.
• Use of software tools: Employ tools like Excel or RevMan to manage the data extraction table more efficiently. These tools often include features that help flag inconsistencies or errors, which can be particularly helpful when dealing with large datasets.
• Clear definitions: Provide clear definitions for each field in the data extraction table to ensure that all team members understand the meaning and scope of each variable. This will reduce ambiguity during the extraction process.

Assessing Discrepancies in Extracted Data

• Double-checking extracted data: Discrepancies in the data extraction table can occur when different extractors interpret data differently. In such cases, the team should meet to discuss the discrepancies and reach a consensus on the correct interpretation.
• Cross-checking with the original study: If discrepancies are identified, cross-check the data against the full text of the study. This process helps ensure that the data recorded in the data extraction table accurately reflects the information from the original source.
• Consistency checks: Use PICO (Population, Intervention, Comparison, and Outcome) or similar frameworks to assess the applicability and consistency of the extracted data. This helps ensure that each data point included in the data extraction table is relevant and fits within the review’s focus.
• Document discrepancies: It is important to document any discrepancies found in the data extraction table and how they were resolved. This improves transparency and ensures that future users can understand how the data was treated.
• Least two reviewers: Discrepancies can be resolved by having at least two reviewers assess the same data entry. If there is still disagreement, the review team can discuss and come to a decision, improving the reliability of the data extraction table.

By following these practices, you can improve the reliability of the data extraction table, reduce errors, and ensure that the data collected during your systematic review or meta-analysis is both accurate and consistent.

What are the Best Practices for Extracting Data from Included Studies?

Creating an accurate data extraction table is an essential step in synthesising evidence from systematic reviews and meta-analyses. By following best practices, you can ensure that the data extraction table is consistent, reliable, and captures all necessary information for your research question. Below are some best practices for extracting data from included studies.

Identifying Relevant Studies for Data Extraction

• Initial screening: Begin by conducting a comprehensive search of relevant databases to identify studies that meet the predefined eligibility criteria. This includes considering inclusion and exclusion criteria carefully when selecting studies for extraction.
• Assess eligibility: Use the data extraction table to record eligibility information from the studies, ensuring that only studies that meet the criteria for the review are included. The review team should work together to confirm eligibility, particularly in cases where study selection is ambiguous.
• Use of PICO framework: Apply the PICO (Population, Intervention, Comparison, and Outcome) framework to identify the most relevant studies for data extraction. This helps to focus the data extraction table on studies that answer the research question most effectively.
• Specific review topic: Ensure that the data extraction table captures data directly related to the topic to identify what information is most pertinent to the review. For example, if the review focuses on intervention effectiveness, the table should include details of the intervention and outcome measures.

Using the PRISMA Guidelines in Data Collection

• PRISMA checklist: The PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) are essential for ensuring transparency and completeness in data extraction. Use the PRISMA checklist to guide the creation of the data extraction table, ensuring that all relevant study characteristics are collected systematically.
• Standardising data: By following the PRISMA guidelines, extractors are using standardised forms and templates to ensure that the data extraction table captures the most important data elements consistently across all included studies.
• Documenting the flow: The PRISMA flow diagram is a useful tool for tracking the number of studies assessed for eligibility, included, and excluded. This should be referenced in the data extraction table to maintain transparency in the process.
• Ensuring applicability: The PRISMA guidelines also emphasise assessing the applicability of the data collected. The data extraction table should capture information on study quality and risk of bias to support valid conclusions.

Synthesizing Data from Qualitative and Quantitative Studies

• Qualitative and quantitative data: Data extraction may involve both qualitative and quantitative studies. Ensure that the data extraction table is designed to accommodate both types of data. For quantitative studies, include fields for sample size, outcomes, and effect sizes. For qualitative studies, include key themes, study designs, and participant descriptions.
• Integration of results: The data extraction table should support the integration of data from both qualitative and quantitative studies, allowing you to synthesise findings across different types of evidence. This is especially important in scoping reviews where you may include a diverse range of studies.
• Grouping studies: When synthesising, group studies by design or type of outcome (e.g., effectiveness, safety), ensuring that data is comparable across studies. The data extraction table can help organise data by these categories, simplifying the analysis process.
• Collaboration and consensus: In cases of discrepancies, the review team should discuss the data extracted and agree on the final entries for the data extraction table. It is important that extractors may consider conducting joint assessments when synthesising both qualitative and quantitative findings.

By following these best practices, you ensure that the data extraction table accurately captures all the relevant data from the included studies, contributing to a high-quality, evidence-based systematic review.

How to Document the Extraction Process in a Systematic Review?

Documenting the data extraction process is essential for ensuring transparency and reproducibility in systematic reviews. The data extraction table plays a critical role in this, but it’s also important to have a clear record of the extraction process itself. Below are key strategies for documenting the extraction process in your systematic review.

Creating an Extraction Log for Transparency

• Extraction log: An extraction log is a detailed record that tracks the data extraction process. This log should document each step, such as which studies were included, what data was extracted, and how discrepancies were resolved. It helps maintain transparency in the review process.
• Tracking changes: Whenever updates or changes are made to the data extraction table, these should be recorded in the extraction log. This includes noting when additional reviewers are involved or when data is re-extracted due to errors or missing information.
• Inclusion and exclusion decisions: The log should also document the reasons for including or excluding studies, based on the eligibility criteria. This ensures that the decisions made during the extraction process are well justified.
• Clear version control: If multiple versions of the data extraction table are created, maintain clear version control in the extraction log. This helps avoid confusion and ensures that the final dataset is well-documented.

Reporting the Data Extraction Process in Your Dissertation

• Methodology section: When writing your dissertation, it’s important to explain the data extraction process in detail in the methodology section. Describe how the data extraction table was created, what information was included, and how decisions were made.
• Tools and templates: Be sure to mention any tools or templates, such as Covidence or Excel, that you used to create the data extraction table. If relevant, include references to literature that informed your data extraction methods, such as the Cochrane Handbook or PRISMA guidelines.
• Collaborative process: If multiple extractors were involved, explain the collaborative nature of the process, highlighting the role of each reviewer. This ensures that the reader understands how the data extraction table was created with consensus.
• Addressing discrepancies: Detail how discrepancies were handled. For example, if extractors are using different approaches or interpretations, explain the process of resolving these differences to ensure that the final data extraction table is consistent and reliable.

Utilizing Supplementary Materials for Data Extraction Documentation

• Supplementary materials: Include any supplementary materials that support the data extraction table and the extraction process. These materials could include full-text study descriptions, additional forms, or logs that were used to track the process.
• Additional tables or charts: If your systematic review involves complex data, you may consider including additional tables or charts that help illustrate the data extraction process. These can be particularly helpful for showing how different studies were grouped or how data was synthesised.
• Appendices: To ensure that the data extraction table is fully transparent, consider including it in an appendix of your dissertation or report. This allows readers to easily verify the extracted data and review the specific information used in your analysis.
• Documentation of amendments: If there are any amendments made during the data extraction process, document these in the supplementary materials. This includes changes to the data extraction table itself or the criteria used for extracting data.

By following these practices, you can ensure that the data extraction table and the process behind it are clearly documented, which enhances the transparency and reliability of your systematic review. This documentation is essential for maintaining the credibility of your findings and allowing others to replicate the review if necessary.