Sample Data Extraction
Form Systematic Review
Automate every stage of your literature review to produce evidence-based research faster and more accurately.
Data extraction is a central component of a systematic review. Data collected and organized is then analyzed to arrive at conclusions, recommendations, and rejection or support of a theory.
A standardized data extraction form can be customized to the nature of the review and enables reviewers to import specific data values and metrics from a range of papers, studies, and qualitative or quantitative reports. Incorporating an efficient data extraction template for systematic review allows conclusions to be inspected, verified, and validated, with summarized data sources and how they have contributed to the review outcomes.
Why Use a Data Extraction Form?
Reviewers recognize that tools for extracting data from a database for systematic reviews solve countless potential problems and inefficiencies:
- Facilitating online collaborations quicker and more easily than juggling large spreadsheets and relying on manual data transcription
- Collating appropriately coded data in one central place or summary enabling reviewers to produce updates or validate the outcomes arrived at
- Publishing new versions of data, with each designated a reference to allow for ongoing data audits or verifications
Options to export data and summary tables using applicable filters, search parameters, or formatting
Other advantages relate to quality control, the ability to swiftly identify possible duplications or conflicts, and applying best practices across repeating data sets.
Systematic Review Data Extraction Form Template
Data extraction forms must be customized to the scope and scale of the review, but a template can ensure style and formatting consistency across reporting, allowing stakeholders and colleagues to compare, contrast, and extract the relevant metrics to arrive at conclusions.
The below template demonstrates how data extraction forms can be sorted into concise fields to allow easy interpretation and amalgamation of the data presented.
Data Extraction Form: General Information |
| Author(s): | |
| Systematic Review Title / Reference: | |
| Publication date: |
Study Background |
| Aims / Objectives: | |
| Variances in Baseline Characteristics to Note: | |
| Participant Selection Basis: |
Participants |
| Baseline: | Follow-Up: | |
| Controls (count): | ||
| Interventions (count): | ||
| Gender: | ||
| Mean Age: | ||
| Scope of Age Range: | ||
| Ethnicity: | ||
| Other Relevant Factors: |
Study Details |
| Setting / Context: | |
| Sources Used: | |
| Appraisal Instruments Used: | |
| Method(s) of Analysis: | |
| Outcomes Assessed: |
Outcomes |
| Units / Count: | Definition: | |
| Measure of Characteristic A | ||
| Measure of Characteristic B | ||
| Measure of Characteristic C | ||
| Measure of Characteristic D | ||
| Measure of Characteristic E |
Data Results |
| Outcome: | Mean Variance: | Ratio: |
| Comments: |
Learn More About DistillerSR
(Article continues below)
Benefits of Using a Data Extraction Template for Systematic Reviews
Complex or large-scale data extraction using repeating data can be time-consuming and exposed to manual error. Where several reviewers or data entry professionals work together, streamlining that process to ensure all outputs are identically formatted can be a significant challenge.
Professionally designed data extraction forms incorporating advanced features can expedite the process and mitigate any incompatible formatting or categorization issues. Quality control is highly relevant to data extraction and can designate responsibilities without relying on one reviewer to approve the collated data output.
Where each reviewer utilizes a consistent data extraction form, it allows projects and data sets to be split between participants without allowing extracted data to be presented in multiple formats or layouts, requiring further manual data sorting to allow the systematic review to proceed to the next step.
3 Reasons to Connect
Follow Us
