About Systematic Reviews
What Is the Purpose
of a Data Extraction Table?
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Data extraction is an important step in the systematic review process. It involves collecting data relevant to answering the question posed in the review—data used to arrive at comprehensive conclusions.
Data extraction tables are used to compile the data that matches the intervention question. Information provided in these data extraction tables allows readers to assess the applicability of the findings in their area of interest.
This article will show you the function of a data extraction table and consider some data extraction form examples.
Data Relevant to the Systematic Review Question
The purpose of a data extraction table is to accumulate data that is useful to the systematic review question. A systematic review combines individual studies relevant to a specific research question into one document using rigorous and transparent methods.
In a six-step process, researchers comb through studies and by following inclusion and exclusion criteria, they leave out studies that are irrelevant to the systematic review. The researchers then extract useful data from the remaining eligible studies for data analysis in a systematic review.
This data is usually collected in a data extraction table. This table includes the following information:
- Information About the Article
When readers consume the systematic review, they may be interested in conducting research in a related area. If you’re part of the team compiling the systematic review, include the name of the author(s), year of publication, title, and DOI of the studies you used. You can also include the name of the journal. - Information About the Study
Because a systematic review involves combining studies relevant to a specific research question, the data extraction table should contain information about these studies. For example, the type of study used in the systematic review, be it a random controlled trial, a cohort study, or a case-control study. You may also include participant requirements, level of evidence, and study quality. - Patient Demographics
This data type includes the age, sex, ethnicity, diseases, conditions, and other characteristics related to the outcome or intervention. - Intervention
This data type may include route of administration, quantity, dosage, format, duration, and time frame. - Outcomes
The outcomes you include may be qualitative or quantitative. They are primarily the main results/findings presented in the included eligible studies.
Data extraction tables may also contain more data, such as sample sizes, dependent variables, and reliability measures. Given the complexity of these reports, it’s important to have a strategy for data synthesis in a systematic review.
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Data Extraction Form
At least two people assist in data extraction in a systematic review to reduce error and bias. To standardize the process and improve the validity of the results, you should use a data extraction form.
Given the importance of data extraction forms, ample time and thought should be invested in their design. Since each review is different, data collection forms will vary across reviews, but there are similarities in the important information types. Because of this, a strong form can be adapted from one review to the next.
Reviewers most commonly create their data extraction forms in Excel, Word, or Access, depending on the complexity of the data. You can create your own data extraction form, or you may consider using an existing tool to generate a data extraction form. Another data extraction form example is the Systematic Review Data Repository. The SRDR can facilitate data extraction and storage for systematic reviews and meta-analyses. It’s an open and searchable archive of systematic reviews and their data.
Process of Data Extraction
The data extraction process involves using specific tools. There are various types of data extraction tools, such as systematic review software, spreadsheets, and electronic documents like Google Docs. It’s easy to undertake extraction using extraction forms.
- Identify a data extraction form example to guide you. Use existing systematic reviews on your topic to determine the type of information to collect if you’re not sure. To customize your data extraction form, you can go through the selected eligible studies to identify patterns and themes in reported data.
- Train your review team on the extraction categories and the types of data they should collect. This way, you’ll reduce the number of discrepancies and establish a standard.
- Perform a trial of the data extraction forms to ensure they record similar data.
- Discuss any inconsistencies throughout the data extraction process.
In Conclusion
Data extraction forms are used in the data extraction process to collect useful information. A systematic review is done by combining individual studies that are relevant to a specific research question. Data from these studies are collected and used to prepare a comprehensive analysis that readers may find helpful.
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