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Published by at November 1st, 2023 , Revised On November 1, 2023

Data Extraction in Systematic Review | Data Extraction Methods

Systematic reviews are a pillar of evidence-based practice and research because they help to synthesise the body of information on a given subject and guide decision-making. These evaluations mainly rely on data extraction, which entails compiling, organising, and assessing pertinent information from carefully chosen studies. The intricate details of data extraction in systematic reviews will be covered in this in-depth guide.

Understanding the Basics

Before discussing data extraction methods, let’s ensure we understand systematic reviews and why they are crucial in academic research.

What is a Systematic Review?

A systematic review is a research method that involves a thorough and structured examination of all available evidence on a specific research question or topic. It follows a predefined protocol, which includes a well-defined research question, search strategy, and inclusion and exclusion criteria for studies.

Systematic reviews are often used to:

  • Summarise existing evidence.
  • Identify gaps in the literature.
  • Inform clinical practice and policy decisions.
  • Reduce bias by following a rigorous methodology.
  • Provide a foundation for meta-analysis when appropriate.

Systematic reviews provide a comprehensive and unbiased summary of existing knowledge in a particular field.

The Importance of Data Extraction in Systematic Review

Data extraction is a pivotal step within the systematic review process. It systematically gathers relevant data from selected studies to address the research question and objectives. The importance of data extraction in systematic reviews cannot be overstated, as it serves several crucial purposes:

  • Ensuring Objectivity
  • Data extraction offers a systematic way of gathering information from many sources while attempting to eliminate bias. By doing this, the evaluation procedure is made as impartial as feasible.
  • Synthesising Evidence 
  • Extracted data is the foundation for putting together a case and making judgments. The review’s conclusions could be affected without accurate and complete data.
  • Making Comparisons Easier 
  • Numerous studies with various methodologies and methods of data presentation are frequently included in systematic reviews. By standardising and organising this information, data extraction makes it simpler to compare and interpret.
  • In favour of Transparency 
  • For systematic reviews to be reproducible and credible, data extraction procedures must be reported clearly and concisely. It enables other people to evaluate the reliability of the review’s findings.

Now that we understand why data extraction is crucial, let’s explore the various methods and considerations involved in this process.

Data Extraction Methods in Systematic Reviews

Data extraction is a systematic and structured process involving identifying, collecting, and organising relevant data from each study. The choice of data extraction method depends on the nature of the research question, the type of data available in the selected studies, and the review’s objectives. Here are some common data extraction methods employed in systematic reviews:

1. Manual Data Extraction

Manual data extraction involves the extraction of data from primary studies by human reviewers. This method is often used when the data of interest are textual and require interpretation. Manual extraction may include:

  • Extracting key study characteristics (e.g., study design, sample size).
  • Recording outcome measures and effect sizes.
  • Collecting data from tables, figures, and text.
  • Assessing study quality and risk of bias.

Manual data extraction allows reviewers to exercise judgment and interpret data as needed. However, it can be time-consuming and may introduce subjectivity if not performed rigorously.

2. Automated Data Extraction

Automated data extraction involves using software or tools to extract data from studies automatically. This method is particularly useful when dealing with large datasets or structured data that machines can easily process. Automated extraction may include:

  • Data mining techniques to extract relevant information from text.
  • Parsing tables and figures to extract numerical data.
  • Standardised forms for data input and extraction.

Automation can significantly speed up the data extraction process and reduce the risk of human error. However, it may be limited by the availability of suitable tools and the complexity of the data.

3. Combination of Manual and Automated Methods

Both human and automatic data extraction techniques are frequently used in systematic reviews. The advantages of both approaches are used in this hybrid strategy. For instance, quantitative data from tables and figures can be extracted using automated methods, and qualitative data can be interpreted via manual extraction.

4. Forms for Data Extraction

Standardised spreadsheets or templates are used as data extraction forms to direct the process. These forms often have sections where study features, results, and other pertinent information can be entered. A well-designed data extraction form must be made to capture all relevant data and maintain consistency.

5. Double Data Extraction

In certain systematic reviews, two independent reviewers collect data from the same study to increase accuracy and eliminate errors. Any disagreements are clarified through conversation or by bringing in a third reviewer. The dependability of the extracted data is improved by double data extraction.

6. Software programs for Data Extraction 

Several software programs make it easier to extract data for systematic reviews. These technologies frequently include automation features and customisable data extraction forms. Software like EndNote, Covidence, and DistillerSR are a few examples.

Results of Data Extraction in Systematic Reviews

The findings and conclusions of systematic reviews are built on the data extraction results. These findings cover a broad variety of data gathered from each included study. The following significant data extraction outcomes:

1. Study Characteristics

  • Study Design: Identifying the type of study (e.g., randomised controlled trial, cohort study).
  • Sample Size: Recording the number of participants in each study.
  • Duration of Follow-up: Noting the duration of the study period.
  • Geographic Location: Documenting where the study was conducted.

2. Participant Characteristics

  • Demographics: Collecting data on the age, gender, and other relevant characteristics of study participants.
  • Inclusion and Exclusion Criteria: Understanding the criteria used to select participants.

3. Intervention and Exposure

  • Description of Intervention: Detailing the intervention or exposure being studied.
  • Dose and Duration: Documenting the dosage and duration of the intervention.
  • Comparator: Identifying the control or comparison group.

4. Outcome Measures

  • Primary Outcome(s): Recording the primary outcome measures specified in each study.
  • Secondary Outcome(s): Noting any secondary outcome measures.

5. Data for Meta-analysis

  • Effect Sizes: Extracting effect sizes, such as odds ratios, hazard ratios, or mean differences.
  • Confidence Intervals: Collecting confidence intervals for effect sizes.
  • Raw Data: If available, extracting raw data for meta-analysis.

6. Risk of Bias Assessment

  • Quality Assessment: Assessing the quality and risk of bias in each study using appropriate tools (e.g., Cochrane Risk of Bias tool).
  • Publication Bias: Considering the potential for publication bias in the included studies.

7. Additional Information

  • Author Contact: Recording author’s contact information for potential clarification or additional data.
  • Funding Source: Noting the source of funding for each study.

It’s important to emphasise that the specific data extraction outcomes may vary depending on the research question and objectives of the systematic review. Therefore, the data extraction form should be tailored to comprehensively capture the relevant information needed to address the research question.

Challenges and Considerations in Data Extraction

Data extraction in systematic reviews is important, but there are difficulties and things to consider. Reviewers need to be mindful of biases and potential hazards that could compromise the validity and accuracy of the extracted data. Here are some crucial factors to remember:

1. Subjectivity

Subjectivity may still exist even with standardised data extraction procedures and guidelines, especially when evaluating qualitative data. When making their decisions, reviewers should strive for uniformity and openness.

2. Absence of Data

Primary studies can fail to provide all relevant information or do so inadequately. Reviewers must note any missing information and consider the implications of their findings.

3. Heterogeneity

Studies with various methodologies, demographics, and interventions are frequently included in systematic reviews. The heterogeneity of the included studies and how it affects data synthesis and meta-analysis should be carefully considered by reviewers.

4. Bias in publications

The data extraction results can be impacted by publication bias, which occurs when research with good results is more likely to be published. Reviewers should be conscious of this prejudice and consider ways to lessen its effects.

5. Data Extraction Training

Reviewers should be trained in data extraction techniques and follow the review methodology to guarantee accuracy and consistency.

Similar Review Techniques

Let’s briefly examine how data extraction links to other review techniques and protocols to give a thorough grasp of the systematic review procedure:

1. Scoping Review

A scoping review is a less formal literature analysis that seeks to map the body of knowledge on a wide subject. Even while data extraction in scoping reviews is less thorough than in systematic reviews, it nevertheless entails locating and gathering important data from a few well-chosen sources. Data extraction principles can help researchers conduct scoping review structures and present their findings effectively.

2. Traditional Literature Review

The thorough and organised approach of systematic reviews is sometimes missing from traditional literature reviews. They might not use formal data extraction to the same degree, which could result in biases in data selection and interpretation. When conducting literature reviews, researchers should consider the advantages of using systematic approaches, including data extraction.

3. Protocols For Systematic Reviews

Before a systematic review, researchers typically develop a review protocol outlining the research question, search strategy, inclusion/exclusion criteria, and data extraction methods. This protocol serves as a roadmap for the entire review process, ensuring transparency and consistency in data extraction and analysis.


A crucial and rigorous phase in the systematic review process is data extraction. It is crucial in combining the available data, assuring accuracy, and defending the integrity of the results. By comprehending the various data extraction methods, results, and concerns, researchers may carry out systematic reviews that support evidence-based practice and assist in decision-making in a variety of sectors.

A rigorous and organised method of data extraction remains a crucial tool in pursuing knowledge and advancing scholarly research despite obstacles and constraints.

Frequently Asked Questions

Data extraction in a systematic review involves systematically collecting and recording essential information from selected studies to answer a specific research question. It includes details like study characteristics, participant demographics, interventions, and outcome measures.

To write data extraction in a systematic review, create a structured data extraction form, specify data sources, record study details, participant information, intervention specifics, outcome measures, and assess the risk of bias. Maintain accuracy, consistency, and transparency throughout the process.

About Owen Ingram

Avatar for Owen IngramIngram is a dissertation specialist. He has a master's degree in data sciences. His research work aims to compare the various types of research methods used among academicians and researchers.