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Strategy for Data Synthesis
in Systematic Review

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The purpose of a data extraction table within a systematic review becomes apparent during synthesis, where reviewers collate and evaluate the meaning of the data gathered. Synthesis means that reviewers use the information from their data extraction template for systematic review to create coherent bodies of data that can be analyzed to gain a deeper understanding of the information conveyed.

Reviewers should have a clear strategy showing how they will approach data synthesis to expedite and verify outcomes, such as whether or not their specific review subject requires a meta-analysis or a quantitative synthesis.

The Importance of a Data Synthesis Strategy

Numerous synthesis methodologies are available, making it important to have a defined data extraction process systematic review relevant that describes how a reviewer will categorize and interpret data and use that evaluation to reach conclusions.

Appropriate research approaches can adopt broad categories, such as emerging, qualitative, quantitative, and conventional syntheses. However, each has varying characteristics, context, assumptions, analysis units, strengths, and restrictions that determine which potential technique is most suited to the systematic review in question.

The right data extraction process for systematic review will depend on these variables and the anticipated outcomes and theories that the study seeks to uphold or disprove.

Alternative Data Synthesis Approaches

Below, we examine the four primary subsections of data synthesis used in systematic reviews to demonstrate how each applies depending on the data types available.

Conventional Synthesis

This is used to produce charts, diagrams, maps, and tables, demonstrating conceptual frameworks or theories. This type of data synthesis examines data types such as quantitative studies, literature, policy documentation, and qualitative research.

Some downsides include a reduced element of critique, and systematic evaluation, making it more suitable for reassessing existing topics or preliminary conceptualization for new pieces of research.

Qualitative Synthesis

Our next data synthesis approach involves collating or integrating multiple data sets comprising qualitative research findings and theoretical literature. Outcomes involve conceptual frameworks or maps, definitions, and narrative summaries of the subject matter.

Quantitative Synthesis

This category of systematic review is similar to qualitative synthesis, although it uses quantitative studies to produce generalizable statements, narrative summaries, and mathematical scoring evaluations.

Emerging Synthesis

Finally, approaching data synthesis with an emerging strategy takes a newer approach, incorporating literature and metrics from a broad spectrum of data types, including diverse subject groups.

Selected data sources might include quantitative and qualitative studies, editorials, policies, evaluations, commentaries, and theoretical work. A systematic review adopting an emerging data synthesis approach can produce conceptual maps, decision-making reports, and statistics such as charts, graphs, diagrams, and scoring.

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Why Does Systematic Review Require a Data Synthesis Strategy?

Synthesized data represents the results derived from studies and analyzed in relevance to the question or theory the systematic review attempts to answer. Because the synthesis technique used dictates the data used and the possible outcomes of the review, reviewers must take the right approach, evaluating the strengths and drawbacks of each and how synthesis adds value to the exercise.

The right strategy can make a considerable difference to the integrity of the outcomes and effects found and the value and credence of the quality of the information provided as a final conclusion.

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