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Systematic Review vs Meta Analysis: A Difference That Makes Sense

It is noteworthy that a lot of academic writings are complicated not so much by the execution processes but by the incomprehensibility of requirements and features. One of the most popular situations is when students struggle to distinguish between systematic review vs meta analysis. And in most cases, these terms are often used interchangeably but mean two different things. Although each process entails considerable research about research, there are some crucial differences between the two.

A useful point in the systematic review vs. meta analysis is that a systematic review implies a piece of academic writing that asks the researcher questions, then answers them through a summary of available evidence meeting a set criterion. In some systematic reviews, authors present their findings using meta-analysis. However, it implies combining the findings of different trials to produce an average finding.

Notably, experts often prefer meta analysis as it helps generate a more precise approximation of the impact of treatment compared to individual studies.

What Is Main Difference Between Systematic Review and Meta Analysis

Before proceeding, we must explore the difference between systematic review and meta analysis. While the two terms are often used interchangeably, meta analysis differs from a systematic review.

  • Methods Used

A systematic review implies a process that answers a precise research question by gathering and summarizing all the empirical evidence fitting specified eligibility criteria. On the other hand, a meta-analysis suggests using statistical approaches to synthesize the findings of empirical studies.

  • Requirements to Quality

Just like other research undertakings, systematic reviews can vary in terms of quality. The projects tend to be monumental in volume, requiring considerable time and research.

A good systematic review should have clearly stated objectives with a predetermined criterion for eligibility. It should also have explicit and reproducible eligibility. There should also be a systematic presentation and synthesis of the attributes and findings of the studies included. When comparing systematic review versus meta analysis, understand that not all systematic reviews comprise meta analysis.

  • Research Analysis

Another point to note in the meta analysis versus systematic review debate is the approach used. Unlike the systematic review, a meta analysis implies using statistical approaches to synthesize the findings of independent studies.

The researcher combines information from related studies, making it possible to offer more precise estimates. Unlike the systematic review, the meta analysis goes beyond critiquing and integrating studies, carrying out secondary statistical analysis of the findings.

Meta Analysis Versus Systematic Review: Additional Points to Consider

Studies evaluation objectivity is another important nuance that should be considered while exploring the difference between meta analysis vs systematic review.

One notable advantage of a meta analysis is that it can be completely objective when evaluating research findings, what’s especially important for some assignments like a narrative literature review. Of course, not all topics contain enough evidence to permit meta analysis. In such instances, carrying out an integrative review makes more sense. The bottom line is that it can improve the precision of effect estimates. However, the findings can also be misleading when conducted with data that are not sufficiently similar or when the methodological quality of the data is poor.

And What About a Systematic Review?

On the other hand, a systematic review with meta analysis or as a separate work opens with a research question and a plan on how the synthesis of studies is to be conducted. A team of reviewers searches for studies to answer questions using an efficient search strategy. After that, the collected studies are evaluated for eligibility based on a predetermined approach.

Next, the reviewer can extract useful information from the studies, assessing the quality of the included findings. After this extraction, results can be synthesized, sometimes using meta analysis, before presenting the findings.

Difference Between Meta Analysis and Systematic Review in the Writing

A systematic review implies a reproducible piece of observational research and should comprise a protocol that clearly outlines specific objective assessment conducting methods. The task focuses on controlling errors and reducing random ones through meta analysis. The success of your meta analysis systematic review will depend on, among other things, the use of objective and methodically sound evidence.

How to Conduct a Systematic Review

To create a good meta analysis vs. systematic review project, start by identifying the research question since all your decisions will be based on it. The objective of your work is to fill a knowledge gap.

Once you have settled on a research question to inform your review, define the criteria you intend to use to include or exclude studies. Afterward, you can search for studies and select the ideal ones based on your criteria. In the final stages, extract data, analyze the quality of the information, then synthesize and present your findings.

How to Conduct a Meta Analysis

Like the systematic review, the meta analysis opens with a project type and topic specification. You will also be expected to research and follow the guidelines set for different assignment types. Also, you need to establish inclusion criteria and define essential variables.

The broader your inclusion criteria for the systematic review meta analysis project, the more heterogeneity between the studies. Furthermore, relatively narrow inclusion criteria make finding appropriate studies. So you’ll need to conduct systematic searches through different databases.

Once you have settled on the studies to include in your meta analysis, complete the following steps:

  • Reach out to authors of primary articles to seek missing data.
  • Choose the most ideal statistical models for your question.
  • Use the relevant software to conduct meta-statistical analysis.
  • Make sure the records of your study report are complete and transparent.
  • Offer sufficient data in your manuscript to allow comprehension.
  • Offer context for your findings and make recommendations for the future.

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