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Difference Between Systematic Reviews and Meta-Analysis

Systematic review involves a meticulous plan and search strategy, with an objective of minimizing bias by identification, appraisal and synthesizing of all studies related to a specific topic. A systematic review usually includes a meta-analysis section which utilizes statistical methods in synthesizing data from various studies into a distinct quantitative estimation. The paper explores the differences between systematic reviews and meta-analysis. Furthermore, it will also address the strengths of quasi-experimental research design.

A systematic review is a detailed, comprehensive, and crystal clear method of medical literature interrogation. Several steps are observed when using systematic review design. These steps are asking a query which can be answered, identification of a single or several databases to source for the answer, establishing a straightforward search strategy, choosing titles, manuscripts and abstracts on basis of explicit inclusive and exclusive criteria, and conceptualizing data into a standardized format. On the other hand, a meta-analysis is a statistical method used in combining the data obtained from a systematic review. Therefore, each meta-analysis is done on the basis of underlying systematic review. However, not every systematic review results to a meta-analysis.

Systematic reviews enables us to account for a whole range of appropriate findings from research on a specific topic. They can be utilized to determine whether the results of a scientific research are consistent, including whether such findings can be generalized across populations, treatments and settings, or whether the outcomes are varying across the subgroups. Furthermore, the clear methods employed in systematic reviews reduce biasness, improves reliability and accurateness of the conclusions.

Sometimes there is quantitative pooling of data from individual studies and reanalyzing of such data by statistical techniques in systematic reviews. The approach employed in this case is called meta-analysis. The technique increases the overall sample size by combining samples from individual studies and this improves statistical power of the analysis as well as the estimates of the treatment effects.

Strengths of Quasi-experimental Research Design

There are many advantages of using quasi-experimental design. The technique is more feasible since it there is no time and logistical constraints commonly found with other experimental designs. The method has no artificial research environment and thus gives genuine reactions of the test subjects. Quasi-experimental research design is crucial in the identification of general trends from the outcomes, mostly in social science fields. Besides, the results obtained from quasi-experimental designs can be utilized in reinforcing case study findings by carrying out research that leads to statistical analysis. Extensive pre-screening and randomization are not necessary in quasi-experimental approaches and this reduces the time and resources required in the research. Moreover, it is easy to identify and address the threats to validity when using the approach.

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