Meta-analysis is a set of statistical techniques for synthesizing data across studies. It is a statistical method for combining the findings from quantitative studies. It evaluates, synthesizes, and summarizes results. It may be conducted independently or as a specialized subset of a systematic review. A systematic review attempts to collate empirical evidence that fits predefined eligibility criteria to answer a specific research question. Meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research (Haidrich, 2010). Rigorously conducted meta-analyses are useful tools in evidence-based medicine. Outcomes from a meta-analysis may include a more precise estimate of the effect of a treatment or risk factor for disease or other outcomes. Not all systematic reviews include meta-analysis, but all meta-analyses are found in systematic reviews (Haidrich, 2010).
A Meta analysis is appropriate when a group of studies report quantitative results rather than qualitative findings or theory, if they examine the same or similar constructs or relationships, if they are derived from similar research designs and report the simple relationships between two variables rather than relationships that have been adjusted for the effect of additional variables (siddaway, et al., 2019).
Haidich A. B. (2010). Meta-analysis in medical research. Hippokratia, 14(Suppl 1), 29–37.
A meta synthesis is the systematic review and integration of findings from qualitative studies (Lachal et al., 2017). Reviews of qualitative information can be conducted and reported using the same replicable, rigorous, and transparent methodology and presentation. A meta-synthesis can be used when a review aims to integrate qualitative research. A meta-synthesis attempts to synthesize qualitative studies on a topic to identify key themes, concepts, or theories that provide novel or more powerful explanations for the phenomenon under review (Siddaway et al., 2019).