Systematic Literature Review, Meta Analysis and PRISMA 2020 Statement || Bibliometric Analysis

Meta-analysis and systematic literature review (SLR) are presented as the backbone for turning scattered research into a structured, evidence-based answer—then quantifying (meta-analysis) and reporting it transparently using PRISMA 2020. The core message is practical: build a repeatable pipeline from a clearly defined research question, through disciplined study selection and data preparation, to analysis (including moderator checks), and finally to reporting that lets others verify what was included and why. That matters because it reduces subjectivity in literature synthesis and makes future research directions easier to justify.

The discussion starts by separating SLR from meta-analysis. SLR is framed as a qualitative synthesis that provides a comprehensive picture of what is known and where future research should go. Meta-analysis adds quantification—turning results across studies into a statistical relationship—so researchers can test patterns more rigorously. The transcript also highlights why systematic methods matter: selection and interpretation can otherwise become subjective, and important evidence may remain hidden if only published studies are considered. Alongside that, it notes that systematic reviews can include unpublished or hard-to-find studies when feasible, and that the process typically begins with a research question and ends with a structured report.

A step-by-step workflow is then laid out for both SLR and meta-analysis. For SLR, the sequence runs from defining the research question, collecting and preparing data, analyzing it, and reporting findings. The review can be domain-based, theory-based, or method-based depending on the goal. For meta-analysis, the workflow mirrors the same backbone but adds statistical decisions: define the research question, collect studies, apply inclusion/exclusion criteria, clean and prepare the dataset, and then choose an appropriate model for effect estimation. The transcript specifically mentions fixed-effect and random-effects models, and it emphasizes testing beyond a single pooled estimate through moderator analysis.

Moderator analysis is described as a way to examine when and why effects differ across studies. Techniques named include subgroup analysis, meta regression, and multilevel analysis—used to test how moderators influence the meta-analytic relationships. The transcript also points to tools and software commonly used for meta-analysis workflows, including R and R packages, and it references visualization approaches such as word clustering and mapping views (with a “VOSviewer” mention) to interpret research themes and connected terms.

Finally, PRISMA 2020 is introduced as the reporting standard for systematic reviews and meta-analyses. The emphasis is on a checklist and a flow diagram that document the full journey of studies: identification (from databases/registers), screening, eligibility assessment, and inclusion. The transcript notes that PRISMA 2020 was published in 2009 and later updated, and it stresses that the checklist’s structure—title, abstract, methods (eligibility criteria and data collection), results, discussion, and other information—helps ensure completeness and transparency. A PRISMA flow example is described with numbers moving from initial records through exclusions to the final included studies, reinforcing that reviewers must record reasons for exclusions and keep the selection process auditable. The overall takeaway is that combining SLR/meta-analysis methods with PRISMA 2020 reporting creates a defensible, reproducible literature synthesis that supports credible future research directions.