What type of study combines information from multiple studies to draw a conclusion?

A meta-analysis is a quantitative research method that systematically combines data from multiple studies to arrive at a pooled estimate of effect or conclusion. This approach enhances statistical power and provides a more precise understanding of a research question than individual studies alone. By aggregating results from various studies, a meta-analysis can detect patterns, trends, or discrepancies that may not be evident when examining a single study. It also helps to mitigate the variability and bias associated with smaller studies, offering a clearer perspective on a particular issue or intervention.

In contrast, a case study focuses on an in-depth analysis of a single individual, group, or event, which does not facilitate the comparison across multiple studies. A cohort study follows a group over time to observe outcomes relative to exposures, and while it may provide robust data, it does not synthesize existing research findings. A cross-sectional study analyzes data at a single point in time, offering a snapshot rather than a comprehensive analysis of multiple research contributions. Therefore, the meta-analysis stands out as the correct choice for combining information across studies to draw informed conclusions.