When conducting meta-analysis, what types of data are combined?

In meta-analysis, the primary focus is on synthesizing research findings from multiple studies to provide a comprehensive understanding of a particular question or effect. This process predominantly involves the combination of quantitative data, specifically numerical results from studies that measure similar outcomes. By aggregating this quantitative data, researchers can calculate overall effect sizes, assess variability, and determine the robustness of the findings across different contexts.

However, it is also important to recognize that while traditional meta-analysis relies heavily on quantitative data, some modern approaches may incorporate qualitative findings. This can occur through systematic reviews that combine both types of data to offer deeper insights, particularly in areas where quantitative measures are insufficient or when exploring complex constructs.

Therefore, the choice highlighting a mix of qualitative and quantitative data captures the evolving nature of meta-analysis, where integrating diverse data types can enhance the richness of conclusions drawn from the combined studies. Such an approach allows for a broader understanding of the topic by considering both numerical assessments and the context or nuances provided by qualitative information.