What might researchers further analyze when p > .05 in a meta-analysis?

When researchers encounter a p-value greater than .05 in a meta-analysis, it indicates that the combined results of the studies being analyzed do not show statistically significant effects. In such cases, it is crucial to explore deeper factors that might be influencing these results. Analyzing subgroups of studies allows researchers to investigate whether certain characteristics or conditions within the studies could be hiding significant effects that are not apparent in the aggregated data.

For instance, subgroups might be based on factors like age, gender, the type of intervention, or the context in which the data was collected. By conducting a subgroup analysis, researchers can identify whether specific segments of the studies show different outcomes that collectively shift the overall results closer to significance or reveal trends that might be overlooked when considering all studies together. This targeted analysis could also help explain heterogeneity among study results, offering insights that lead to a better understanding of the effects being studied.

While examining data collection methods, fixed effects models, and sample sizes could provide useful information about the analysis process or the quality of evidence, it is the subgroup analysis that specifically addresses the question of why an overall non-significant finding may exist, enabling more tailored conclusions and recommendations based on the individual characteristics of the studies involved.