Meta-analysis combines results from multiple studies to create a more complete picture of a research problem. It can reveal statistical patterns that a single study might not show, but selection errors and biases can lead to erroneous conclusions. The criteria for selecting studies depends on the objective of the analysis. Critics argue that the process is not truly objective or scientific because of the selection issue.
A meta-analysis is an analytical review of several research studies on a common topic. Scientific research relies on statistical results, but these studies are often limited by sample size, as only a small sample of possible data can be collected in the course of a given project. The meta-analysis aims to overcome this difficulty by combining the results of several studies, creating a more complete picture of the research problem. While this type of analysis has advantages, it also has disadvantages, such as selection errors and possible statistical biases that can lead to erroneous conclusions.
Meta-analyses can be performed in any study area where a body of statistical research literature exists. For the analysis to be valid, however, it must be done systematically, like a research study itself. After the problem has been formulated, certain studies are selected for inclusion in the analysis based on specific criteria.
The nature of the criteria depends on the objective of the meta-analysis. For example, a researcher conducting a meta-analysis of treatments for heart attack patients would include specific studies on this topic. The researcher could further narrow the literature selection by choosing only studies performed with an appropriate methodology. For example, the requirement for randomization, or random selection of samples to avoid bias, could be a criterion for inclusion.
After the studies have been collected and reviewed, statistical methods are used to combine and filter the data. Because the sample size in a meta-analysis is actually much larger than the sample size in an ordinary research study, it may be possible for the analysis to reveal statistical patterns that a single study might not show. The small sample size of a single research study can also disproportionately magnify some random effects. Meta-analysis can be used to resolve inconsistencies between studies resulting from such random fluctuations.
Perhaps the biggest drawback to the meta-analysis process is the selection issue. As the researcher must choose which studies to include in his analysis, bias in the overall statistical conclusions is inevitable. A researcher with a given agenda could conceivably bias selection to favor certain conclusions over others. Even if the topic of the analysis is narrow enough to allow for a review of all available literature, unpublished studies will not be included. Critics of meta-analysis point to this as evidence that the process is not truly objective or scientific.
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