Correlations and Meta- Analysis

• A meta- analysis is a quantitative research method which takes data from published studies: includes statistical calculation of numerical findings of lab experiments, correlational studies and question- based research
• In a meta- analysis researchers combine the findings from these multiple studies too draw an overall conclusion about the topic: expressed in terms of the size effect
• The size effect refers to the strength of the relationship between two variables on a numerical scale (highest being 1)
• Meta- analysis allow from trends/ patterns to be identified by combining the data of lots of smaller studies- trends that would not be identifiable if only one study at a time was used
• Strengths of meta- analysis:

  • less chance of bias confounding the results due to usage of secondary data
  • researchers cannot have influenced outcome of study as have not conducted it themselves- increased reliability and statistical power of data
  • possible to generalise findings- increasing external validity
• Limitations of meta- analysis:

  • secondary data means researcher cannot be certain the precision it was collected by- no control over key variables and how they were operationalised: limiting reliability
  • may be difficult to find/ access relevant studies- time consuming- leading to abandonment of the research: losing valuable insight into topic