A New Measure for an Acceptable Level of Homogeneity in Meta-Informatics

This paper addresses the challenges in assessing heterogeneity in meta-analytic studies. The specifics include mental health research work. Three key statistical scores in meta-analytics—Higgins’ I2, Birge’s H2, and the newly developed S2 score—are discussed and illustrated. The paper critiques the subjectivity of these scores and introduces elasticity to enhance the accuracy and objectivity in assessing heterogeneity. The integration of elasticity into the meta-informatic score measures how heterogeneity changes as new studies are added, improving the interpretation of meta-analytic results. Also, the authors compute and compare elasticity scores in the context of mental health research, offering a novel approach to visualizing and quantifying heterogeneity. The authors demonstrate how elasticity improves the assessment of heterogeneity. The paper recommends the use of the meta-informatic S2 score, integrated with elasticity, for more reliable and objective conclusions in mental health as well as in other meta-analyses. The new rectified score, S2, overcomes issues with the I2 score when the chi-squared distribution fails due to small sample sizes or negative values.