Is Our Result Unusual? A Practical Procedure for Benchmarking Small-Sample Correlations against Meta-Analytic Evidence

Issah Quarmson; Maria Sonia Justin Raj

doi:10.36948/ijfmr.2026.v08i06.91207

Is Our Result Unusual? A Practical Procedure for Benchmarking Small-Sample Correlations against Meta-Analytic Evidence

Author(s)	Dr. Issah Quarmson, Prof. Maria Sonia Justin Raj
Country	United Arab Emirates
Abstract	Practitioner researchers, including doctoral candidates in professional programmes, often estimate correlations from samples of around fifty and compare them with meta-analytic estimates. The comparison is usually informal: a result is called consistent with, or weaker than, the literature without asking whether the gap exceeds what sampling error, measurement error and genuine between-study variation would produce. This paper sets out a transparent six-step procedure for making the comparison properly: select a commensurable benchmark; estimate a confidence interval for the observed correlation using Fisher's z transformation; attenuate the corrected benchmark to the reliabilities of the study's own measures; compare the observed correlation with both the benchmark mean and the distribution of true effects implied by the meta-analysis; stress-test the conclusion; and calculate what the study could have detected. The procedure is illustrated with six correlations from a survey of 52 expatriate educators in Guangzhou, China, benchmarked against Tett and Meyer (1993) and Meyer et al. (2002). None of the six correlations differed reliably from its benchmark. An apparent shortfall in the satisfaction and turnover intention correlation (r = -0.26 against a published estimate of -0.58) was significant (p = 0.046) under only one combination of analytic choices. It was no longer significant with a construct-matched benchmark (p = 0.107), with allowance for between-study heterogeneity (p = 0.167), after correction for multiple testing, with a bootstrap interval, or when a single influential respondent was excluded. With 52 respondents, only differences of roughly 0.20 to 0.40 correlation units were detectable with 80 percent power. Researchers should choose benchmarks that match their constructs and measures, put both correlations on the same metric, distinguish a result that differs from the average study from one that is unusual among studies, and report what their sample could and could not have detected. Spreadsheet formulas are provided for every step.
Keywords	Meta-Analysis, Benchmarking, Correlation, Fisher z Transformation, Credibility Interval, Prediction Interval.
Field	Business Administration
Published In	Volume 8, Issue 6, November-December 2026
Published On	2026-11-03
DOI	https://doi.org/10.36948/ijfmr.2026.v08i06.91207

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Is Our Result Unusual? A Practical Procedure for Benchmarking Small-Sample Correlations against Meta-Analytic Evidence

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