Testing the Magnitude of Correlations Across Experimental Conditions.

ABSTRACT: Correlation coefficients are often compared to investigate data across multiple research fields, as they allow investigators to determine different degrees of correlation to independent variables. Even with adequate sample size, such differences may be minor but still scientifically relevant. To date, although much effort has gone into developing methods for estimating differences across correlation coefficients, adequate tools for variable sample sizes and correlational strengths have yet to be tested. The present study evaluated four different methods for detecting the difference between two correlations and tested the adequacy of each method using simulations with multiple data structures. The methods tested were Cohen's q, Fisher's method, linear mixed-effects models (LMEM), and an

SUBMITTER: Di Plinio S 

PROVIDER: S-EPMC9177411 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature