Population models and simulation methods: The case of the Spearman rank correlation

• Published in: British journal of mathematical & statistical psychology 2017-11, Vol.70 (3), p.347-367
• Main Authors: Astivia, Oscar L. Olvera, Zumbo, Bruno D.
• Format: Article
• Language: English
• Subjects: Algorithms; Computer Simulation; copula; Humans; Mathematical models; Models, Psychological; Models, Statistical; Monte Carlo Method; Monte Carlo simulation; Parameters; Population; Population Density; Psychometrics - statistics & numerical data; rank correlation; Selection Bias; Simulation; simulation design; Spearman rank correlation; Statistics, Nonparametric
• ISSN: 0007-1102; 2044-8317
• DOI: 10.1111/bmsp.12085

The purpose of this paper is to highlight the importance of a population model in guiding the design and interpretation of simulation studies used to investigate the Spearman rank correlation. The Spearman rank correlation has been known for over a hundred years to applied researchers and methodologists alike and is one of the most widely used non‐parametric statistics. Still, certain misconceptions can be found, either explicitly or implicitly, in the published literature because a population definition for this statistic is rarely discussed within the social and behavioural sciences. By relying on copula distribution theory, a population model is presented for the Spearman rank correlation, and its properties are explored both theoretically and in a simulation study. Through the use of the Iman–Conover algorithm (which allows the user to specify the rank correlation as a population parameter), simulation studies from previously published articles are explored, and it is found that many of the conclusions purported in them regarding the nature of the Spearman correlation would change if the data‐generation mechanism better matched the simulation design. More specifically, issues such as small sample bias and lack of power of the t‐test and r‐to‐z Fisher transformation disappear when the rank correlation is calculated from data sampled where the rank correlation is the population parameter. A proof for the consistency of the sample estimate of the rank correlation is shown as well as the flexibility of the copula model to encompass results previously published in the mathematical literature.