Assessing Partial Association Between Ordinal Variables: Quantification, Visualization, and Hypothesis Testing

Dungang Liu, Shaobo Li, Yan Yu and Irini Moustaki

Journal of the American Statistical Association, 2021, vol. 116, issue 534, 955-968

Abstract: Partial association refers to the relationship between variables Y1,Y2,…,YK while adjusting for a set of covariates X={X1,…,Xp}. To assess such an association when Yk’s are recorded on ordinal scales, a classical approach is to use partial correlation between the latent continuous variables. This so-called polychoric correlation is inadequate, as it requires multivariate normality and it only reflects a linear association. We propose a new framework for studying ordinal-ordinal partial association by using Liu-Zhang’s surrogate residuals. We justify that conditional on X, Yk, and Yl are independent if and only if their corresponding surrogate residual variables are independent. Based on this result, we develop a general measure ϕ to quantify association strength. As opposed to polychoric correlation, ϕ does not rely on normality or models with the probit link, but instead it broadly applies to models with any link functions. It can capture a nonlinear or even nonmonotonic association. Moreover, the measure ϕ gives rise to a general procedure for testing the hypothesis of partial independence. Our framework also permits visualization tools, such as partial regression plots and three-dimensional P-P plots, to examine the association structure, which is otherwise unfeasible for ordinal data. We stress that the whole set of tools (measures, p-values, and graphics) is developed within a single unified framework, which allows a coherent inference. The analyses of the National Election Study (K = 5) and Big Five Personality Traits (K = 50) demonstrate that our framework leads to a much fuller assessment of partial association and yields deeper insights for domain researchers. Supplementary materials for this article are available online.

Date: 2021
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DOI: 10.1080/01621459.2020.1796394

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