Jae-On Kim

TL;DR: How to assess the nature of missing data especially with regard to randomness, a comparison of listwise and pairwise deletion, and methods for using maximum information to estimate parameters or missing values are covered.

Abstract: This article examines the assumptions underlying two multivariate strategies commonly used in analyzing ordinal data Both strategies employ as a descriptive tool the ordinary multiple regression algorithms; the crucial difference between the two is that the first, ordinal strategy, uses the matrix of Kendall's 's as the building block of multivariate analysis, while the second, parametric strategy, uses the matrix of Pearson's 's These two strategies are evaluated and constrasted in terms of their usefulness in answering basic research questions that arise in multivariate analysis One overriding conclusion is that, contrary to the claims of its proponents, the ordinal strategy is no better than the parametric strategy at meeting some of the basic requirements of multivariate analysis It is argued that parametric strategy, when accompanied by careful evaluation of the validity of the implict quantification of ordinal variables, is more amenable to one of the goals of scientific research: successive app

TL;DR: This article argued that the standardization of variables and scales should be separated from the habitual use of standardized coefficients, and that the use of standard coefficiencies should be distinguished from the use standardization coefficients.

TL;DR: The inadvisability of using standardized regression coefficients for the purpose of comparing causal relationships across populations or establishing causal laws is addressed by explicating the relationship between the underlying causal structure and the variance-covariance structure of a system of variables.