How to Use Ridit Analysis

Abstract

In many scientific studies in the biological and behavioral sciencesprobably in a majority of such studies-the scientist has to work with a response variable which falls in the borderland between dichotomous classifications (e.g. lived-died, yes-no) and refined measurement systems (i.e. measurements which are highly reproducable at different times or at different places). Sometimes the response variable is a subjective scale (i.e. a well ordered series of categories such as minor, moderate, severe). At other times the response variable takes numerical values but the measurement system is heavily dependent on the quality of experimental material, details of protocol, or the technical skill of the scientist. These borderland response variables may not be adequately analysed by the chi-square family of statistical methods and at the same time the t-test family of techniques may not be appropriate. In this situation ridit analysis may serve as a missing link between the two traditional families of statistical methods. This paper is addressed to scientists who are working with borderland response variables. It will contain no mathematical derivations (these will appear in a subsequent paper [1]). Its purpose is to explain and illustrate how to use ridit analysis in a scientific study. For this purpose a ridit analysis of data from the Cornell Automotive Crash Injury Research Program (ACIR) will be presented. This material will serve to illustrate the various problems that come up in actual studies, problems ranging from difficulties due to peculiarities and imperfections of the basic data to questions of presentation and interpretation of the results. The ACIR analysis will also show the role of ridit analysis in achieving the objectives of a scientific study. In this particular case the analysis throws light on a major public health problem-the carnage due to auto accidents. Nowadays the catalogue of statistical methods is so very extensive that a working scientist is somewhat less than overjoyed at the prospect