Measurement

ANALYSIS WORKGROUP
Our mission is to foster the improvements in the analysis of health related phenomena among older minority; encourage the development of methods and measures that better capture the health and determinants of health of diverse elders; promote collaboration between RCMAR sites on analysis issues; and disseminate new knowledge in this area.

Measurement


Overviews of Measurement Issues
This article promotes a better understanding of the nature of measurement, the special problems posed by measurement in the social sciences, and the inevitable limitations on inferences in science (so that results are not overinterpreted), by using the measurement of blood pressure as an example. As it is necessary to raise questions about the meaning and extent of the validity of something as common as measured blood pressure, even more serious questions are unavoidable in relation to other commonly used measures in social science. The central issue is the validity of the inferences about the construct rather than the validity of the measure per se.


This article presents five examples of different ways of measuring health disparities to show how different approaches can lead to different conclusions. It discusses the ways in which the approaches are based on normative judgments, through either the selection of one measurement strategy to the exclusion of others or the selection of the type, significance, or weight assigned to the variables being measured. Quantitative estimates of the magnitude, direction, and rate of change of health inequalities play a crucial role in creating and assessing policies aimed at eliminating the disproportionate burden of disease in disadvantaged populations. But it is generally assumed that the measurement of health inequalities is a value-neutral process, providing objective data that are then interpreted using normative judgments about whether a particular distribution of health is just, fair, or socially acceptable. Because values implicit in the generation of health inequality measures may lead to radically different interpretations of the same underlying data, researchers should explicitly consider and transparently discuss the normative judgments underlying their measures.


updated April 2013