BACKGROUND: Studies on how to better measure change have been published at least since the third decade of the last century, but no general indicator or strategy of measurement is currently agreed upon. The aim of this study is to propose a new indicator, the indicator of positive change, as an option for the assessment of change when ordinal scores are used in pretest and posttest designs. METHODS: The basic idea is to measure the proportion of possible (positive) change inside a group that can be attributed to an intervention. The approach is based on the joint distribution of the before and after scores (differences), represented by the cells (i, j) of a contingency table m × m (m is the number of classes of the ordinal measurement scale; i and j are the lines and columns of the table, respectively). By convention, higher classes are the most unfavorable on the scale such that subjects that improve "migrate" from the higher to the lower classes as a result of an intervention and vice versa. RESULTS: The introduced indicator offers a new strategy for the analysis of change when dealing with repeated measurements of the same subject, assuming that the measured variable is ordinal (e.g., clinician-rating scales). CONCLUSION: The presented approach is easily interpretable and avoids the problems that arise, for instance, in those cases where a large concentration of high/low scores is present at the baseline.
BACKGROUND: Studies on how to better measure change have been published at least since the third decade of the last century, but no general indicator or strategy of measurement is currently agreed upon. The aim of this study is to propose a new indicator, the indicator of positive change, as an option for the assessment of change when ordinal scores are used in pretest and posttest designs. METHODS: The basic idea is to measure the proportion of possible (positive) change inside a group that can be attributed to an intervention. The approach is based on the joint distribution of the before and after scores (differences), represented by the cells (i, j) of a contingency table m × m (m is the number of classes of the ordinal measurement scale; i and j are the lines and columns of the table, respectively). By convention, higher classes are the most unfavorable on the scale such that subjects that improve "migrate" from the higher to the lower classes as a result of an intervention and vice versa. RESULTS: The introduced indicator offers a new strategy for the analysis of change when dealing with repeated measurements of the same subject, assuming that the measured variable is ordinal (e.g., clinician-rating scales). CONCLUSION: The presented approach is easily interpretable and avoids the problems that arise, for instance, in those cases where a large concentration of high/low scores is present at the baseline.
Authors: Mirjam A G Sprangers; Carol M Moinpour; Timothy J Moynihan; Donald L Patrick; Dennis A Revicki Journal: Mayo Clin Proc Date: 2002-06 Impact factor: 7.616