Statistics for evaluating pre-post change: Relation between change in the distribution center and change in the individual scores

In a number of scientific fields, researchers need to assess whether a variable has changed between two time points. Average-based change statistics (ABC) such as Cohen's d or Hays' ω2 evaluate the change in the distributions' center, whereas Individual-based change statistics (IBC) such as the Standardized Individual Difference or the Reliable Change Index evaluate whether each case in the sample experienced a reliable change. Through an extensive simulation study we show that, contrary to what previous studies have speculated, ABC and IBC statistics are closely related. The relation can be assumed to be linear, and was found regardless of sample size, pre-post correlation, and shape of the scores' distribution, both in single group designs and in experimental designs with a control group. We encourage other researchers to use IBC statistics to evaluate their effect sizes because: (a) they allow the identification of cases that changed reliably; (b) they facilitate the interpretation and communication of results; and (c) they provide a straightforward evaluation of the magnitude of empirical effects while avoiding the problems of arbitrary general cutoffs.EE was supported by the scholarship FPI-UAM 2011 (granted by Universidad Autónoma de Madrid). The publication fee was partially supported by the Library of UC, Davi

Decision-making through sustainability

From immemorial time, dams have contributed significantly for the progress of civilizations. For this reason, nowadays, there is a vast engineering heritage. Over the years, these infrastructures can present some ordinary maintenance issues associated with their normal operation or with ageing processes. Normally, these problems do not represent an important risk for the structure, but they have to be attended. To do it, owners of dams have to finance many ordinary interventions. As it is impossible to carry out all of them at the same time, managers have to make a decision and select the most “important” ones. However, it is not easy because interventions usually have very different natures (for example: repair a bottom outlet, change gates, seal a crack...) and they cannot use a classical risk analysis for these type of interventions. The authors, who are aware this problem, present, in this paper, a multi-criteria decision-making system to prioritize these interventions with the aim of providing engineers a useful tool, with which they can prioritize the interventions from the most important to the least. To do it, the authors have used MIVES. This tool defines the Prioritization Index for the Management of Hydraulic Structures (PIMHS), which assesses, in two phases, the contribution to sustainability of each intervention. The first phase measures the damage of the dam, and the second measures the social, environmental and economic impacts. At the end of the paper, a case of study is presented where some interventions are evaluated with PIMHS.Postprint (published version