experimental design

A predictive measure of the additional loss of a non-optimal action under multiple priors

In Bayesian decision theory, the performance of an action is measured by its posterior expected loss. In some cases it may be convenient/necessary to use a non-optimal decision instead of the optimal one. In these cases it is important to quantify the additional loss we incur and evaluate whether to use the non-optimal decision or not. In this article we study the predictive probability distribution of a relative measure of the additional loss and its use to define sample size determination criteria in one-sided testing.

Partial sleep deprivation and food intake in participants reporting binge eating symptoms and emotional eating: preliminary results of a quasi-experimental study

Sleep deprivation consistently increases food intake. This study aimed to evaluate the effect of partial sleep deprivation on food intake in individuals reporting binge eating, controlling for self-reported depressive emotional eating. Fourteen young adults reporting binge eating symptoms and 14 controls denying any eating disorders symptoms were offered a large and varied breakfast after a night of habitual sleep (HN) and after a night of partial sleep deprivation (DN). Food intake was unobtrusively measured while daily food intake was measured via a food diary.

Evaluating intervention programs with a pretest-posttest design. A structural equation modeling approach

A common situation in the evaluation of intervention programs is the researcher's possibility to rely on two waves of data only (i.e., pretest and posttest), which profoundly impacts on his/her choice about the possible statistical analyses to be conducted. Indeed, the evaluation of intervention programs based on a pretest-posttest design has been usually carried out by using classic statistical tests, such as family-wise ANOVA analyses, which are strongly limited by exclusively analyzing the intervention effects at the group level.

Chemometrics in analytical chemistry-part I: history, experimental design and data analysis tools

Chemometrics has achieved major recognition and progress in the analytical chemistry field. In the first part of this tutorial, major achievements and contributions of chemometrics to some of the more important stages of the analytical process, like experimental design, sampling, and data analysis (including data pretreatment and fusion), are summarised. The tutorial is intended to give a general updated overview of the chemometrics field to further contribute to its dissemination and promotion in analytical chemistry.

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