758,413 research outputs found

    Statistical aspects of experimental designs implemented in designs

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    The report describes the statistical aspects of experimental designs used in an project

    Designing and Deploying Online Field Experiments

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    Online experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often requires sophisticated experimental designs, iterative refinement, and careful logging and analysis. Few tools exist that support these needs. We thus introduce a language for online field experiments called PlanOut. PlanOut separates experimental design from application code, allowing the experimenter to concisely describe experimental designs, whether common "A/B tests" and factorial designs, or more complex designs involving conditional logic or multiple experimental units. These latter designs are often useful for understanding causal mechanisms involved in user behaviors. We demonstrate how experiments from the literature can be implemented in PlanOut, and describe two large field experiments conducted on Facebook with PlanOut. For common scenarios in which experiments are run iteratively and in parallel, we introduce a namespaced management system that encourages sound experimental practice.Comment: Proceedings of the 23rd international conference on World wide web, 283-29

    Optimal experimental designs for inverse quadratic regression models

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    In this paper optimal experimental designs for inverse quadratic regression models are determined. We consider two different parameterizations of the model and investigate local optimal designs with respect to the cc-, DD- and EE-criteria, which reflect various aspects of the precision of the maximum likelihood estimator for the parameters in inverse quadratic regression models. In particular it is demonstrated that for a sufficiently large design space geometric allocation rules are optimal with respect to many optimality criteria. Moreover, in numerous cases the designs with respect to the different criteria are supported at the same points. Finally, the efficiencies of different optimal designs with respect to various optimality criteria are studied, and the efficiency of some commonly used designs are investigated.Comment: 24 page

    Computing Optimal Experimental Designs via Interior Point Method

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    In this paper, we study optimal experimental design problems with a broad class of smooth convex optimality criteria, including the classical A-, D- and p th mean criterion. In particular, we propose an interior point (IP) method for them and establish its global convergence. Furthermore, by exploiting the structure of the Hessian matrix of the aforementioned optimality criteria, we derive an explicit formula for computing its rank. Using this result, we then show that the Newton direction arising in the IP method can be computed efficiently via Sherman-Morrison-Woodbury formula when the size of the moment matrix is small relative to the sample size. Finally, we compare our IP method with the widely used multiplicative algorithm introduced by Silvey et al. [29]. The computational results show that the IP method generally outperforms the multiplicative algorithm both in speed and solution quality

    Experimental and quasi-experimental designs

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    Researchers within the field of applied linguistics have long used experiments to investigate cause-effect relationships regarding the use and learning of second languages (L2s). In experimental research, one or more variables are altered and the effects of this change on another variable are examined. This change or experimental manipulation is usually referred to as the treatment. Researchers typically draw upon either experimental or quasi-experimental research designs to determine whether there is a causal relationship between the treatment and the outcome. This chapter outlines key features and provides examples of common experimental and quasi-experimental research designs. We also make recommendations for how experimental designs might best be applied and utilized within applied linguistics research
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