Participant observation | EPFL Graph Search

Participant observation is one type of data collection method by practitioner-scholars typically used in qualitative research and ethnography. This type of methodology is employed in many disciplines, particularly anthropology (incl. cultural anthropology and European ethnology), sociology (incl. sociology of culture and cultural criminology), communication studies, human geography, and social psychology. Its aim is to gain a close and intimate familiarity with a given group of individuals (such as a religious, occupational, youth group, or a particular community) and their practices through an intensive involvement with people in their cultural environment, usually over an extended period of time. The concept "participant observation" was first coined in 1924 by Eduard C. Lindeman (1885-1953), an American pioneer in adult education influenced by John Dewey and Danish educator-philosopher N.F.S.Grundtvig, in his book "Social Discovery: An Approach to the Study of Functional Groups." T

Compte rendu de formation continue : Certificat en gestion de documentation et de bibliothèque

Anne Cherbuin, Isabelle Dubot, François Schmitt

En 2002-2003, nous avons eu la chance de suivre la première session de cette filière certifiante d'une année qui s'adresse spécifiquement aux professionnels du domaine bibliothéconomique, prêts à se réinvestir dans un effort de longue haleine afin de remettre à jour leurs connaissances et de compléter leurs acquis par des cours et des travaux pratiques dispensés par des spécialistes. Cette formation, proposée conjointement par les Universités de Fribourg et Genève et l'Association des Bibliothèques et Bibliothécaires suisses, s'est articulée autour de 4 modules : Module 1 : Gestion des collections Module 2 : Gestion des nouvelles technologies de l'information Module 3 : Gestion du changement et de la qualité Module 4 : Information et communication Travail final Pour terminer ce cycle de formation, les cours ont donné lieu à des travaux appliqués ou applicables au sein de l'institution des participants, soit la Bibliothèque Centrale dans notre cas. Après validation d'un expert dans le domaine traité, nous avons ici, le plaisir de mettre à votre disposition nos travaux finaux : . La Bibliothèque Centrale de l'EPFL remue ses méninges et ses meubles ! ou : Un petit réaménagement avant la création d'un grand Learning Center / Anne Cherbuin . Quand la consultation ravage les documents : un concept de sensibilisation des usagers / Isabelle Dubot . MELETIS REDIVIVUS : Valorisation du fonds précieux de la Bibliothèque centrale de l'Ecole polytechnique fédérale, Lausanne / François Schmitt

2003

The Confidence Database

Nathan Quentin Faivre, Xiao Hu, Inaki Asier Iturrate Gil, Michael Eric Anthony Pereira, Caroline Peters

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at ) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects.

NATURE PUBLISHING GROUP2020

Robust Parameter Estimation for the Ornstein-Uhlenbeck Process

Sonja Rieder

In this thesis, we treat robust estimation for the parameters of the Ornstein–Uhlenbeck process, which are the mean, the variance, and the friction. We start by considering classical maximum likelihood estimation. For the simulation study, where we also investigate the choice of the time lag, we use the method of moment (MoM) estimator as initial estimator for the friction parameter of the maximum likelihood estimator (MLE). However, in several aspects the MLE is not robust. For robustification, we first derive elementary M-estimates by extending the method of M-estimation from Huber (1981). We use an intuitively robustified MoM estimate as initial estimate and compare by means of simulation the M-estimate with the MLE. This approach is, however, only ad-hoc since Huber’s minimum Fisher information and minimax asymptotic variance theory remains incomplete for simultaneous location and scale, and does not cover more general models (as for example the Ornstein–Uhlenbeck process). A more general robustness concept due to Kohl et al. (2010), Rieder (1994), and Staab (1984) is based on local asymptotic normality (LAN), asymptotically linear (AL) estimates, and shrinking neighborhoods. We then apply this concept to the Ornstein–Uhlenbeck process. As a measure of robustness, we consider the maximum asymptotic mean square error (maxasyMSE), which is determined by the influence curve (IC) of AL estimates. The IC represents the standardized influence of an individual observation on the estimator given the past. For two kind of neighborhoods (average and average square neighborhoods) we obtain optimally robust ICs. In case of average neighborhoods, their graph exhibits surprising, redescending behavior. For average square neighborhoods the graph is between the one of the elementary M-estimates and the MLE. Finally, we discuss the estimator construction, that is, the problem of constructing an estimator from the family of optimal ICs. We carry out in our context the One-Step construction dating back to LeCam and use both an intuitively robustified MoM estimate and the elementary M-estimate as initial estimate. This results in optimally AL estimates (for average and average square neighborhoods). By means of simulation we then compare the different estimators: MLE, elementary M-estimates, and optimally AL estimates. In addition, we give an application to electricity prices.

EPFL2012

Robust Parameter Estimation for the Ornstein-Uhlenbeck Process

Sonja Rieder

EPFL2012