Search results for "Methodology"
showing 10 items of 852 documents
PRÁCTICAS DEL ANÁLISIS FACTORIAL EXPLORATORIO (AFE) EN LA INVESTIGACIÓN SOBRE CONDUCTA DEL CONSUMIDORY MARKETING
2012
El Análisis Factorial Exploratorio (AFE) es una de las técnicas estadísticas más utilizadas en la investigación social. El principal objetivo de este trabajo es describir las prácticas más utilizadas por los investigadores en el área de la conducta del consumidor y el marketing. Mediante una metodología de revisión documental se analizan las prácticas de AFE en cinco revistas españolas dedicadas a dicha temática (2000-2010). Se analizan las elecciones de los investigadores relacionadas con el modelo factorial, el criterio de retención, la rotación, la interpretación de los factores y otras cuestiones relevantes para el análisis factorial. Los resultados sugieren que los investigadores ejecu…
Scientific studies on writing in second language learning (2005-2017)
2019
Este artículo revisa críticamente las líneas temáticas que desde 2005 hasta 2017 se han publicado sobre Second Language Writing (SLW) con el objetivo de presentar un análisis de las tendencias predominantes en las publicaciones indexadas en SCOPUS. Como metodología de estudio se ha procedido a la selección de aquellas contribuciones que han recibido 60 citas o más. De la revisión de literatura científica realizada se concluye que son tres las líneas de investigación mayoritarias: el proceso de composición o redacción del texto escrito; la retroalimentación que el docente ofrece al aprendiz; y la escritura en línea de una L2. Las conclusiones señalan la necesidad de profundizar en la investi…
La escritura expresiva en la orientación. Una metodología educativa para la construcción del proyecto de vida personal y profesional
2020
La scrittura espressiva adottata a scuola come strumento di autorientamento dell’adolescente di 17-18 anni non sostituisce l’intervento educativo dell’insegnante, piuttosto può considerarsi una tecnica efficace per raccogliere tutte le informazioni necessarie con cui formare, con il consenso dell’alunno e insieme a lui, un quadro esaustivo in cui includere lo stato attuale delle sue competenze, le sue attitudini, i suoi interessi, le sue motivazioni, la sua storia formativa, il suo profilo di personalità, i suoi limiti, nonché i suoi obiettivi e le modalità concrete con cui egli pensa di raggiungerli. Essa è pertanto un metodo educativo con cui gli studenti che si approssimano a svolgere gl…
On the interpretability and computational reliability of frequency-domain Granger causality
2017
This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, s…
Group Importance Sampling for particle filtering and MCMC
2018
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discus…
Unsupervised Anomaly and Change Detection With Multivariate Gaussianization
2022
Anomaly detection (AD) is a field of intense research in remote sensing (RS) image processing. Identifying low probability events in RS images is a challenging problem given the high dimensionality of the data, especially when no (or little) information about the anomaly is available a priori. While a plenty of methods are available, the vast majority of them do not scale well to large datasets and require the choice of some (very often critical) hyperparameters. Therefore, unsupervised and computationally efficient detection methods become strictly necessary, especially now with the data deluge problem. In this article, we propose an unsupervised method for detecting anomalies and changes …
Analyzing multidimensional movement interaction with generalized cross-wavelet transform
2021
Humans are able to synchronize with musical events whilst coordinating their movements with others. Interpersonal entrainment phenomena, such as dance, involve multiple body parts and movement directions. Along with being multidimensional, dance movement interaction is plurifrequential, since it can occur at different frequencies simultaneously. Moreover, it is prone to nonstationarity, due to, for instance, displacements around the dance floor. Various methodological approaches have been adopted for the study of human entrainment, but only spectrogram-based techniques allow for an integral analysis thereof. This article proposes an alternative approach based upon the cross-wavelet transfor…
Do-search -- a tool for causal inference and study design with multiple data sources
2020
Epidemiologic evidence is based on multiple data sources including clinical trials, cohort studies, surveys, registries, and expert opinions. Merging information from different sources opens up new possibilities for the estimation of causal effects. We show how causal effects can be identified and estimated by combining experiments and observations in real and realistic scenarios. As a new tool, we present do-search, a recently developed algorithmic approach that can determine the identifiability of a causal effect. The approach is based on do-calculus, and it can utilize data with nontrivial missing data and selection bias mechanisms. When the effect is identifiable, do-search outputs an i…
On resampling schemes for particle filters with weakly informative observations
2022
We consider particle filters with weakly informative observations (or `potentials') relative to the latent state dynamics. The particular focus of this work is on particle filters to approximate time-discretisations of continuous-time Feynman--Kac path integral models -- a scenario that naturally arises when addressing filtering and smoothing problems in continuous time -- but our findings are indicative about weakly informative settings beyond this context too. We study the performance of different resampling schemes, such as systematic resampling, SSP (Srinivasan sampling process) and stratified resampling, as the time-discretisation becomes finer and also identify their continuous-time l…
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
2017
Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or sy…