23 papers · ranked by Valyu relevance
Mohsen Tavakol, Angela Wetzel
Introduction Factor analysis (FA) allows us to simplify a set of complex variables or items using statistical procedures to explore the underlying dimensions that explain the relationships between the multiple variables/items. For example, to explore inter-item relationships for a 20-item instrument, a basic analysis…
Iosifina Pournara, Lorenz Wernisch
Background Most existing algorithms for the inference of the structure of gene regulatory networks from gene expression data assume that the activity levels of transcription factors (TFs) are proportional to their mRNA levels. This assumption is invalid for most biological systems. However, one might be able to…
Justin Philip Tuazon, Gia Mizrane Abubo, Jos 'e Luis Montiel Olea
Factor analysis is a way to characterize the relationships between many observable variables in terms of a smaller number of unobservable random variables. However, the application of factor models and its success can be subjective or difficult to gauge, since the factor model is not identifiable. Thus, there is a need…
Tim Cosemans, Yves Rosseel, Sarah Gelper
Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We…
Nadine Correia Santos, Patrício Soares Costa, Liliana Amorim, Pedro Silva Moreira + 4 more
'Pedro Silva Moreira' 'Pedro Cunha' 'Jorge Cotter' 'Nuno Sousa' 'Hemachandra Reddy'] Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051…
Zenon Gniazdowski
The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed. The problem of determining the number of principal components in PCA and factors…
Yoshikazu Terada
For factor analysis, many estimators, starting with the maximum likelihood estimator, are developed, and the statistical properties of most estimators are well discussed. In the early 2000s, a new estimator based on matrix factorization, called Matrix Decomposition Factor Analysis (MDFA), was developed. Although the…
Muhammad Ardiyansyah, Luca Sodomaco
The factor analysis model is a statistical model where a certain number of hidden random variables, called factors, affect linearly the behaviour of another set of observed random variables, with additional random noise. The main assumption of the model is that the factors and the noise are Gaussian random variables.…
Cees van der Eijk, Jonathan Rose
This paper undertakes a systematic assessment of the extent to which factor analysis the correct number of latent dimensions (factors) when applied to ordered-categorical survey items (so-called Likert items). We simulate 2400 data sets of uni-dimensional Likert items that vary systematically over a range of conditions…
Mehdi Momen, Madhav Bhatta, Waseem Hussain, Haipeng Yu + 1 more
Inferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multi-dimensional phenotypes are limited…
Erik-Jan van Kesteren, Rogier A. Kievit
Dimension reduction is widely used and often necessary to reduce high dimensional data to a small number of underlying variables – factors or components – to make data analyses and their interpretation tractable. One popular technique is Exploratory Factor Analysis (EFA), which extracts factors when the underlying…
Glaucia Cristina Rodrigues Nascimento, Marcela Baraúna Magno, Giseon Heo, David Normando
High-dimensional data hinder sample visualization and limit exploration of data1. In these cases, we can make use of multivariate analysis techniques, such as Factor Analysis (FA) and/or Principal Component Analysis (PCA), to reduce a complex data set to one of lower dimensions so as to reveal any hidden features and…
Luis Cid, Diogo Monteiro, Diogo Santos Teixeira, Anastasiia Evmenenko + 5 more
'Anastasiia Evmenenko' 'Ana Andrade' 'Teresa Bento' 'Anabela Vitorino' 'Nuno Couto' 'Filipe Rodrigues'] Translating and validating measurement instruments in sport and exercise psychology is not an easy task. Rather, it is a task that requires effort and time, for the process is not limited to a simple translation to…
Britta Velten, Jana M. Braunger, Damien Arnol, Ricard Argelaguet + 1 more
Factor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor analysis assume independence of the observed samples, an assumption that fails in emerging spatio-temporal profiling…
Tyler J. VanderWeele, C. J. K. Batty
Summary. It is shown, with two sets of survey items that separately load on two distinct factors, independent of one another conditional on the past, that if it is the case that at least one of the factors causally affects the other, then, in many settings, the process will converge to a factor model in which a single…
Matteo Barigozzi, Marc Hallin
Dynamic factor models have been developed out of the need of analyzing and forecasting time series in increasingly high dimensions. While mathematical statisticians faced with inference problems in high-dimensional observation spaces were focusing on the so-called spiked-model-asymptotics, econometricians adopted an…
Suk-Heung Song, Herb Ryan, Jens Hoefflin, Taeyoon Kyung + 3 more
Analytical technologies for engineered biological systems hold great promise in addressing various challenges in modern pharmaceuticals and biomedical therapies. These endeavors often follow a design-build-test-learn approach, utilizing biological data from genetic circuits, signal pathways, metabolites, and proteins…
Ingrid Måge, Christina Steppeler, Ingunn Berget, Jan Erik Paulsen + 1 more
This paper presents a strategy for statistical analysis and interpretation of longitudinal intervention effects on bacterial communities. Data from such experiments often suffers from small sample size, high degree of irrelevant variation, and missing data points. Our strategy is a combination of multi-way…
Juerg Straubhaar, Alexandria D’Souza, Zachary Niziolek, Bogdan Budnik
Single-cell analysis has clearly established itself in biology and biomedical fields as an invaluable tool that allows one to comprehensively understand the relationship between cells, including their types, states, transitions, trajectories, and spatial position. Scientific methods such as fluorescence labeling…
Pär Jonsson, Benny Björkblom, Elin Chorell, Tommy Olsson + 1 more
Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a…
Authors not listed
Chiral 2D metal halide perovskites (MHPs) hold promise for next-generation spin-optoelectronic applications. While still in early-stage development, significant efforts have focused on optimizing the dissymmetry factor (g), particularly in terms of their circular dichroism (gabs), which quantifies the chiroptical…
Authors not listed
Deciphering the correct mechanism governing certain phenomenon in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic flow (in the…
James Swift, Matthew Arran Turner, James Christopher Reynolds
A rapid headspace analysis method for the authenticity testing of whiskies of different brands and years was developed for a low cost, deployable atmospheric pressure ionisation mass spectrometer, which required minimal sample preparation. Principal component analysis was applied to the time-averaged mass spectra, the…