24 papers · ranked by Valyu relevance
Killian Asampana Asosega, Atinuke Olusola Adebanji, Eric Nimako Aidoo, Ellis Owusu-Dabo
Introduction Multilevel models have gained immense popularity across almost every discipline due to the presence of hierarchy in most data and phenomena. In this paper, we present a systematic review on the adoption and application of multilevel models and the important information reported on the results generated…
George Leckie
Multilevel models (mixed-effect models or hierarchical linear models) are now a standard approach to analysing clustered and longitudinal data in the social, behavioural and medical sciences. This review article focuses on multilevel linear regression models for continuous responses (outcomes or dependent variables).…
Yoo Min Park, Youngho Kim
to self-rated health status in South Korea Authors: ['Yoo Min Park' 'Youngho Kim'] Background This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related…
Samuel R. Lucas
The multilevel model has become a staple of social research. I textually and formally explicate sample design features that, I contend, are required for unbiased estimation of macro-level multilevel model parameters and the use of tools for statistical inference, such as standard errors. After detailing the limited and…
Jimmie Leppink
A substantial part of medical education research focuses on learning in teams (e.g., departments, problem-based learning groups) or centres (e.g., clinics, institutions) that are followed over time. Individual students or employees sharing the same team or centre tend to be more similar in learning than students or…
Nivedita Bhaktha
Multilevel modeling, also known as hierarchical or mixed effects modeling, is a statistical technique used for fitting nested or clustered data. Multilevel modeling aids in examining associations between variables measured at different levels of the data structure (Raudenbush & Bryk, [3]; Hox et al., [2]). In these…
Tom Edinburgh, Ari Ercole, Stephen J. Eglen
Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model…
Mohammad Javad Kharazifard, Kurosh Holakouie-Naieni, Mohammad Ali Mansournia
'Mohammad Ali Mansournia'] Multilevel analysis which was primarily introduced to deal with hierarchical data was later applied extensively for research in other fields of science and not only for nested data, but also for repeated measurements or clustered trials. This method of statistical analysis was applied in…
Lucia Modugno, Simone Giannerini
In this paper we study the performance of the most popular bootstrap schemes for multilevel data. Also, we propose a modified version of the wild bootstrap procedure for hierarchical data structures. The wild bootstrap does not require homoscedasticity or assumptions on the distribution of the error processes. Hence…
Amira Ibrahim El-Desokey
In this paper, I outline several conceptual and methodological issues related to modeling individual and group processes embedded in clustered/hierarchical data structures. We position multilevel modeling techniques within a broader set of univariate and multivariate methods commonly used to study different types of…
Sooyong Lee, Soyoung Kim
Introduction This study compares Bayesian random coefficient prediction (BRCP) and Bayesian latent interaction (BINT) models to detect moderated mediation effects in multilevel contexts. Materials and methods We evaluated the performance of these models under various conditions using empirical data from the Trends in…
Tsz Chai Fung, Spark C. Tseung
Multilevel data are prevalent in many real-world applications. However, it remains an open research problem to identify and justify a class of models that flexibly capture a wide range of multilevel data. Motivated by the versatility of the mixture of experts (MoE) models in fitting regression data, in this article we…
Steven Geinitz, Reinhard Furrer
Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to the case where all levels of a factor are present in the data (i.e. the population of factor levels has been…
Zenab Tamimy, Sofieke T. Kevenaar, Jouke Jan Hottenga, Michael D. Hunter + 6 more
The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is…
Dandan Chen Kaptur, Yiqing Liu, Bradley Kaptur, Nicholas Peterman + 3 more
Survey Data: Via Multilevel Modeling Authors: ['Dandan Chen Kaptur' 'Yiqing Liu' 'Bradley Kaptur' 'Nicholas Peterman' 'Jinming Zhang' 'Justin L. Kern' 'Carolyn J. Anderson'] Few health-related constructs or measures have received critical evaluation in terms of measurement equivalence, such as self-reported health…
Dániel Czégel, István Zachar, Eӧrs Szathmáry
Complexity of life forms on Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level evolutionary units. Although this process has been likened to the recursive…
Tyler R. Bonnell, Robert Michaud, Angélique Dupuch, Véronique Lesage + 1 more
Photo identification of individuals within a population is a common data source that is becoming more common given technological advances and the use of computer vision and machine learning to re-identify individuals. These data are collected through hand-held cameras, drones, and camera traps, and often come with…
Kristen A. McLaurin, Amanda J. Fairchild, Dexin Shi, Rosemarie M. Booze + 1 more
The translation of preclinical studies to human applications is associated with a high failure rate, which may be exacerbated by limited training in experimental design and statistical analysis. Nested experimental designs, which occur when data have a multilevel structure (e.g., in vitro: cells within a culture dish…
Johannes Oberpriller, Melina de Souza Leite, Maximilian Pichler
Biological data are often intrinsically hierarchical. Due to their ability to account for such dependencies, mixed-effects models have become a common analysis technique in ecology and evolution. While many questions around their theoretical foundations and practical applications are solved, one fundamental question is…
Andrew P. Woodward
Non-compartmental analysis (NCA) is a popular strategy for obtaining estimates of pharmacokinetic parameters, while requiring both minimal structural assumptions, and limited input by the analyst. As typically applied, its scope and depth are constrained by its statistical simplicity. Embedding the NCA within a…
Theresa Ramelot, Roberto Tejero, Gaetano Montelione
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
Prashanth Athri, Vidhya Murali, Pradyumna Y Muralidhar, Cassandra Königs + 4 more
- 1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India - 2. PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India - 3. Bioinformatics and Medical Informatics, Bielefeld University…
Zhiwen Pan, Jan Dellith, Lothar Wondraczek
Understanding the multivariate origin of physical properties is particularly complex for polyionic glasses. As a concept, the term genome has been used to describe the entirety of structure-property relations in solid materials, based on functional genes acting as descriptors for a particular property, for example, for…