However, individual health can be “clustered” due to the influence of shared contexts or “contagious” due to the transmission of ideas or pathogens, violating assumptions of independence. It covers how to understand, fit, and interpret these models in R, explore both nested and crossed data structures, incorporate group-level predictors, quantify explained variance, and perform power analyses. , person, group, etc. Feb 9, 2024 · Course aim By the end of this course you will be able to specify, estimate and interpret multilevel models, ranging from simple two-level random intercept models to more complex ones (including three-level, logistic or longitudinal models). Available courses LEMMA (Learning environment for multilevel methodology and applications) Enrolment Key: lemma The enrolment key is in all lower case letters. Requisite: course 250C. This three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R, or Stata. Maximum likelihood and Bayesian inference and computation. Andrew Gelman, Ph. You will also gain practical competency in statistical software for analyzing data. Introduction to Multilevel Models Nested data structures. Understand the key elements of Multilevel Modelling (MLM and Structural Equation Modelling (SEM) and how these can be used for analysing longitudinal data. Focus on model fitting and interpretation. MLM allows the user to analyse how outcomes are influenced by factors acting at multiple levels. Model Oct 20, 2025 · This course will introduce a variety of extensions to MLM, including cutting-edge multilevel structural equation modeling (MSEM) to handle complex designs and modeling objectives. Preparation and description of variables for use in Multilevel Model B. Methods for modeling dependent data structures. Mixed models may contain more than one level of nested random effects, and hence these Oct 10, 2025 · Bayesian Multilevel Modelling using brms for Ecologists (BMME01) is an intensive 10-day live online course designed to provide ecologists, environmental scientists, and applied researchers with a solid grounding in Bayesian hierarchical modelling using the brms package in R. This course will teach you the theoretical basics of multilevel modelling and some important methodological and statistical issues. Feb 12, 2025 · Level: Professtional (P) This two-day online course is designed to give participants an introduction to the theory and application of multilevel regression models for clustered or hierarchical data using the statistical software (participants can choose between R, Stata and MLwiN). Firstly, it is more efficient and uses more of the available information than the alternative approaches of distributing contextual Structured, PhD‑level training on Multilevel Omega Reliability, Cross-sectional versus lagged effects, Within-Group Agreement as Between-Group (Dispersion) Variable, Moderation of random intercepts by random variances, Stable versus Unstable Factors, Random variances, Coding for day/night transitions and similar periodic effects, Dynamic Structural Equation Modeling (DSEM), Multilevel Model The course will introduce regression models for multilevel data across a wide variety of contexts, with a focus on data management, estimation, and interpretation. I've found two multilevel modeling virtual courses that I wonder if anyone has experience with, and could provide their opinion? The first is a 4-day livestream by Statistical Horizons and called Multilevel and Mixed Models Using R. Learn to handle hierarchical and nested data, interpret models, and apply best practices in research and industry. Hierarchical data structures arises from sampling units at multiple levels, wherein lower-level units are nested within higher-level units. Basic Multilevel Models Random-effects regression. Mixed models contain both fixed effects analogous to the coefficients in standard regression models and random effects not directly estimated but instead summarized through the unique elements of their variance-covariance matrix. Then, we discuss the main concepts and issues in multilevel analysis. 3 days ago · Comprehensive guide to mixed-effects (multilevel) models, with applications to psychological research, longitudinal data, and psychometric testing. Holmes Finch & Brian French teach multilevel modeling for beginners in this on-demand workshop. , days within people, students within classroom Learn multilevel structural equation modeling (MSEM) and Mplus applications in this online seminar with Kristopher Preacher, Ph. Figuring out the nested structure of your data Multilevel Modeling Methods with Introductory and Advanced Applications provides a cogent and comprehensive introduction to the area of multilevel modeling for methodological and applied researchers as well as advanced graduate students. May 1, 2024 · This tutorial begins with a general introduction to multilevel regression models for continuous variables. MLwiN and Multilevel modelling Multilevel modelling has rapidly become established as the appropriate tool for modelling data with complex hierarchical structures. Slopes as Outcomes and Model Evaluation Slopes as outcomes. Jun 6, 2025 · This two-day course provides a theoretical and practical introduction to multilevel and mixed effects models, including linear and generalized linear variants. Variance component models with fixed and random effects. Mixed models contain both fixed effects, analogous to regression coefficients, and random effects, effects that vary across clusters. In this module we consider the whole process of conducting a research project using multilevel modelling, taking as an example a study of ethnic differences in educational achievement and progress. Timothy Hellwig, The Political Methodologist, 'Data Analysis Using Regression and Multilevel/Hierarchical Models is the book I wish I had in graduate school. This practice-oriented course focuses on the application and interpretation of multilevel models and longitudinal data analysis. We focus on two-level linear and logistic regression models for clustered cross-sectional (individuals nested This paper illustrates how multilevel functional models can detect and characterize biomechanical changes along different sport training sessions. . Recommended prerequisite: R programming skills. Fee info Fee 495 EUR, ECPR Member - check if your institution is a member on our website. Overview Software Description Websites Readings Courses OverviewMost of our statistical models rely on the assumption that each observation is independent. To do so, we review the This workshop provides a practical approach to understanding multilevel and dyadic modeling. Fourth Edition, Stata Press. The random-effects ANOVA model. She has also taught a number of graduate-level quantitative methods courses on general linear models, Bayesian inference, social network models, and statistical consulting. Even though there is no mathematical prerequisite, we still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson Lecture, four hours. The research process starts with the formulation of research questions as hypotheses that can be tested using multilevel models. This course deals with the analysis of data from hierarchically structured populations (e. It is a practical and timely resource for public health and health services researchers, statisticians, and students interested in the relationships between contexts and behaviour. Learn advanced extensions to multilevel modeling in this online course with Kristopher Preacher, Ph. Students who are admitted to study programmes A comparison of strategies for analyzing longitudinal data, including repeated measures ANOVA, mixed models analysis, regression, and multilevel modeling Multilevel models for analyzing longitudinal data Models for evaluating changes in “elevation” and “slope” over time. G Orfield Multilevel And Longitudinal Modeling Using Stata Volume Ii Categorical Responses Counts And Survival Third Edition: Multilevel and Longitudinal Modeling Using Stata: Categorical responses, counts, and survival S. This “second course” in MLM will introduce a variety of MLM extensions, including cutting-edge multilevel structural equation modeling (MSEM) to handle complex designs and modeling objectives. It is aimed at graduate students, postdocs, and faculty members who are familiar with the basic principles of OLS regression and are looking to extend their knowledge of regression to nested data (e. M. The Centre for Multilevel Modelling has been at the forefront of the development and dissemination of multilevel modelling since the 1980s and has established a world-wide reputation in this area. Multilevel modelling is now a very widely used method in quantitative social science research for analysing multilevel, longitudinal, and other data with complex hierarchical structures. Using the brms package, participants learn to build generalised linear and multilevel models, incorporate priors, account for spatial and temporal autocorrelation, and conduct joint species distribution We would like to show you a description here but the site won’t allow us. In this course, PhD candidates will get an introduction into the theory of multilevel modelling, focusing on two level multilevel models with a 'continuous' response variable. Multilevel (random-effects) extensions QuantFish is dedicated to providing courses that improve your analytic skills with accessible lessons from the world's leading methodologists. An applied textbook on generalized linear models and multilevel models for advanced undergraduates, featuring many real, unique data sets. Then we proceed by introducing panel regression models and apply them in the context of social sciences. Free online multilevel modelling course (or 'LEMMA course' - Learning Environment for Multilevel Methodology and Applications). Using multilevel models to analyze “treatment effects” over time. Multilevel Structural Equation Modeling Multilevel structural equation modeling (MSEM) is the most general and flexible instantiation of the general linear model. Centering predictors in multilevel models. We focus on two-level linear and logistic regression models for clustered cross-sectional (individuals nested within Multilevel models (MLM), also known as hierarchical linear models (HLM) and mixed effects models are widely used across a range of disciplines, including sociology, psychology, political, education, and public health. Multilevel Models for Longitudinal Data focuses on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, to repeated measures data. Nov 16, 2022 · Multilevel/mixed models using Stata $1,395 4 days 3–4 hours daily This course introduces multilevel/mixed modeling for nested and longitudinal data and its implementation in Stata. A comment on notation (self-study). The underlying graphs and algebra are not covered until Chap. In this module, students will be introduced to multilevel modelling, also known as hierarchical linear modelling. Selected advanced topics, including three-level models, cross-classification, dyadic data, categorical outcomes, power, and assumption violation. Intercepts as outcomes. - Interpret model parameters (including fixed effects and variance components) from a multilevel model, including cases in which covariates are continuous, categorical, or centered. Prerequisite: Statistical Science 360, 601, or 602L. Feb 16, 2022 · Level: Professtional (P) This two-day course is designed to give participants an introduction to the theory and application of multilevel regression models for clustered or hierarchical data using the statistical software (participants can choose between R, Stata and MLwiN). Learn in Mplus, SAS, and R. You will also learn how to analyse multilevel data sets with the R and R Studio programmes, to interpret the output and to report the results. About the Course Short Description After a short review of regression basics, the course discusses modelling heterogeneity and delves into interaction models and fixed versus random effects (our most basic multilevel models). Multilevel Models for Hierarchical Data focuses on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of hierarchical data. Mixed Models, Hierarchical Linear Models, and Multilevel Models: A simple explanation Quant Psych • 82K views 3 years ago Newsom: Multilevel Regression Bayesian Multilevel Modelling using brms for Ecologists (BMME01) is a 10-day online course designed for ecologists looking to apply Bayesian methods to complex hierarchical models in R. Nov 24, 2025 · In this course, PhD candidates will get an introduction into the theory of multilevel modelling, focusing on two level multilevel models with a 'continuous' response variable. Enhance your data analysis skills with this hands-on course on Multilevel and Mixed Effects Models using R. It breaks down artificial barriers between model types and allows investigators in the health and behavioral science maximal ability to pose sophisticated and complex questions. Learn HLM fast and on-demand from the best instructors. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or In this course you will get acquainted with the fundamental theories and application of multilevel models. This open access book is a practical introduction to multilevel modelling or multilevel analysis (MLA). Nov 16, 2022 · Join us as we introduce the concepts and jargon of multilevel modeling for nested and longitudinal data. , multilevel regression, longitudinal models, and more. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). Jan 6, 2026 · The course will focus on multilevel modeling as a framework that can be applied using a variety of software, rather than focusing exclusively on a single one. How to estimate a multilevel SEM model containing both observed and latent variables and any number of dependent levels. , MSEM, multilevel mixture models, combining different variable types) and (b) power analysis. E. Feb 8, 2023 · Level: Professtional (P) This two-day online course is designed to give participants an introduction to the theory and application of multilevel regression models for clustered or hierarchical data using the statistical software (participants can choose between R, Stata and MLwiN). Our LEMMA (Learning Environment for Multilevel Methodology and Applications) online multilevel modelling course, contains a set of graduated modules starting from an introduction to quantitative research progressing to multilevel modelling of continuous and binary data. Topics include multilevel S. Sweet has taught several workshops on social network models and machine learning for the social sciences and has recently taught graduate courses on multilevel models. student nested within classes, individuals nested within households or geographical areas) and longitudinal data (e. Start learning hierarchical linear models with a free preview. It is important for extending our understanding of social, biological and other sciences beyond that which can be obtained through single level modelling. The objective of this course is to get participants acquainted with multilevel models. S/U or letter grading. Topics in Part 1 include: Identifying if MLM is necessary – the first step is determining whether the data actually violates assumptions of independence. … The text is an obvious candidate for use in courses or course modules on multilevel modeling, especially in Part 2. I love psychology, and my developmental focus was on adolescence and young adulthood. Refund Policy: This 2-part multilevel modeling (MLM) tutorial is designed for newbies as well as researchers who have been exposed to it through a prior class or workshop but still have lots of questions. , will provide an intensive introduction to multilevel models (MLM) using R. In addition, participants will learn how to run basic two-level model in R. Because this is a hands-on course, learners are encouraged to bring a laptop to class with a copy of R installed, along with the following packages: lme4, nlme, and lmerTest. repeated measurements of individuals in a panel survey). The course comprises of a mixture of short lectures on the basic theory behind multilevel models, teaching via examples worked through by the trainer on real data sets which participants can follow, and exercises to practice the skills just learned. , students nested within schools, individuals nested within households or geographical areas) and longitudinal data (eg repeated measurements of individuals in a panel survey). Ignoring dependence. The focus will be on using these methods for applied research. Christian Geiser teaches multilevel modeling for beginners in this on-demand workshop. Rabe-Hesketh,Anders Skrondal,2012 Multilevel and Longitudinal Modeling Using Stata Third Edition discusses regression modeling of clustered or hierarchical data such as data We would like to show you a description here but the site won’t allow us. This is an introduction to multilevel modelling. - Understand the taxonomy of models, including why we start with an unconditional means model. Feb 29, 2020 · Secondly, multilevel analysis enables the testing of more interesting hypotheses, especially those referring specifically to variation in outcomes or concerning the interactions between characteristics of the context and of individuals. Basics of random coefficient models for analysis of data from (1) individuals nested within groups and (2) repeated observations of individuals (longitudinal growth models). Advanced Modeling Generalized linear models Logistic regression Multilevel models, or Mixed-Effects models Multilevel modeling in R: a visual explanation Regular linear model Multilevel model with random intercepts Multilevel model with random intercepts and random slopes Comparing multilevel models Multilevel Logistic or Poisson regression Hello, I have some funds to pay for statistical training through my work. Model building. Multilevel models (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, or random-effects models) are stati Modelling the Dynamics of Intensive Longitudinal Data (online course) 2026 Multilevel models are a class of regression models for data that have a hierarchical or nested structure. These models are often used for the analysis of In this course, PhD candidates will get an introduction into the theory of multilevel modelling, focusing on two level multilevel models with a 'continuous' response variable. These models are also referred to as hierarchical linear models (HLMs) or mixed models Multilevel Modelling Description In this module, students will be introduced to multilevel modelling, also known as hierarchical linear modelling. The latter includes standard modelling, alternative modelling and the analysis of RCT data. Setting up the Multilevel Model C. This course will help you begin to learn how to analyze multilevel data sets and interpret results of multilevel modeling analyses. Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality; the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. Plotting and Probing Interactions The overall set-up of the models follows Bolger & Laurenceau (2013 Feb 24, 2021 · Growth-curve models for repeated measures longitudinal data Multilevel logistic regression for binary response data Resources continuing your multilevel modelling learning after the course Applied quantitative researchers with an interest in the analysis of clustered cross-sectional and longitudinal data. Jun 3, 2024 · These methods fall under the heading of multilevel modeling, which is also sometimes referred to as mixed modeling, hierarchical linear modeling, or random coefficients modeling. Dr. A multilevel model is a model where we can have one or more cluster-specific parameters for each cluster (e. Multilevel (random-effects) extensions What are multilevel models and why should I use them? Why use multilevel modelling? (voiceover with video and slides) by Jon Rasbash What are multilevel models? Many kinds of data, including observational data collected in the human and biological sciences, have a hierarchical or clustered structure. This is a great resource, we only ask that you give us as much information as possible about yourself for our research into learning. This three-day course held in January and July every year provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R or Stata. We focus on two-level linear and logistic regression models for clustered cross-sectional (individuals nested Sophia Rabe-Hesketh,Anders Skrondal Multilevel And Longitudinal Modeling Using Stata Volume Ii Categorical Responses Counts And Survival Third Edition: Multilevel and Longitudinal Modeling Using Stata: Categorical responses, counts, and survival S. The book is designed to be able to serve as a textbook for a one or two semester course in multilevel modeling. This two-day course is designed to give participants an introduction to the theory and application of multilevel regression models for clustered or hierarchical data using statistical software (participants can choose between R, Stata, and MLwiN). Introduction to multilevel modeling theory and practice using R, focusing on two-level models with continuous response variables for analyzing hierarchical data in social sciences. Jul 25, 2025 · An introduction to the Multilevel Model for change and the latent growth model using Stata. Rabe-Hesketh,Anders Skrondal,2012 Multilevel and Longitudinal Modeling Using Stata Third Edition discusses regression modeling of clustered or This online course taught by Stephen Vaisey, Ph. Multilevel and Longitudinal Modeling Using Stata; Volume 1 : Continuous Responses, Volume 2: Categorical Responses, Counts, and Survival. My dissertation involved using a Bayesian multilevel structural equation modeling design, and I have guest lectured in college statistics courses. How to use longitudinal data to understand causal relationships or used as part of impact evaluation. Longitudinal Multilevel Modeling with SAS is backed by a complete money-back guarantee for 7 days following the start of the course. g. , including multilevel structural equation modeling (MSEM). Beyond that, where should it be used? Instructors of first-year graduate methods courses should consider complementing their texts with material from Part 1. Start learning hierarchical linear models with a free lesson. This chapter also covers the idea of what constitutes a level in multilevel research. D. Repeated measurements in longitudinal studies or patients nested within hospitals are examples of data with a hierarchical structure. Feb 29, 2020 · In this chapter, we will introduce the basic methodological background to multilevel modelling in verbal form. Hierarchical Linear Models (HLM) Multilevel Modeling Training Course is a specialized program designed for data professionals, researchers, and social scientists looking to master the complexities of analyzing nested data structures. ), and these parameters are assumed to come from some common distributions. Multilevel modelling FAQs - introductory multilevel modelling FAQs and resources Journal of Applied Statistics, 'Data Analysis Using Regression and Multilevel/Hierarchical Models is the book I wish I had in graduate school. In this session we cover … A. presents multilevel modeling methods for complex design and inference challenges in this online course. May 7, 2025 · Multilevel Intervention Training Institute (MLTI) modules provide foundations in conducting MLI research focused on cancer across the cancer control continuum. It is intended to be accessible to undergraduate students who have successfully completed a regression course. Using the Mutlilevel Model to Examine Between-Person Differences in Within-Person Associations D. We will demonstrate how to fit multilevel and longitudinal models using Stata's mixed command and how to visualize the results using Stata's predict, twoway, margins, and marginsplot commands. Not open to students who have taken Statistical Science 410L. Nov 16, 2022 · This course is an introduction to using Stata to fit multilevel/mixed models. Introduction What do multilevel models do and why should I use them? What are the data structures that multilevel models can handle? What type of model can be fitted? What size of sample is best? Do multilevel models ever give different results? (PDF, 100kB) by Kelvyn Jones It is sometimes said that the use of multilevel models over OLS regression makes no substantive difference to 4 days ago · This three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R, or Stata. Multilevel and hierarchical models for longitudinal and/or clustered data. The text is an obvious candidate for use in courses or course modules on multilevel modeling, especially in Part 2. We establish a comprehensive foundational understanding of multilevel modelling that prepares readers to recognize when such models are needed, conduct their own, and critically analyze their use in the literature. Jan 22, 2025 · Multilevel modeling is a statistical approach for analyzing nested data, accounting for variability within and between groups to model hierarchical structures. Aug 19, 2021 · Mplus is especially useful for (a) going beyond what most other MLM software can do (e. Key terms: quantitative research, multilevel modelling, hierarchical data, correlational analysis, causality, R software package, introductory course. 5. This non-independence may be of direct About the course This six-day course will explain the basic concepts of multilevel analysis, some specific application of multilevel analysis and will further focus on longitudinal data analysis. A practical introduction to multilevel modelling with applications in social research. There are two principal reasons for the increasing popularity of multilevel analysis. Our analysis focuses on the relevant cases to identify differences in knee biomechanics in recreational runners during low and high-intensity exercise sessions with the same energy expenditure by recording $20$ steps.

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