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Understand Your Data: Workshop 3, Session 1 - Multilevel Analysis
 
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Cristiano Guarana introduces multilevel analysis and explains what multilevel models, Rwg, ICC1, and ICC2 are. DATA SET DEPARTMENT: http://dm.darden.virginia.edu/ResearchMethods/DataSet-Department.zip DATA SET EMPLOYEE: http://dm.darden.virginia.edu/ResearchMethods/DataSet-Employee.zip BLIESE 2000: http://dm.darden.virginia.edu/ResearchMethods/Bliese2000.pdf CHAN 1998: http://dm.darden.virginia.edu/ResearchMethods/Chan1998.pdf MORGESON & HOFMANN 1999: http://dm.darden.virginia.edu/ResearchMethods/MorgesonAndHofmann1999.pdf RWG AND ICC CALCULATION: http://dm.darden.virginia.edu/ResearchMethods/RwgAndIccCalculation.xls The BRAD Lab is an interdisciplinary laboratory supporting behavioral research at Darden School of Business. Our goal is to strengthen Darden’s research community to leverage knowledge creation and dissemination. We study organizational behavior, marketing, business ethics, judgment and decision-making, behavioral operations, and entrepreneurship, among other areas. MORE: http://www.darden.virginia.edu/brad-lab/
Views: 2388 DardenMBA
Mixed Models, Hierarchical Linear Models, and Multilevel Models: A simple explanation
 
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What happens when you have nested data? Find out, yo.
Views: 1372 Quant Psych
Multilevel binary logistic regression example in SPSS
 
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This video is intended to be a broad demonstration of some of the SPSS functions available for carrying out multilevel binary logistic regression using Generalized Mixed Models in SPSS. I provide a review of single level binary logistic regression, and then demonstrate how to carry out the analyses taking into account the multilevel nature of the data. You can obtain a copy of the data and follow along with the presentation by going to the following web address: https://drive.google.com/open?id=1irHe8S9kdUIGP0d0HBK5hG6e1Fh6FXXK For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 13389 Mike Crowson
4.1: Logistic Regression and Multilevel Models - Introduction to R Workshop
 
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Materials: https://github.com/jeromyanglim/introduction-to-r-one-day-workshop Playlist for full course: https://www.youtube.com/playlist?list=PLegh-m6sYwadxWLUwI-5Lnlmv8ZpK0Xur
Views: 5889 Jeromy Anglim
Introduction to Multi-Level Modeling
 
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Many areas of research are looking into questions where the data is nested in layers. In these cases, standard regressions don't do an adequate job finding accurate correlations. Multi-Level Models allow you to use the nested nature of the data to your advantage, and this video gives you a brief introduction to using them. See this video in context and much more on social science research methods and concepts at the Mod-U site: https://modu.ssri.duke.edu
Hierarchical Linear Models I: Introduction
 
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This is the first in a series of lectures covering hierarchical linear models, also known as multilevel models, mixed models, random effects models, and variance components models. The material in this video outlines the motivation for using specialized methods for clustered data, and it describes random effects from the perspective of regression, ANOVA, and latent variable models. Subsequent lectures in the series are meant to build cumulatively in a manner that mimics classroom learning and provide you with a comprehensive understanding of how multilevel models apply to your own research. Complement your learning by setting up a session with one of our statistical consultants. Just contact us at 734-544-8038, by email at [email protected], or visit our website, http://methodsconsultants.com.
Hierarchical Multiple Regression (part 1)
 
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I demonstrate how to perform and interpret a hierarchical multiple regression in SPSS. I pay particular attention to the different blocks associated with a hierarchical multiple regression, as well as R squared change and F change.
Views: 124900 how2stats
Forward, backward, and hierarchical binary logistic regression in SPSS
 
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This video provides a demonstration of several variable selection procedures in the context of binary logistic regression. I begin by discussing the concept of nested models and then move to a presentation on how to carry out and interpret models where variables are entered using either an empirical approach (i.e., forward and backward) or a hierarchical approach (i.e., based on the researcher's conceptual frame). A copy of the data can be downloaded here: https://drive.google.com/open?id=1p1H92YaBWGtHyBovKSb4YnNNZpYl8Pps For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 3307 Mike Crowson
Introduction to multilevel linear models in Stata®, part 1: The -xtmixed- command
 
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Discover the basics of using the -xtmixed- command to model multilevel/hierarchical data using Stata. If you'd like to see more, please visit the Stata Blog: http://blog.stata.com/2013/02/04/multilevel-linear-models-in-stata-part-1-components-of-variance Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 99072 StataCorp LLC
R - Multilevel Models Lecture (Updated)
 
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Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2017 This video is a rerecording of a multilevel model lecture I gave a while back - covers the ideas behind MLM and how to run a model in R using nlme. The example is new! Lecture materials and assignment available at statstools.com. http://statstools.com/learn/advanced-statistics/
Views: 10449 Statistics of DOOM
Multilevel binary logistic regression in SPSS video 2 adding fixed level 1 predictors
 
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This example reviews how to carry out and interpret a multilevel binary logistic regression that incorporates fixed Level 1 predictors using SPSS. The example comes from Chapter 4 of Heck et al.'s (2012) book: https://www.routledge.com/Multilevel-Modeling-of-Categorical-Outcomes-Using-IBM-SPSS/Heck-Thomas-Tabata/p/book/9781848729568 A copy of the data can be downloaded here: https://drive.google.com/open?id=1FNYoyHLD5IOWXcip4QG5jO3TfmkyIUrq For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 1027 Mike Crowson
Two-level multilevel model using SPSS (chapter 3 v1)
 
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This is the first of several videos illustrating how to carry out multilevel modeling involving two levels. The examples and data are associated with Heck et al. (2014) book, Multilevel and Longitudinal Modeling with IBM SPSS (2nd ed.), which an be found at the publisher's website at: https://www.routledge.com/Multilevel-... The data can also be accessed at https://drive.google.com/open?id=1-u6z-LQ4ZyoWD6uMsAuFlMYt1LUKyVJa. More info is available at: https://mikesstatsblog.blogspot.com/2018/01/example-of-hlm-analyses-in-spss-using.html..
Views: 14895 Mike Crowson
Hierarchical multiple regression using STATA
 
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This video provides a quick overview of how you can run hierarchical multiple regression in STATA. It demonstrates how to obtain the "hreg" package and how to use it to carry out your analysis. The data associated with this demonstration can be downloaded here: https://drive.google.com/open?id=1YUntvjTxmUvdhQtcLAnl3z449OYKJjFW The notes can be downloaded here: https://drive.google.com/open?id=1PP-TvbeQnWlyfO0G7uRmI_BcjhbJKNIm Check out other videos and resources at my following sites: https://sites.google.com/view/statisticsfortherealworldagent/home https://sites.google.com/view/statistics-for-the-real-world/home
Views: 3268 Mike Crowson
R -  Multilevel Model Example
 
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Recorded: Fall 2015 Lecturer: Dr. Erin M. Buchanan This video gives an example of multilevel modeling in R - covers data screening in wide format, melting to long format, nlme for analysis, and interpretation of predictors. Lecture materials and assignment available at statstools.com. http://statstools.com/learn/advanced-statistics/
Views: 29496 Statistics of DOOM
Hierarchical Multiple Regression in SPSS
 
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This brief video explains how to perform a 4 step (block) Hierarchical Multiple Regression analysis in SPSS. This should NOT be confused with Hierarchical Linear Modeling (HLM), which is a much more sophisticated statistical procedure performed across multiple levels.
Views: 211 Gerard Babo
Multilevel modeling using STATA (updated 2/9/18)
 
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This video provides an introduction to using STATA to carry out several multi-level models, where you have level 1 and level 2 predictors of a level 1 outcome variable. The video begins with a random intercept model and concludes with a model incorporating Level 1 and Level 2 predictors, along with varying intercepts and slopes. Some discussion of cross-level interaction is provided. Data for this video can be downloaded at: https://drive.google.com/open?id=1TpvKDOUrYaeYn-74zSL74bK3-QL3edQ7 The Excel calculator for computing significance tests for variance components can be downloaded here: https://drive.google.com/open?id=1LY-u4r0Ln0vzkNRhOihqUB_5mozXIMC2 You can also download the notes I go over here: https://drive.google.com/open?id=1Ods4_aG9Z1NLdWLHGKaV43RJjlzYaxoJ
Views: 13010 Mike Crowson
An Introduction to Multilevel Modeling - basic terms and research examples - John Nezlek
 
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An Introduction to Multilevel Modeling - basic terms and research examples John B. Nezlek, College of William & Mary Warsaw, 15.10.2014
Introduction to multilevel linear models in Stata®, part 2: Longitudinal data
 
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Explore the basics of using the -xtmixed- command to model longitudinal data using Stata. If you'd like to see more, please visit the Stata Blog: http://blog.stata.com/2013/02/18/multilevel-linear-models-in-stata-part-2-longitudinal-data/ Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 47270 StataCorp LLC
Multilevel modeling of cross-sectional data - Mplus Short Courses, Topic 7
 
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Topic 7. Multilevel modeling of cross-sectional data. Recorded presentation at Johns Hopkins University, March 17, 2009. Link to handouts associated with this segment: http://www.statmodel.com/download/Topic7Handout.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 1272 Mplus
Binary logistic regression using Stata
 
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This video provides a demonstration of the use of Stata to carry out binary logistic regression. It covers menu options and syntax, and reviews post-estimation options that are available to you. You can download a copy of the data file used in the video here: https://drive.google.com/open?id=13ioHeJ51937Y6I2QCnrbd8dh5UZU9y9W You can download a copy of the referenced "do file" here: https://drive.google.com/open?id=15i_QVkr2drKAxhykONVaB18ydFC_XhUV For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 15706 Mike Crowson
Tour of multilevel GLMs in Stata®
 
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Explore the new multilevel modeling features in Stata 13, including support for binary outcomes via logistic, probit, and complementary log-log models; support for ordinal outcomes via ordered logit and ordered probit models; support for count outcomes via Poisson and negative binomial models; and support for multilevel generalized linear models (multilevel GLMs). Created using Stata 13; applicable to Stata 14. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 24385 StataCorp LLC
How to Use SPSS-Hierarchical Multiple Regression
 
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Predicting a quantittive outcome from 2+ predictior variables while controlling for potential confounding-covariate variables.
Multilevel Models:  Introducing multilevel modelling | Ian Brunton-Smith
 
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Multilevel Models: Introducing multilevel modelling
Views: 298 NCRMUK
R - Hierarchical Multiple Regression
 
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Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2018 This video replaces a previous live in-class video. You will learn how to run a hierarchical multiple linear regression using R's lm() function. The video starts with power in G*Power, works through data screening, and then interpretation of the regression output. You will also learn how to compare steps and models as part of the hierarchical regression. You can view the materials and an example write up on our OSF page. List of videos for class on statstools.com: http://statstools.com/learn/advanced-statistics/ All materials archived on OSF: https://osf.io/dnuyv/
Views: 1950 Statistics of DOOM
Conducting a Multiple Regression After Dummy Coding Variables in SPSS
 
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This video demonstrates how to dummy code nominal variables in SPSS and use them in a multiple regression. The “Recode into Different Variables” function is use to code one variable with three levels into three variables with two levels each.
Views: 104593 Dr. Todd Grande
Multilevel binary logistic regression in SPSS video 1 unconditional model
 
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This example reviews how to carry out and interpret an unconditional multilevel binary logistic regression model using SPSS. The example comes from Chapter 4 of Heck et al.'s (2012) book: https://www.routledge.com/Multilevel-Modeling-of-Categorical-Outcomes-Using-IBM-SPSS/Heck-Thomas-Tabata/p/book/9781848729568 A copy of the data can be downloaded here: https://drive.google.com/open?id=1FNYoyHLD5IOWXcip4QG5jO3TfmkyIUrq For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 2405 Mike Crowson
What is Multilevel Modelling? by Mark Tranmer
 
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Multilevel modelling is a quantitative statistical method to investigate variations and relationships for variables of interest, taking into account population structure and dependencies. These population structures may be hierarchical, such as pupils in classes in schools. For more methods resources see: http://www.methods.manchester.ac.uk
Views: 34642 methodsMcr
HLM example in SPSS (video 1) using school data
 
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Illustration of steps involved in HLM with data from Goldstein et al. (1993); data originally downloaded from the University of Bristol Center for Multilevel Modeling can be downloaded at: https://drive.google.com/open?id=15XL0f_4ZNlXOHkb92g5rZhVU-Bo9rUsy The original download site was: http://www.bristol.ac.uk/cmm/learning/support/datasets/
Views: 4810 Mike Crowson
Illustration of HLM program (by SSI) with multilevel data
 
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This video is intended to provide a demonstration of how the HLM program (student version) by SSI is set up and some of its features. I run through several examples using the program to illustrate its features. The student version of the program can be downloaded at: http://www.ssicentral.com/hlm/student.html Level 1 dataset can be downloaded here: https://drive.google.com/open?id=1KGFDvfTVk7Smx3FVoJNSWxLoer61F57_ Level 2 dataset can be downloaded here: https://drive.google.com/open?id=14Z0Shht93upcplxa1PPtZtxVXp2QYhZR You can download a copy of the free Student Edition at this site: http://www.ssicentral.com/hlm/student.html For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 2771 Mike Crowson
R - Hierarchical Models Examples
 
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Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This example video covers how to perform a first order CFA, second order CFA, and bi-factor CFA. Lavaan, semPath, and the cfa functions are covered, along with interpretation of the models and some guidance on how to pick between these options. Lecture materials and assignment available at statstools.com. http://statstools.com/learn/structural-equation-modeling/ Used in the following courses: Structural Equation Modeling
Views: 2155 Statistics of DOOM
HLM Testing and probing within and cross level interactions using STATA
 
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This video provides an illustration of testing and probing interaction effects using STATA in the context of HLM. The video demonstrates cross-level and within-level interactions. The STATA datafile can be downloaded here: https://drive.google.com/open?id=1iufFGdHUNLKWC0tg-R53urfpCmhhvHRU The do-file referenced in the video can be found here: https://drive.google.com/open?id=154puX3XHCC0Dc4Szh9zEof_MfL1IP3oN The commands are also in a word file here: https://drive.google.com/open?id=1TMX_SUNWfqk8I70Fya1NK5l_ofVh_kdi Demonstration of probing interactions can be seen here: https://www.stata.com/statalist/archive/2012-01/msg00380.html
Views: 770 Mike Crowson
21. Generalized Linear Models
 
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MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about linear model, generalization, and examples of disease occurring rate, prey capture rate, Kyphosis data, etc. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 29543 MIT OpenCourseWare
Binary logistic regression using SPSS (new)
 
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This video provides a demonstration of options available through SPSS for carrying out binary logistic regression. It illustrates two available routes (through the regression module and the generalized linear models module). If you wish to download the data and follow along, you can do so by going here: https://drive.google.com/open?id=13vJ_GnjlKwCEWX7hB-CJME1Tu5jcJydQ For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 36808 Mike Crowson
Random Intercept Multi-Level Models
 
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If you want to look at a research question where the data is in nested levels, you can use the simplest version of a multilevel model, which uses a random intercept. We explain the intuition and show you how to use the xtmixed command in STATA to try it for yourself. If you want to learn more about Group Mean Centering, check out this guide: http://web.pdx.edu/~newsomj/mlrclass/ho_centering.pdf See this video in context and much more on social science research methods and concepts at the Mod-U site: https://modu.ssri.duke.edu
R - Mediation Analyses with Multilevel
 
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Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2018 This video replaces a previous live in-class video. You will learn how to do mediation analyses in regression. First, we start with power in G*Power, work through data screening, and then analyze the stages of mediation in R. Next, the Sobel test is examined with the multilevel package. You can view the materials and an example write up for mediation on our OSF page. List of videos for class on statstools.com: http://statstools.com/learn/advanced-statistics/ All materials archived on OSF: https://osf.io/dnuyv/
Views: 1716 Statistics of DOOM
Two-level multilevel model using SPSS (chapter 3 v4); testing random slopes in HLM
 
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This is the fourth of several videos illustrating how to carry out multilevel modeling involving two levels. Specifically, it addresses how to test for random variation in slopes across Level 2 units. The examples and data are associated with Heck et al. (2014) book, Multilevel and Longitudinal Modeling with IBM SPSS (2nd ed.), which an be found at the publisher's website at: https://www.routledge.com/Multilevel-... The data can also be accessed at https://drive.google.com/open?id=1-u6z-LQ4ZyoWD6uMsAuFlMYt1LUKyVJa. More info is available at: https://mikesstatsblog.blogspot.com/2018/01/example-of-hlm-analyses-in-spss-using.html..
Views: 3347 Mike Crowson
Hierarchical Multiple Regression in SPSS with Assumption Testing
 
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This video demonstrates how to conduct and interpret a hierarchical multiple regression in SPSS including testing for assumptions. A hierarchical multiple regression determines the contribution of predictor variables to an outcome variable while controlling for one or more predictor variables.
Views: 22148 Dr. Todd Grande
What is multilevel structural equation modelling? by Nick Shryane
 
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Structural equation modelling is a family of statistical models that encompasses regression-, path- and factor analysis. For more methods resources see: http://www.methods.manchester.ac.uk
Views: 43982 methodsMcr
Learn R Multilevel Models Lecture
 
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Recorded: Fall 2015 Lecturer: Dr. Erin M. Buchanan This video covers the basic introduction to multilevel models, how to do basics in R (import data, factor data, reshape data, and packages), how to run mlm in R using nlme, and some considerations for MLM analyses. Note: This video was recorded live during a demo lecture - it will have pauses, changes in voice loudness as I wander around the room, and ridiculous jokes. If anything is unclear, please leave a comment, and I will clarify. Lecture materials available at statstools.com.
Views: 12786 Statistics of DOOM
Multilevel modeling of longitudinal data - Mplus Short Courses, Topic 8
 
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Topic 8. Multilevel modeling of longitudinal data. Recorded presentation at Johns Hopkins University, March 18, 2009. NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 632 Mplus
HLM II: The General Linear Model and the Linear Mixed Model
 
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This is the second in a series of lectures covering hierarchical linear models, also known as multilevel models, mixed models, random effects models, and variance components models. The material in this video reviews the General Linear Model (GLM) that encompasses both ANOVA and regression, and it introduces the Linear Mixed Model (LMM), of which HLM is a special case. Complement your learning by setting up a session with one of our statistical consultants. Just contact us at 734-544-8038, by email at [email protected], or visit our website, http://methodsconsultants.com.
Jonathan Sedar - Hierarchical Bayesian Modelling with PyMC3 and PySTAN
 
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PyData London 2016 Can we use Bayesian inference to determine unusual car emissions test for Volkswagen? In this worked example, I'll demonstrate hierarchical linear regression using both PyMC3 and PySTAN, and compare the flexibility and modelling strengths of each framework. Overview Bayesian inference bridges the gap between white-box model introspection and black-box predictive performance. We gain the ability to fully specify a model and fit it to observed data according to our prior knowledge. Small datasets are handled well and the overall method and results are very intuitive: lending to both statistical insight and future prediction. This talk will demonstrate the use of Bayesian inference in a real-world scenario: using a set of hierarchical models to compare exhaust emissions data from a set of vehicle manufacturers. This will be interesting to people who work in the Type A side of data science, and will demonstrate usage of the tools as well as some theory. The Frameworks PyMC3 and PySTAN are two of the leading frameworks for Bayesian inference in Python: offering concise model specification, MCMC sampling, and a growing amount of built-in conveniences for model validation, verification and prediction. PyMC3 is an iteration upon the prior PyMC2, and comprises a comprehensive package of symbolic statistical modelling syntax and very efficient gradient-based samplers using the Theano library of deep-learning fame for gradient computation. Of particular interest is that it includes the Non U-Turn Sampler NUTS developed recently by Hoffman & Gelman in 2014, which is only otherwise available in STAN. PySTAN is a wrapper around STAN, a major3 open-source framework for Bayesian inference developed by Gelman, Carpenter, Hoffman and many others. STAN also has HMC and NUTS samplers, and recently, Variational Inference - which is a very efficient way to approximate the joint probability distribution. Models are specified in a custom syntax and compiled to C++. The Real-World Problem & Dataset I'm currently quite interested in road traffic and vehicle insurance, so I've dug into the UK VCA Vehicle Type Approval to find their Car Fuel and Emissions Information for August 2015. The raw dataset is available for direct download and is small but varied enough for our use here: roughly 2500 cars and 10 features inc hierarchies of car parent-manufacturer - manufacturer - model. I will investigate the car emissions data from the point-of-view of the Volkswagen Emissions Scandal which seems to have meaningfully damaged their sales. Perhaps we can find unusual results in the emissions data for Volkswagen. GitHub repo: https://github.com/jonsedar/pymc3_vs_pystan
Views: 7797 PyData
Multilevel models for survey data in Stata
 
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Stata 14 provides survey-adjusted estimates for multilevel models. In this video, we take you on a quick tour of the situations where such adjustments are needed and the dialog boxes involved. For more information about survey-adjustment and multilevel models, see http://stata.com/stata14/multilevel-models-survey-data/ Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 10631 StataCorp LLC
Bayesian Mixed Effects Models: A tutorial with rstan and glmer2stan
 
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This video provides a tutorial on Bayesian mixed effects models in R using the rstan and glmer2stan package as well as some custom functions. supporting code can be found here https://github.com/cgonza12/bmem
Views: 11817 Christian Gonzalez
Tour of multilevel generalized SEM in Stata®
 
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Tour generalized structural equation modeling in Stata 13, including support for continuous, binary, ordinal, count, and multinomial outcomes via generalized response variables; support for multilevel data; and the corresponding enhancements to the SEM builder. Created using Stata 13; applicable to Stata 14. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 19770 StataCorp LLC