Home
Search results “Data analysis using regression and multilevel hierarchical”
Hierarchical Multiple Regression (part 1)
 
05:05
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: 109245 how2stats
How to Use SPSS-Hierarchical Multiple Regression
 
18:00
Predicting a quantittive outcome from 2+ predictior variables while controlling for potential confounding-covariate variables.
Hierarchical Multiple Regression in SPSS with Assumption Testing
 
11:32
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: 13681 Todd Grande
Multilevel binary logistic regression example in SPSS
 
32:18
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: 5080 Mike Crowson
Multiple Linear Regression in SPSS with Assumption Testing
 
14:54
This video demonstrates how to conduct and interpret a multiple linear regression in SPSS including testing for assumptions. The assumptions tested include: normally distributed dependent variable, multicollinearity, outliers, linear relationship between IV’s and DV, and sample size.
Views: 61656 Todd Grande
Two-level multilevel model using SPSS (chapter 3 v1)
 
26:00
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: 5922 Mike Crowson
4.1: Logistic Regression and Multilevel Models - Introduction to R Workshop
 
05:19
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: 3928 Jeromy Anglim
What is multilevel structural equation modelling? by Nick Shryane
 
42:09
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: 41264 methodsMcr
R - Hierarchical Multiple Linear Regression Example
 
30:19
Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2016 This video covers how to run and interpret hierarchical multiple linear regression with continuous variables. We walk through data screening, outliers, assumptions, and running the linear model. Note: This video was recorded live during class - 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 and assignment available at statstools.com. http://statstools.com/learn/advanced-statistics/
Views: 3745 Statistics of DOOM
Hierarchical multiple regression using STATA
 
04:38
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: 1295 Mike Crowson
Introduction to multilevel linear models in Stata®, part 1: The -xtmixed- command
 
10:19
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: 88135 StataCorp LLC
Multilevel modeling using STATA (updated 2/9/18)
 
33:20
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: 4505 Mike Crowson
Hierarchical Linear Models I: Introduction
 
42:38
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.
Understand Your Data: Workshop 3, Session 1 - Multilevel Analysis
 
16:10
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: 1396 DardenMBA
Interpreting Output for Multiple Regression in SPSS
 
08:41
This video demonstrates how to interpret multiple regression output in SPSS. This example includes two predictor variables and one outcome variable. Unstandardized and standardized coefficients are reviewed.
Views: 96534 Todd Grande
Introduction to Multi-Level Modeling
 
04:38
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 multiple regression in spss
 
14:34
This video provides a discussion of hierarchical multiple regression using SPSS. 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: 8396 Mike Crowson
Hierarchical Multiple Regression (part 3)
 
05:04
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: 50027 how2stats
Introduction to Bayesian Data Analysis and Stan with Andrew Gelman
 
01:19:49
Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this talk, we will demonstrate the use of Stan for some small problems in sports ranking, nonlinear regression, mixture modeling, and decision analysis, to illustrate the general idea that Bayesian data analysis involves model building, model fitting, and model checking. One of our major motivations in building Stan is to efficiently fit complex models to data, and Stan has indeed been used for this purpose in social, biological, and physical sciences, engineering, and business. The purpose of the present webinar is to demonstrate using simple examples how one can directly specify and fit models in Stan and make logical decisions under uncertainty.
Views: 17726 Generable
R - Multilevel Models Lecture (Updated)
 
56:09
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: 5574 Statistics of DOOM
SPSS - Hierarchical Multiple Linear Regression
 
35:37
Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2015 This video covers hierarchical linear regression in SPSS, along with data screening procedures from Tabachnick and Fidell (2014). Lecture materials and assignment available at statstools.com. http://statstools.com/learn/advanced-statistics/
Views: 9377 Statistics of DOOM
R -  Multilevel Model Example
 
47:57
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: 25032 Statistics of DOOM
An Introduction to Multilevel Modeling - basic terms and research examples - John Nezlek
 
01:44:43
An Introduction to Multilevel Modeling - basic terms and research examples John B. Nezlek, College of William & Mary Warsaw, 15.10.2014
R - Hierarchical Multiple Regression
 
26:24
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: 684 Statistics of DOOM
Illustration of HLM program (by SSI) with multilevel data
 
24:07
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: 792 Mike Crowson
Modern repeated measures analysis using mixed models in SPSS (2)
 
16:09
This uses a Repeated measures analyse as an introduction to the Mixed models (random effects) option in SPSS. Demonstrates different Covariance matrix types & how to use the Likelihood ratio test to evaluate different models. First an inappropriate standard regression model is developed then one with a random intercept ( considering the patient a level 2 variable) and finally a random intercept+ slope model, each is evaluated using the likelihood ratio test (see previous video for more details on obtaining chi square p value). The example is from Twisks excellent book - applied multilevel analysis p.91-95 with him using the free package MLWin Robin Beaumont for Full notes, MCQ's etc see: http://www.robin-beaumont.co.uk/virtualclassroom/stats/course2.html
Views: 88523 Robin Beaumont
Forward, backward, and hierarchical binary logistic regression in SPSS
 
26:03
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: 668 Mike Crowson
02 Andrew Gelman
 
49:20
Views: 2104 Harvard CMSA
Multilevel Mixed-Effects Modeling Using MATLAB
 
34:50
See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r Learn how to fit wide variety of Linear Mixed-Effect (LME) models to make statistical inferences about your data and generate accurate predictions in this new webinar. Mixed-effect models are commonly used in econometrics (Panel Data), biostatistics and sociology (Longitudinal Data) where data is collected and summarized in groups. In these cases LME models with nested or crossed factors can fully incorporate group level contextual effects which cannot be accurately modeled by simple linear regression. Topics covered in this webinar include: Groups, hierarchy and advantages of LME models Preparing and organizing your data to fit LME models Specifying LME models using formula notation and design matrices Estimating model parameters using maximum likelihood (ML) and restricted maximum likelihood (REML) Generating confidence intervals on fixed effects, random effects, and covariance parameters Performing residual diagnostics and model comparison tests using theoretical or simulated likelihood ratio tests Making predictions on new data using the fitted LME model About the Presenter: Shashank Prasanna is Product Marketing Manager at the MathWorks focused on MATLAB and add-on products for Statistics, Machine Learning and Data Analytics. His prior experience includes technical support at the MathWorks and software development at Oracle. Shashank holds an M.S. in electrical engineering from Arizona State University.
Views: 2617 MATLAB
Bayesian hierarchical models
 
11:57
Basic introduction to Bayesian hierarchical models using a binomial model for basketball free-throw data as an example.
Views: 21674 Jarad Niemi
Modern repeated measures analysis using mixed models in SPSS (1)
 
17:36
Repeated measures analyse an introduction to the Mixed models (random effects) option in SPSS. Demonstrates different Covariance matrix types & how to use the Likelihood ratio test to evaluate different models. Robin Beaumont Full notes, MCQ's etc at: www.robin-beaumont.co.uk/virtualclassroom/stats/course2.html
Views: 162742 Robin Beaumont
Introduction to multilevel linear models in Stata®, part 2: Longitudinal data
 
09:12
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: 42843 StataCorp LLC
Multilevel models for survey data in Stata
 
01:15
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: 8803 StataCorp LLC
Growth Curve Episode 3: A Multilevel Modeling Framework
 
22:46
In an earlier episode of Office Hours, Patrick addressed the question, “What are growth curve models”. In this episode he explores how a growth curve model can be estimated within the multilevel linear modeling (MLM) framework.... Patrick begins by reviewing the assumption of independence in the general linear model and how this is violated when data are nested (e.g., children nested within classrooms). He then describes how the MLM allows for the direct modeling of nested behavior, and how this framework can be extended to estimate growth models in which repeated measures are nested within individual. He examines the unconditional growth model, the incorporation of time-invariant and time-varying covariates, and expanding the MLM growth model to include additional levels of nesting.
What is Multilevel Modelling? by Mark Tranmer
 
34:58
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: 31133 methodsMcr
Poisson hierarchical models
 
48:30
An in-class lecture covering Poisson hierarchical models and its application to a US cancer data set.
Views: 2761 Jarad Niemi
HLM III: Cross-Sectional and Longitudinal Applications
 
55:55
This is the third 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 walks step-by-step through one cross-sectional and one longitudinal example. The emphasis is on interpreting both the fixed effects and the variance components returned by HLM software. 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.
What Sample Size Do you Need for Multiple Regression?
 
05:03
I address the issue of what sample size you need to conduct a multiple regression analysis.
Views: 12914 how2stats
Multilevel binary logistic regression in SPSS video 1 unconditional model
 
17:46
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: 811 Mike Crowson
Jonathan Sedar - Hierarchical Bayesian Modelling with PyMC3 and PySTAN
 
40:02
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: 5545 PyData
Intro to Mixed Effect Models
 
15:41
Mixed effect models include fixed (e.g., planned treatments) and random effects (e.g., time, space). Very helpful but can kinda tricky to grasp at first.
Random Intercept Multi-Level Models
 
07:37
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
Tour of multilevel generalized SEM in Stata®
 
06:45
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: 17653 StataCorp LLC
Multilevel binary logistic regression in SPSS video 2 adding fixed level 1 predictors
 
16:46
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: 373 Mike Crowson
What is MULTILEVEL MODEL? What does MULTILEVEL MODEL mean? MULTILEVEL MODEL meaning & explanation
 
02:31
What is MULTILEVEL MODEL? What does MULTILEVEL MODEL mean? MULTILEVEL MODEL meaning - MULTILEVEL MODEL definition - MULTILEVEL MODEL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Multilevel models (also hierarchical linear models, nested models, mixed models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parameters that vary at more than one level. An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the students are grouped. These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became much more popular after sufficient computing power and software became available. Multilevel models are particularly appropriate for research designs where data for participants are organized at more than one level (i.e., nested data). The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level). While the lowest level of data in multilevel models is usually an individual, repeated measurements of individuals may also be examined. As such, multilevel models provide an alternative type of analysis for univariate or multivariate analysis of repeated measures. Individual differences in growth curves may be examined (see growth model). Furthermore, multilevel models can be used as an alternative to ANCOVA, where scores on the dependent variable are adjusted for covariates (i.e., individual differences) before testing treatment differences. Multilevel models are able to analyze these experiments without the assumptions of homogeneity-of-regression slopes that is required by ANCOVA. Multilevel models can be used on data with many levels, although 2-level models are the most common and the rest of this article deals only with these. The dependent variable must be examined at the lowest level of analysis.
Views: 2620 The Audiopedia