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Course Name: Linear Mixed-Effects Models with R

Subtitle: Learn how to specify, fit, interpret, evaluate and compare estimated parameters with linear mixed-effects models in R.

Instructor: Taught by Geoffrey Hubona, Ph.D., Professor of Information Systems

Category: Academics

Subcategory: Math & Science

Provided by: Udemy

Price: $20 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of July 20, 2016…

Students: 722 students enrolled

Ratings: 27 reviews

Rank: ranked #85 in Udemy Academics Courses

Brief course description

Linear Mixed-Effects Models with R is a 7-session course that teaches the requisite knowledge and skills necessary to fit, interpret and evaluate the estimated parameters of linear mixed-effects models using R software. Alternatively referred to as nested, hierarchical, longitudinal, repeated measures, or temporal and spatial pseudo-replications, linear mixed-effects models are a form of least-squares model-fitting procedures. They are typically characterized by two (or more) sources of variance, and thus have multiple correlational structures among the predictor independent variables, which affect their estimated effects, or relationships, with the predicted dependent variables. These multiple sources of variance and correlational structures must be taken into account in estimating the “fit” and parameters for linear mixed-effects models.

The structure of mixed-effects models may be additive, or non-linear, or exponential or binomial, or assume various other ‘families’ of modeling relationships with the predicted variables. However, in this “hands-on” course, coverage is restricted to linear mixed-effects models, and especially, how to: (1) choose an appropriate linear model; (2) represent that model in R; (3) estimate the model; (4) compare (if needed), interpret and report the results; and (5) validate the model and the model assumptions. Additionally, the course explains the fitting of different correlational structures to both temporal, and spatial, pseudo-replicated models to appropriately adjust for the lack of independence among the error terms. The course does address the relevant statistical concepts, but mainly focuses on implementing mixed-effects models in R with ample R scripts, ‘real’ data sets, and live demonstrations. No prior experience with R is necessary to successfully complete the course as the first entire course section consists of a “hands-on” primer for executing statistical commands and scripts using R.

(Read more about this course on the official course page.)

Geoffrey Hubona, Ph.D. bio

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master’s and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 77 lessons

Duration: 10.5 hours of video

What you get: Specify an appropriate linear mixed-effects model structure with their own data.

Target audience: Students do NOT need to be knowledgeable and/or experienced with R software to successfully complete this course.

Requirements: Students will need to install the no-cost R console and the no-cost RStudio application (instructions and provided).

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons