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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.

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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.

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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops a strong foundation in mixed-effects models with R from first principles
Taught by authors who have a strong history of conducting research and developing practical applications for mixed-effects models
Uses mixed-effects models for a variety of applications
Provides hands-on experience with R, which is becoming an industry standard in statistical analysis
Covers the statistical concepts behind mixed-effects models, which is essential for understanding the models and their applications
Assumes no prior experience with R, making it accessible to beginners

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Linear Mixed-Effects Models with R with these activities:
Compile a Resource List on Linear Mixed-Effects Models
Encourages students to actively gather and organize resources, deepening their understanding of the topic.
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  • Search for relevant books, articles, websites, and online forums on linear mixed-effects models.
  • Create a curated list of these resources, categorizing them by topic or type.
Review Statistical Concepts
Ensures that learners have a solid foundation in statistical concepts, which are essential for understanding linear mixed-effects models.
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  • Review fundamental statistical concepts such as probability, hypothesis testing, and regression analysis.
  • Brush up on concepts of variance and covariance.
  • Read articles, books, or research papers to refresh your understanding.
Follow Online Tutorials on R Packages for Linear Mixed-Effects Models
Provides additional support and guidance for students in using R packages for fitting linear mixed-effects models.
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  • Identify online tutorials on relevant R packages, such as `lme4` or `nlme`.
  • Follow the tutorials step-by-step, experimenting with the code.
  • Replicate the examples shown in the tutorials using your own data.
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Solve Practice Problems
Reinforces the understanding of linear mixed-effects models by providing additional practice in solving problems.
Show steps
  • Solve practice problems from textbooks, online resources, or the course materials.
  • Discuss solutions with peers or instructors for feedback.
Practice Fitting Linear Mixed-Effects Models
Provides hands-on experience in fitting linear mixed-effects models, reinforcing the concepts learned in the course.
Browse courses on R Programming
Show steps
  • Choose a research question and gather relevant data.
  • Import the data into R and explore it.
  • Fit a linear mixed-effects model using the `lme4` package.
  • Interpret the model results and draw conclusions.
Read 'Linear Mixed Models' by Singer and Willett
Provides a comprehensive understanding of linear mixed-effects models, complementing the concepts covered in the course.
Show steps
  • Read through the book, making notes and highlighting key concepts.
  • Complete the practice exercises and discussion questions.
  • Summarize the main takeaways and how they relate to the course material.
Write a Report on Linear Mixed-Effects Model Analysis
Develops students' ability to communicate their findings and insights gained from analyzing linear mixed-effects models.
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  • Summarize the research question and data used.
  • Describe the model fitted and its results.
  • Interpret the findings and discuss their implications.
Attend a Workshop on Advanced Linear Mixed-Effects Modeling
Exposes students to advanced topics and techniques in linear mixed-effects modeling, extending their knowledge beyond the course.
Browse courses on Advanced Techniques
Show steps
  • Identify and register for relevant workshops conducted by experts in the field.
  • Attend the workshop and actively participate in discussions and exercises.
  • Apply the learned concepts to your own research or projects.

Career center

Learners who complete Linear Mixed-Effects Models with R will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Linear Mixed-Effects Models with R.
Provides a comprehensive overview of longitudinal data analysis. It covers a wide range of topics, including linear mixed models, generalized linear mixed models, and nonlinear mixed models.
Provides a unified approach to generalized linear mixed models. It covers a wide range of topics, including model formulation, estimation, and inference.
Provides a practical guide to applied longitudinal analysis. It covers a wide range of topics, including data management, model building, and interpretation.
Provides an introduction to statistical learning with applications in R. It covers a wide range of topics, including linear models, regression, and classification.
Provides an introduction to Bayesian statistics with examples in R and Stan. It covers a wide range of topics, including Bayesian inference, model building, and prediction.
Provides a tutorial on Bayesian data analysis with R, JAGS, and Stan. It covers a wide range of topics, including Bayesian inference, model building, and prediction.
Provides a gentle introduction to regression analysis. It covers a wide range of topics, including linear models, regression, and classification.
Provides a first course in R programming and statistics. It covers a wide range of topics, including data manipulation, visualization, and statistical modeling.
Provides a comprehensive overview of the theory of statistics. It covers a wide range of topics, including probability, inference, and statistical modeling.
Provides a comprehensive overview of statistical inference. It covers a wide range of topics, including probability, estimation, and hypothesis testing.
Provides a comprehensive overview of probability and statistical inference. It covers a wide range of topics, including probability, inference, and statistical modeling.
Provides a comprehensive overview of statistics for the life sciences. It covers a wide range of topics, including probability, inference, and statistical modeling.

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