[ICPSR] Learn multilevel modeling with 2-week workshop

[Scott Campbell]

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Flexible models for measuring outcomes
Registration for this workshop ends next week!

*Multilevel Modeling in the Social Sciences*
*Dates:* June 7-18, 2021
*Instructor: *Michelle Dion, McMaster University
*Registration Deadline:* June 3

Across the social sciences, we encounter multilevel, hierarchical, or
clustered data, such as students nested in classrooms or schools or voters
nested in electoral districts or states. Often, we have theories about how
characteristics at different levels of analysis are related to lower level
outcomes. For instance, student characteristics help explain student
learning outcomes, but so do the characteristics of their teachers and
classroom peers. We may even hypothesize that students with certain
characteristics will learn differently if they share certain
characteristics with their teachers or peers. To incorporate
characteristics from each level into our analysis, we use multilevel
models, which are also known as hierarchical linear models or mixed models
in some social science disciplines. If our outcomes of interest have
non-normal distributions, we may use multilevel/hierarchical/mixed
generalized linear models. Repeated observations over time, sometimes
referred to as pooled time series, panel data, or time series cross-section
data (TSCS), can also be analyzed as a case of multilevel modelling. These
models have applications in education, psychology, political science,
sociology, public policy, and public health.

In this workshop, we will examine and apply all these types of multilevel
models in social science applications. The workshop will both present the
statistical models and demonstrate how to apply the models using the R
programming language in R Studio, which is free software that includes all
the core functionalities of statistical software (e.g. command text editor,
command console, results pane, and graphics viewer). Though the primary
tool used for teaching and demonstrations during class will be R,
comparable commands or syntax for STATA will be provided when available.
Workshop participants will be provided sample datasets and command syntax
and encouraged to follow along on their laptops or lab machines during
workshop meetings. We will also set aside time to discuss how to apply
these methods to workshop participants’ projects and to demonstrate how to
visualize or graph model results.

*Bio: *Michelle Dion is Professor of Political Science and the founding
Academic Director of the Spark Centre for Innovation in Social Research at
McMaster University (Hamilton, Ontario, Canada). She has extensive
experience teaching introductory graduate-level quantitative research
methods to students from a wide range of disciplinary backgrounds and
levels of preparation. She has taught in the ICPSR Summer Program since
2015. At McMaster University, she teaches graduate quantitative methods for
political and public policy analysis and has taught similar courses
previously in Spanish at the Centro de Investigación y Docencia Económicas
(CIDE, Mexico City, Mexico) and at Georgia Institute of Technology
(Atlanta, GA). Professor Dion is also active in Visions in Methodology
(VIM, visionsinmethology.org), an organization that seeks to support women
engaged in the quantitative study of politics.

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Scott Campbell
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