## Y639: Multilevel Modeling

### Class Description

This course will introduce students to the theory and practice of multilevel models - an increasingly common technique for dealing with clustered data. Examples for the course will come primarily from the field of education; however, the methods are presented in general and examples are easily extended to many fields. Students will learn to develop, implement, interpret, and report research involving multilevel analyses. Further, students are expected to gain proficiency in SAS as it pertains to multilevel analysis.

### Class Information

**School/Department: **Education

**Semester(s): **Spring

**Year(s) Offered: **_2018

**Class time: **
Tuesday, Thursday : 9:30AM - 10:45AM

**Website: **http://oncourse.iu.edu

**Capacity: ** 24

**Instructor: **Julie Lorah - jlorah@indiana.edu

### Other Details

**Sequence: ** No

**Prerequisites: ** 2 semesters of graduate coursework in Statistics

**Algebra Required?: ** For notation, some proofs

**Calculus Required?: ** Used for some concepts and derivations

**Day(s) per week offered: ** 2 days per week, usually Tue/Thur

**Recommended follow-up classes: ** Depends on research interests

**Substantive Orientation: ** Social Sciences, however, links to the natural/physical sciences are plentiful

**Statistical Orientation: ** Frequentist

**Books used: ** Snijders and Bosker (2012). Multilevel analysis. Similar, although more comprehensive would be Raudenbush and Bryk (2002).

**Applied/Theoretical: ** This is an applied course with some theoretical derivation and explanation

**Software Used: **
R, SPSS

**How the software is used: ** Computation, some programming and data analysis

**Problem Sets: ** Yes, with a mix of theoretical/conceptual/analytic problems and applied, data analysis

**Data Analysis: ** Yes, for the homework assisgnments and the final class project

**Presentations: ** Yes, a research paper is required for the final project. Can include methodological or applied perspectives

**Exams: ** None at present, however, that could change

**Keywords: ** Multilevel models, mixed modeling, hierarchical linear modeling, random effects models