PSYED 3408 Syllabus

PSYED 3408: Hierarchical Linear Modeling

School of Education

University of Pittsburgh

Spring, 2015

Instructor: Feifei Ye Location: 5520B WWPH

Time: 1:00-3:40 Monday Office: 5924 WWPH

Phone: 412-624-7233 Email:

Office Hours: 4-5pm, Tuesday

Course Overview:

The purpose of this course is to introduce hierarchical models for continuous and discrete outcome. Hierarchical models are used when the units of observation are grouped within clusters. In such clustered data observations for the same cluster cannot be assumed to be mutually independent for given covariate values as required by conventional linear and logistic regression. Longitudinal or repeated measures data can also be thought of as clustered data with measurement occasions clustered within subjects. This course will focus on understanding the hierarchical (generalized) linear models and their assumptions, as well as practical aspects of developing models to address research questions and interpreting the findings. This course emphasizes practical, hands-on development, analysis and interpretation of hierarchical linear and nonlinear models. Applications will be drawn from education, psychology, and other social sciences disciplines.

Prerequisites

PSYED 2410 (Applied Regression) or equivalent

Text

Required:

Raudenbush, S. W. Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods, 2nd edition. Newbury Park, CA: Sage.

Lecture notes and handouts: Lecture notes (copies of slides presented in class), handouts, and additional articles or monographs on relevant topics will be made available on the course web.

Recommended:

Raudenbush, Bryk, Cheong, & Congdon (2010). HLM7 (manual). Lincolnwood, IL: SSI.

Supplementary texts

Ø  Singer & Willett (2003). Applied Longitudinal Data Analysis. NY: Oxford

Ø  Snijders, T., & Bosker, R. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage.

Ø  More resources on multilevel modeling at http://www.bristol.ac.uk/cmm/learning/.

Computing

We will mainly use the computer program—HLM—for this course. Copies of the software are available on the computers in the computer lab in 5520 WWPH. Additional information about the software and HLM in general can be found at the website for Scientific Software International (http://www.ssicentral.com/hlm/hlm.htm). The company also provides a free student-version of the software (http://www.ssicentral.com/hlm/student.html). The student edition of the software can handle 8,000 level-1 units and 350 level-2 units in a two-level model, or 8,000 level-1 units, 1,700 level-2 units, and 60 level-3 units in a three-level model. No more than 5 predictors may be included at any level of the model, and no more than 25 effects may be included in the whole model. Student version will be sufficient for assignments.

Students taking this course should be comfortable preparing and analyzing data in SPSS. Through this course, we will use SPSS as well for data preparation and some data analysis. It is fine if you choose other software for assignments, but questions regarding your software will not be discussed in class.

Evaluation

Homework: There are a total of five assignments. All assignments turned in after the due date will be docked 20% of the assignment total for each day late. Extensions will be granted only in the case of personal emergency.

Midterm Exam: There will be one in-class midterm exam.

Article Critique: You will review two articles published in a journal in your field that adopted hierarchical (generalized) linear model following the reviewer guideline provided on the course web.

You will be evaluated on the basis of your performance on the homework assignments (40%), the midterm exam (30%), and two article critique papers (30%).

Letter grades will be based on actual points earned as follows:

Point / Letter / Point / Letter
Above 96 / A+ / 77-80 / C+
93-96 / A / 74-77 / C
90-93 / A- / 70-74 / C-
87-90 / B+ / 67-70 / D+
84-87 / B / 64-67 / D
80-84 / B- / 60-64 / D-
Below 60 / F

For students who choose to audit, there is no requirement and it is up to you regarding whether to complete assignments, midterm exam, and article critique.


Tentative Course Outline

Week / Date / Topic / Reading (R&B) / HW /
1 / 01/05/2015 / Course overview / Chapter 1
2 / 01/12/2015 / Random intercept models / Chapters 2 (p16-31) & 4 (p68-75) / HW1
3 / 01/19/2015 / No class (MLK)
4 / 01/26/2015 / Random slope models and beyond / Chapters 4 (p75-85) / HW1 due
5 / 02/02/2015 / Estimation
Centering / Chapter 3 (p85-94)
Chapters 2 (p31-35) & 5 (p134-149) / HW2
6 / 02/09/2015 / Model building / Chapter 4 (p149-152)
Chapters 9 (p252-263, p267-273) / HW2 due
7 / 02/16/2015 / Assumptions and diagnostics / Chapters 9 (p263-267, p273-280) / HW3
8 / 02/23/2015 / Two-level growth models (I) / Chapter 6 (p160-185) / HW3 due
9 / 03/02/2015 / In-class Midterm-Exam
10 / 03/09/2015 / No class (spring break)
11 / 03/16/2015 / Two-level growth models (II) / Chapter 6 (185-199)
12 / 03/23/2015 / Three-level models
Power analysis / Chapter 8 / HW4
13 / 03/30/2015 / Hierarchical generalized linear model (I) / Chapter 10 (p291-309) / HW4 due
Article Critique 1 due
14 / 04/06/2015 / Hierarchical generalized linear model (II) / Chapter 10 (p309-335) / HW5
15 / 04/13/2015 / Cross-classified model / Chapter 12 / HW5 due
Article Critique 2 due
16 / 04/20/2015 / No class (Individual consultation)

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