Winter 2010, Thursdays, 4-7 pm

Professor Russell W. Rumberger

Education, Room 4211

Office:  Education 3113

Website: http://education.ucsb.edu/rumberger/ed216c/

Email:  russ@education.ucsb.edu

 

Office hours:  Tuesdays, 2-4pm

 

TA: Susan Rotermund

 

Office:  Education 2213

 

Email: srotermund@education.ucsb.edu

 

Office hours:  Thursdays, 10am-noon

 

ED 216C Hierarchical Linear Models

Course Syllabus

 

 
Overview

The purpose of this course is to provide students with an introductory background in the basic principles and applications of hierarchical linear modeling (HLM) in educational research.  The course will review both the conceptual issues and methodological issues in using hierarchical linear modeling by working step-by-step on an actual HLM study.

 

Readings

There is one required textbook, which is available at the UCSB bookstore:

 

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

 

All the journal articles listed in the syllabus can be obtained from the Web of Science database available through the UCSB library website (http://www.library.ucsb.edu/).  To access the database off campus, you must login via the off-campus login link in the upper right-hand corner of the website.

 

In addition, there is a set of readings (marked with a * on the reading list) available for purchase at the Alternative Copy Shop, 6556 Pardall Rd, Isla Vista, Phone: 968-1055.  They are open M-F 8a-7p and weekends 10a-4p.  You can order the reader online by:

  1. Logging on to www.alternativecopy.com
  2. Clicking on the 'Order Readers' link in the top right corner of our home page
  3. Entering your class username & password below:

                    Username: ucsbed216c

                    Password: rumberger27

 

Software

We will use the computer program—HLM, version 6.08—for this course.  We will use the student version, which is available free from the website for Scientific Software International (http://www.ssicentral.com/hlm/index.html).  Copies of the student version have been loaded onto all the desktop computers in the classroom.  You may also download copies to use on your own computer.  A limited number of full versions are also available in the classroom for students who wish to work on large datasets for the class project.

 

Assignments

Students will be expected to complete weekly homework assignments and to conduct a class project in which they:

1.   Identify a research problem that can be studied through hierarchical analysis,

2.   Select an existing hierarchical data set that can be used to study the problem,

3.   Construct appropriate variables to use in the analysis,

4.   Develop appropriate HLM models to test the research problem,

5.   Test the models using the HLM program, and

6.   Describe and interpret the results.

 

Before undertaking the project, students should write up and turn in for approval a project proposal that addresses the first three questions above.  The proposal is due on January 21.  The final project report is due on March 11.  The final report should be no longer than 15 double-spaced pages, with tables and figures in an appendix.  

 

Website

All the materials for the course are available on the course website:

http://education.ucsb.edu/rumberger/ed216c/

 

 

Week 1           Conceptual and statistical overview of HLM

January 7          Raudenbush & Bryk, Chapter 1 and  pp. 16-31, 36-37.

Jessor, R. (1993). Successful adolescent development among youth in high-risk settings. American Psychologist, 48, 117-126. 

*Barr, R. & Dreeben, R. (1983). How schools work. Chicago: University of Chicago Press, pp. 1-42.

*Gamoran, A. (1992). Social Factors in Education. In M. C. Alkin (Ed.), Encyclopedia of Educational Research (pp.1222-1229). New York: Macmillan.

*Rumberger, R. W. & Palardy, G. J. (2004). Multilevel models for school effectiveness research. In D. Kaplan (Ed.), Handbook of Quantitative Methodology for the Social Sciences, pp. 235-258. Thousand Oaks, CA: Sage Publications.

 

Week 2           One-way ANOVA and means-as-outcomes models

January 14        Raudenbush & Bryk, pp. 68-75, 99-117.

Rowan, B., Raudenbush, S.W., & Kang, S.J. (1991). Organizational design in high schools:  A multilevel analysis. American Journal of Education, 99, 238-266. 

 

Week 3           One-way ANCOVA models and centering

January 21        Raudenbush & Bryk, pp. 31-35, 134-149.

Gamoran, A. (1996). Student achievement in public magnet, public comprehensive, and private city high schools. Educational Evaluation and Policy Analysis, 18, 1-18. 

Wang, J. (1998). Opportunity to learn: The impacts and policy implications. Educational Evaluation and Policy Analysis, 20, 137-156. 

 

Week 4           Slopes-as-outcomes and random-coefficient models

January 28        Raudenbush & Bryk, pp. 75-85, 94-95, 117-130, 149-152.

Lee, V.E. & Bryk, A.S. (1989). A multilevel model of the social distribution of high school achievement. Sociology of Education, 62, 172-192. 

*Seltzer, M. (2004). The use of hierarchical models in analyzing data from experiments and quasi-experiments conducted in field settings. In D. Kaplan (Ed.), Handbook of Quantitative Methodology for the Social Sciences, pp. 259-280.  Thousand Oaks, CA: Sage Publications.

 

Week 5           Residual analysis

February 4       Raudenbush & Bryk, pp. 85-94, 152-159.

Pituch, K.A. (1999).  Describing school effects with residual terms: Modeling the interaction between school practice and student background. Evaluation Review, 23, 190-211..

Rumberger, R.W. & Palardy, G.J. (2005). Test scores, dropout rates, and transfer rates as alternative indicators of school performance. American Educational Research Journal, 41, 3-42. 

 

Week 6           Review of logistic regression

February 11     Rumberger, R.W. (1995). Dropping out of middle school:  A multilevel analysis of students and schools. American Educational Research Journal, 32, 583-625. 

 

Week 7           Non-Linear models

February 18     Raudenbush & Bryk, Chapter 10.

Rumberger, R.W. & Thomas, S.L. (2000). The distribution of dropout and turnover rates among urban and suburban high schools. Sociology of Education, 73, 39-67. 


February 25    No class

 

Week 8           Growth models

March 4           Raudenbush & Bryk, Chapter 6.

Seltzer, M., Choi, K., & Thum, Y.M. (2003). Examining relationships between where students start and how rapidly they progress: Using new developments in growth modeling to gain insight into the distribution of achievement within schools. Educational Evaluation and Policy Analysis, 25, 263-286. 

Raudenbush, S.W., Brennan, R.T., & Barnett, R. (1995). A multilevel hierarchical model for studying psychological change within married couples. Journal of Family Psychology, 9, 161-174.

Svartberg, M., Seltzer, M.H., Stiles, T.C., & Khoo, S.K.  (1995).  Symptom improvement and its temporal course in short-term dynamic psychotherapy. Journal of Nervous and Mental Disease, 183, 242-248.

 

Weeks 9-10    Three-level models

March 11,18    Raudenbush & Bryk, Chapter 8.

Gamoran, A., Porter, A.C., Smithson, J., & White, P.A. (1997). Upgrading high school mathematics instruction: Improving learning opportunities for low-achieving, low-income youth. Educational Evaluation and Policy Analysis, 19, 325-338. 

Lee, Valerie E., Julia B. Smith, and Robert G. Croninger. (1997). How high school organization influences the equitable distribution of learning in mathematics and science. Sociology of Education, 70, 128-150.

Palardy, G.J. & Rumberger, R.W. (2008). Teacher effectiveness in first grade: The importance of background qualifications, attitudes, and instructional practices for student learning. Educational Evaluation and Policy Analysis, 30, 111-140.