Linear Statistical Models is a two course sequence that covers linear statistical models. Regressionis taught in the Winter quarter and covers, primarily, topics in multiple regression. Analysis of Variance, taught Spring quarter, covers experimental design and analysis of variance.

Purpose

The purpose of these courses is to provide solid and comprehensive training in quantitative methods. It is designed to prepare students to carry out and interpret research using a variety of quantitavie and statistical methods. It will cover key aspects of research design and statistical inference involving linear statistical models. The two quarters provide an integrated and unified approach to the application of linear statistical models in regression, analysis of variance, and experimental and quasi-experimental designs. This integrated approach will give students an understanding of how the analytic approaches are closely connected and will help them develop flexibility in applying quantitative methods correctly to a wide range of research problems. This sequence will also provide a strong foundation for further training in advanced statistical methods.

Prerequisite

Satisfactory completion of Introduction to Research Design & Statistics or a passing score on the Linear Models Screening Exam.

Textbooks

Agresti, A. & Finlay, B. (1997). Statistical methods for the social sciences (3rd edition). New Jersey: Prentice Hall. ISBN 0-13-526526-6 (Ed230B)

Kirk, Roger E. (1998) Experimental Design: Procedures for the Behavioral Sciences, Third Edition. Monterey, California: Brooks/ColePublishing. ISBN 0-534-25092-0 (Ed230C)

Recommended readings for the aggressive graduate student:

Pedhazur, E.J. (1997). Multiple regression in behavioral research, Third edition. New York: Harcourt Brace College Publishers. ISBN 0-03-072831-2

Hamilton, L.C. (1992) Regression with graphics. Belmont, CA: Wadsworth. ISBN 0-534-15900-1

Chatterjee, S., Hadi, A., & Price, B. (2000) Ression analysis by example. New York: Wiley..] ISBN 0-471-31946-5

Weisberg, S. (1985) Applied linear regression. New York: Wiley. ISBN 0-471-87957-6

Regression with Stata Webbook (Ed230B)

Chen, X., Ender, P.B., Mitchell, M. & Wells, C. (2001) Regression with Stata Webbook Los Angeles: UCLA Academic Technology Services.

Student Evaluations

In each of the two quarters, Winter and Spring, there will be four assignments and at least two quizes. The assignments will consist of a mixture of data analysis projects, theoretical interpretations, and critiques of data analyses and published articles from the current research literature. Data analysis projects may be done using any of the major statistical packages, such as, Stata, SAS or SPSS. Students may work cooperatively in small groups (maximum of 3) on portfolio projects. The names of all the participants on a given project should appear on the project report.

Take home quizzes will be handed out several times each quarter so that the students and instructor will be able to monitor the students' progress to date. Students must work independently on their quizzes. There will be a final examination in the Ed230C course.

The instructors may also administer in-class tests should the instructor feel they are needed.

The final course grade for each quarter will be given based upon an evaluation of the assignments and performance on the quizzes and tests.

Internet Access and World Wide Web

Students will be required to have network access by one of the following means: 1) GSE&IS Computer Labs, 2) their own departmental computer labs with Internet access, or 3) their own personal computer at home with Ineternet access. In addition, students will need to use a World Wide Web browser. Recommended browsers include Internet Explorer or Netscape Navigator.

The course web pages will contain important course information, including assignments, datasets, examples, helpsheets, computer printouts, class discussion forums, and lecture notes. The URL is http://www.gseis.ucla.edu/courses/ed230bc1/230bc.html.

Proposed Course Schedule
Ed230B
Week 1-3

Overview and rationale
Correlation Analysis
Simple Linear Regression (SLR)
Basic model: Assumptions, SLR Residuals, Nonlinearity, Transformations
Use of Stata

Multiple Regression
Multiple correlation, partial correlation, variable selection,
partitioning of variation, multicollinearity, cross validation,
shrinkage, interactions, weighted least squares

Qualitative regressors: dummy coding, effects coding

Analysis of covariance, aptitude treatment interaction

Logistic regression
Path Analysis/causal models/structural equation modeling

Ed230C
Week 1

Between group designs
One-way anova/ completely randomized designs

Multiple comparisons

Factorial anova/completely randomized factorial designs

Within group designs
Randomized block design, randomized block factorial design

Split Plot Factorial design

Nested designs, hierarchical linear models

Unbalanced designs,

Ancova & experimental designs

Schedule of Readings

Week	Topic	A & F
	Ed230B
1-3	Simple Linear Regression (SLR) Overview and rationale, basic model, assumptions, SLR residuals, nonlinearity, transformations.	Ch. 9
4-6	Multiple Regression Partial regression coefficients, partitioning of variation, matrix approach, variable selection, multicollinearity cross validation, missing data.	Ch. 10, 11
7-8	Causal models and path analysis	Ch. 16
8-9	Categorical predictors
9-10	Analysis of covariance, aptitude treatment interaction	Ch. 13
	Ed230C	Kirk
1	Between-group designs Completely randomized designs	Ch. 5; 5.1-5.6, 5.9
2	Multiple comparisons	Ch. 4; 4.1-4.5, 4.7-4.8
3	Completely randomized factorial designs	Ch. 9; 9.1-9.6, 9.8, 9.10-9.11, 9.15 Ch. 10; 10.1-10.3
4	Within group designs Randomized block designs	Ch. 7; 7.1-7.5, 7.7, 7.10
5	Randomized block factorial designs	Ch. 10; 10.5-10.7
6	Split Plot Factorial designs	Ch. 12; 21.1-12.12, 12.15
7	Nested designs, hierarchical designs	Ch. 11; 11.1-11.3, 11.6, 11.9
8	Unbalanced designs	Ch. 9; 9.14
9	Analysis of covariance	Ch. 15; 15.1-15.6, 15.9, 15.12
10	Review for final

Use of Teaching Assistants

Teaching Assistant (TAs) are assigned by the Department of Education to the Social Research Methods Division to assist students in our courses. This assistance covers explanations of concepts and procedures, help with assignments and computer runs, and review for examinations. TAs are assigned to particular courses and their office hours are listed on the TA office door. These are the times they are available to help you. In addition, you may use any other "on duty" TA to receive assistance when your class TA is "off duty" or tied up with other students. TAs, however, are not available to provide assistance when they are "off duty". At that time, they are just like you, working on courses and research projects. Out of courtesy, please do not ask for a "minute" of their time; go the TA office to discuss your work with an on duty TA. If you do ask for help from and "off duty" TA, he or she has been instructed to refer you to the TA office.

Linear Statistical Models Course