Syllabus 2008

206. Quantitative Method for Multidimensional Management and Group Decision-Making Spring 2008

Instructor: Moisés Balassiano [email protected] Room 519 2559-5731 Office hours: Monday 4 - 6 PM Lectures: Monday 6:30-9:30 PM (no brake) Required Text LATTIN, JAMES, J. DOUGLAS CARROL & PAUL GREEN. Analyzing Multivariate Data. Thomson, 2003.

Teaching Assistant: Marcela Cohen [email protected] Office hours: TBA

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1. Description

Students in this course improve their capability to deal with complex datasets, choosing the appropriate technique to obtain, understand, interpret, integrate, analyze and conclude upon substantive theoretical grounds. Methods based on both unidimensional and multidimensional data will be developed.

2. Objectives

At the end of this course, students will be able to: • Understand the nature of different datasets and the respective statistical treatments; • Understand and criticize the methodological approaches of articles written in scientific journals; • Apply the appropriate method to the available dataset; and • Master the use of statistical package to solve data analyzes problems.

3. Methodology

The course will be taught in thirteen weekly meetings. The meetings will be either in classroom or lab, depending on the nature of subject to be taught. As a parallel activity, students will be encouraged to use a statistical package in order to solve the exercises. Both SPSS and SAS will be available to students in the 3rd and 4th floor labs.

4. Bibliography (Suggested)

ANDERSON, T.W. (1984). An Introduction to Multivariate Statistical Analysis. New York. John Wiley. HAIR, J.F. et al (1998). Multivariate Data Analysis. Upper Saddle River, NJ. Prentice Hall. JOHNSON, R.A & WICHERN, D.W. (2002). Applied Multiariate Statistical Analysis. New Jersey. Prentice Hall. Fifth Edition. LATTIN, J.M, Carroll, J.D, Green, P.E (2003). Analyzing Multivariate Data. Pacific Grove, CA, USA. Thomson Learning. Inc. KHATTREE,R. Naik, D.N. (2000). Multivariate Data Reduction and Discrimination with SAS Software. John Wiley and Sons, Inc. MCDONALD, R.P. (1999). Test Theory: a unified treatment. New Jersey. Lawrence Erlbaum Associates, Inc. MCDONALD, R.P. (1985). Factor Analysis and Related Methods. New Jersey. Lawrence Erlbaum Associates, Inc. MORRISON, D.F. (1976). Multivariate Statistical Methods. New York. McGraw-Hill. NETER, J. et al.(1996). Applied linear statistical models. 4th Ed. McGraw Hill Comp. Inc. TACQ, J. (1997). Multivariate Analysis Techniques in Social Science Research. London. Sage Publications. TIMM, N.H.(1975). Multivariate Analysis with Applications in Education and Psychology. Monterey, California: Brooks/Cole. WEINBERG,S. (1985). Applied Linear Regression. New York. Wiley.

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5. Related Texts

During the course, students will be requested to read published papers and write comments on the applied method, the generalizability and the validity of the results, adding possible suggestions on alternative means for improvements. Some readings may be used in posterior courses.

6. Evaluation

Students will be graded according to their performance in the midterm and final exams, both with the same weight. Final grade will be converted according to the following transformation: Performance (%) 85 a 100 70 a 84 60 a 69 less than 60

Grade A B C D

Schedule (tentative topics and readings) SECTION 1 2 3 4 5 6 7 8 9 10 11

DAY 08/09 09/09 29/09 30/09 06/10 13/10 20/10 27/10 03/11 10/11 17/11

TOPIC Simple Regression Analysis Simple Regression Analysis Multiple Regression Analysis Experimental Design and One Way ANOVA Two Way ANOVA. ANCOVA Midterm Exam Principal Component Analysis Principal Component Analysis Exploratory Factor Analysis Exploratory Factor Analysis Confirmatory Factor Analysis. LISREL

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24/11 01/12

Quantitative Analysis of Qualitative Data Final Exam

READING 3.1 – 3.5 3.1 – 3.5 HO 11.1 – 11.3 HO 4.1 – 4.5 + HO 5.1 – 5.5 + HO 6.1 – 6.5 + HO 10.1 - 10.4 + HO HANDOUT

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