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206. Quantitative Method for MultidimensionalManagement and Group Decision-Making

Spring 2008

Instructor:Moiss Balassianomoises.balassiano@fgv.br Room 5192559-5731Office hours : Monday 4 - 6 PM

Lectures : Monday 6:30-9:30 PM (no brake)

Text: LATTIN, JAMES, J. DOUGLAS CARROL & PAUL GREEN. AnalyzingMultivariate Data. Thomson, 2003.

Teaching Assistant:Marcela Cohenebcohen@fgv.br Office hours : TBA

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11.. DDeessccrr iipptt iioonn 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 bothunidimensional and multidimensional data will be developed.

22.. OObb j j eecctt iiv v eess At the end of this course, students will be able to:

Understand the nature of different datasets and the respective statisticaltreatments;

Understand and criticize the methodological approaches of articles writtenin scientific journals;

Apply the appropriate method to the available dataset; and Master the use of statistical package to solve data analyzes problems.

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

44.. BBiibblliiooggrr aapphhyy ((SS uuggggeess tt eedd)) 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. 4 th 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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55.. RRee llaa tt eedd T T eexxtt ss During the course, students will be requested to read published papers and write

comments on the applied method, the generalizability and validity of the results,adding possible suggestions on alternative means for improvements. Some readingsmay be used in posterior courses.

66.. EEv v aa lluuaa tt iioonn Students will be graded according to their performance in a midterm and a finalexam, both with the same weight.Final grade will be converted according to the following schedule:

Performance (%) Grade85 a 100 A70 a 84 B60 a 69 C

less than 60 D

SS cchheedduullee (tentative topics and readings)

DAY TOPIC READING1 Introduction. Basic Concepts. Scales. Data

Presentation and Transformations1.1 1.2

2 Simple Regression Analysis 3.1 3.53 Multiple Regression Analysis HO4 Experimental Design and One Way ANOVA 11.1 11.35 Two Way ANOVA. ANCOVA6 Midterm Exam7 Principal Component Analysis 4.1 4.5 + HO8 Principal Component Analysis

9 Exploratory Factor Analysis 5.1 5.5 + HO10 Exploratory Factor Analysis11 Confirmatory Factor Analysis. LISREL 6.1 6.5 + HO

10.1 - 10.4 + HO12 Quantitative Analysis of Qualitative Data HANDOUT 13 Final Exam

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