Quantitative Methods in Management Research
09CMR50378
Faculty:
Joan M. Batista Foguet
Credits: 3
ECTS Credits: 3
Objectives:

The objective of the course is therefore to introduce the basic concepts of statistical multivariate methods, focusing in particular on the use of software packages.

By the end of these sessions students should be able to:

Analyze data on complex SSCC phenomena and interpret the findings
Describe large data matrices, briefly and simply. Apply multivariate techniques using SPSS
Related variables using dependence and interdependence analysis techniques
Understand multivariate techniques in business-related fields, particularly marketing & HR research
Critically read literature on multivariate analysis as applied in the business world
 
Summary:


Statistics has nothing to do with a set of recipes to solve already specified problems. It is actually an instrument to help you to make decisions. My objective is that you learn THE CRAFT of statistics
The course begins by reminding you Hypothesis testing and Significance tests. Then, the following seven sessions are splited into two parts, on the first part the Simple Regression model is reviewed to introduce the Multiple Regression perspective.
The second part of the course is devoted to techniques of Multivariate Analysis as data reduction devices of the columns and rows of the original data matrix. That is, Factor Analysis, Correspondence Analysis and Cluster Analysis Techniques.
There are afternoon sessions to discuss and practice the concepts reviewed and to master the statistical software.
 
Methodology:


During the sessions participants will be provided with the material needed to follow this course. The material includes both the theoretical content of the different subjects to be discussed and practical exercises related to these subjects to be solved using SPSS.

Participants solve the exercises in the afternoon practical sessions, discussing the content and the use of the various techniques in the light of the problems arising.
 
Assessment:

Two unexpected quizes alomg the course and, the presentation of a final dissertation consisting of the completion of an exercise with SPSS covering all of the subjects discussed. Data and the specific problem to solve will be chosen by the student, according to his or her research interests.
 
Incompatibilities:

 
Syllabus:


COURSE PROGRAM. October 2008 (7 sessions with 3hours/session)
1. Dependence Analysis of the relationships between continuous variables(Sessions 1st, 2nd & 3rd)
    - Sampling Covariance and Correlation coefficient
    - Rank correlation coefficient
    - Nominal & Continuous variables
    - Simple Linear Regression. A descriptive approach
    - Least squares adjustment criterion
    - Analysis of variation and goodness of fit
    - The Simple Linear Model.
    - Process of statistical modelling. Specification. Estimation and Testing. Prediction
    - Assumptions check. Residual analysis and influent data
    - Introduction to Multiple Regresión.
    - Specification errors. Multicollinearity.
    - Bulding Regression Models. Usual algorithms.
    - Dichotomous independent variables. Interaction (Moderate) effects.
2. Introduction to Multivariate Analysis (Session 4th )
    - Interdependency techniques. Classification, objectives and types of data. Examples.
    - Basics of measurement in marketing research. Real and measured phenomena. Dimensionality. Relevant information.
    - Representing data in Rp and RN.
3. Principal Components Analysis & Factor Analysis model (Session 5th)
    - Introduction to principal components analysis
    - Different definitions. Introductory concepts
    - A geometrical approach
    - Determining principal components: criteria and process
    - Correlation matrix analysis. Loadings
    - Analysing and interpreting results. Rotation
    - Factor Analysis Model
    - Introduction to the confirmatory approach
4. Simple Correspondence Analysis and HOMALS (Session 6th)
    - Introduction. Traditional analysis of qualitative data. Chi-square test. Weaknesses
    - Objective. Type of data. Notations. Practical applications of simple correspondence analysis (SCA).
    - Geometric approach to the study of relationships in a contingency table. Profiles and interdependence relationship. Centroid
    - Benzecri distance definition. Property of distributional equivalence
    - Adjustment criterion to obtain the factor structure. Concept of inertia
    - Factor interpretation. Absolute and relative contributions. The use of supplementary individuals
    - Introduction to Multiple Analysis of Correspondences and other related techniques. HOMALS
5. Classification techniques. Cluster Analysis (Session 7th)
    - Objective
    - Prior decisions on the data matrix
    - Selecting similarity/dissimilarity measurements
    - Aggregation: criteria and process
    - Interpreting and analyzing results
    - Validating results: a feedback process
     
Bibliography:
1sr part(Sessions 1 to 3). Select on of these two:

- Chatterjee, Samprit & Hadi, Ali S. (2006). Regression Analysis by Example. Wiley (Only for Sessions 1 to 3)

- Hair, Joseph F., Money, Arthur H., Samouel, Phillip & Page, Mike (2007). Research Methods for Business. Wiley

- Wonnacott, TH.& Wonnacott, RJ. (1995). Introductory Statistics for Business and Economics. Wiley & Sons (Sessions 1 to 3 and previous basic requirements)

2nd part(Sessions 4 to 7). HAIR, F.J., ANDERSON, R.E., TATHAM, R.L. & BLACK, W.C. (2006,1997) "Multivariate Data analysis with readings" (4th Ed.) Prentice Hall.
 
Timetable:
Wednesday 07/10/09
From 15:00 h. to 18:00 h.

Monday 19/10/09
From 09:00 h. to 12:00 h.
Wednesday 21/10/09
From 15:00 h. to 18:00 h.
Monday 26/10/09
From 09:00 h. to 12:00 h.
Every Monday from 16/11/09 to 30/11/09
From 09:00 h. to 13:00 h.
Exam 14/12/09 . At 09:00 h.