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Quantitative Methods

Code: 41984
Credits: 10
2026/2027
Degree programme Type Course
Management, Organization and Business Economics OB 0

Contact lecturer

Name :
Alexandra Simon Villar
Email :
alexandra.simon@uab.cat

Teaching staff

Alexandra Simon Villar
Giuseppe Lamberti
Miguel Karlo de Jesús Reyes
Nour Chams

Teaching staff (external to UAB)

Alejandro Bello Pintado

Group languages

You can consult this information at the end of the document.

Prerequisites

No specific preconditions although some general knowledge in statistics are more than welcome.

Objectives

The module introduces multivariate methods for the quantitative analysis of large databases. It also includes methods for creating and improving measurement scales and for analysis of experimental and non-experimental data. The data used will be related to economic and business issues, with an special emphasis on introducing gender aspects in the analyses. The use of statistical packages is emphasized through exercises and applied works. The module also contains econometric methods including response models, discreet censored regression models, methods of sample selection and panel data models. Additionally, also addresses mathematical programming in the context of operational research. The course also gives an introduction to qualitative methods.

Learning outcomes

  1. Know and distinguish the characteristics of the different business databases.
  2. Show mastery of the analysis of experimental data and survey data.
  3. Know different statistical, econometric and mathematical programming techniques.
  4. Select the most appropriate techniques to analyse both quantitative and qualitative information.
  5. Resolve the models of probability and statistics, econometrics and mathematical programming.
  6. Further investigate the differences between different organisational situations.
  7. Identify the sources of data at international level.
  8. Identify the aspects that differentiate the theoretical models.
  9. Choose the most appropriate theoretical model for the objectives set by the business situation under study.
  10. Present research results to various audiences using the different media available.
  11. Master the technical and IT tools needed to carry out applied studies.
  12. Identify the relevant sources of information and their content for subsequent analysis.
  13. Develop an ethical, social and environmental commitment.
  14. Work in multidisciplinary international teams.
  15. Develop a critical and a constructive attitude to one's work and that of others.
  16. Explain and motivate the analyses, interpret the results and present all these clearly and concisely in English.
  17. Leadership and decision-taking capability.
  18. Distinguish between the effect of variables in sex and gender in both theoretical and empirical analyses.

Contents

The module provides vital input into decision-making in business and management. In particular, the course provides an applied introduction to data analysis. The main purpose is to provide students with the basic knowledge for developing empirical analysis and understanding the results. The approach to the subject will be essentially practical, being STATA the statistical computer package used throughout the module.


The following topics will be covered:


Part 1


1. Data management, graphics and applications.


2. Descriptive statistics. Significance. Plots. Hypotheses tests.
3. Normality tests. Parametric and non-parametric tests for comparison of means.
4. Analysis of cross-classifications.
5. Measures of association.
6. Correlation.
7. Regression.
8. Logistic regression.
9. Factor analysis. Cluster analysis and property fitting.


10. Structural Equation Models.
11. Discrete choice models.
12. Censored and truncated models.
13. Panel Data.


 


Part 2


1. Basic knowledge of Social Research terminology (Ontology, Epistemology, etc.)


2. Interviews and focus groups


3. Grounded Theory for Management studies


4. Basic Training in use of a computer package to assist with qualitative data analysis (e.g. NVivo)


5. Thematic análisis


 


Further details are provided in the MMOBE web page.

Learning activities and methodology

Title Hours ECTS Learning outcomes
Type: Autonomous
Reading related cases and practical preparation, study and preparation of schemes 95 3.8 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
Type: Guided
Lectures, discussions and case presentations 100 4 1, 2, 3, 4, 5, 6, 7, 8, 9
Type: Supervised
Training and monitoring of work in progress and cases 15 0.6 1, 2, 3, 4, 5, 6, 7, 8, 9

The module presents a practical approach, therefore sessions are scheduled in computer rooms and developed through the use of statistical packages (STATA mainly).

Generally, professors present different techniques (objectives and requirements related to the type of variables), they use the statistical packages and teach how they can be used in relation to the techniques previously commented, and finally they develop some exercises.

Other exercises and cases are assigned to the students.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Assignments 40% 30 1.2 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
Class participation 5% 0 0 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
Test 55% 10 0.4 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 16

The system followed in the module considers 3 elements to assess the performance of the students:


1. Class participation.

2. Assignments.

3. Test.

“A student who does not take any assessable evaluation is considered ‘non-assessable’; therefore, a student who takes any continuous assessment component can no longer be classified as ‘non-assessable’.”

This subject/module does not offer the option for comprehensive evaluation.

AI use:

Model 2 - Restricted Use: “For this subject, the use of Artificial Intelligence (AI) technologies is permitted exclusively in support tasks, such as bibliographic or information searches, text correction or translations.

The student must clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how these have influenced the process and the final result of the activity.

The lack of transparency in the use of AI in this assessable activity will be considered a lack of academic honesty and may lead to a partial or total penalty in the grade of the activity, or greater sanctions in serious cases.”


The completion of assessment activities is subject to the provisions set out in this course guide and in the "Policy of the School of Economics and Business on the Detection of Irregularities during Assessment Activities", which regulates the conditions under which assessment tasks are conducted and the procedures applicable in cases where indications of irregularities are detected. Students are encouraged to consult the policy.


Bibliography

Afifi, A., May, S., and Clark, V.A. (2011) Practical Multivariate Analysis, 5th ed., Chapman & Hall/CRC.

Amemiya, T. (1981) Qualitative Response Models: A Survey, Journal of Economic Literature, 19: 483–536.

Cameron, A.C., and Trivedi, P.K (2009) Microeconomics using Stata, STATA Press.

GIOIA, D. A., CORLEY, K.G., HAMILTON, A.L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational research methods, vol. 16, no 1, p. 15-31.

Greene, W. (2003) Econometric Analysis. Fifth edition. Upper Saddler River. New Jersey, USA: Prentice – Hall.

GRIX, J. (2002). Introducing students to the generic terminology of social research. Politics, vol. 22, no 3, p. 175-186.Hair, J., Black, B., Babin, B., Anderson, R., Tatham, R. (2005) Multivariate data analysis. Sixth edition. Upper Saddler River. New Jersey, USA: Prentice – Hall.

Hair, J., Black, B., Babin, B., Anderson, R., Tatham, R. (2010) Multivariate data analysis. Sixth edition. Upper Saddler River. New Jersey, USA: Prentice – Hall.Maddala, G. (1983) Limited Dependent and Qualitative Variables in Econometrics. Econometric Society Monographs No 3, Cambridge University Press, Cambridge, chapters 2 and 3.

Software

STATA, NVivo, R

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TEm) Theory (master) 30 English second semester morning-mixed
(PLABm) Practical laboratories (master) 30 English second semester morning-mixed