Quantitative Methods
Code: 41984 ECTS Credits: 10| Degree | Type | Year | Semester |
|---|---|---|---|
| 4311312 Management, Organization and Business Economics | OB | 0 | 1 |
Contact
- Name:
- Alexandra Simon Villar
- Email:
- alexandra.simon@uab.cat
Teaching groups languages
You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.
Teachers
- Alexandra Simon Villar
External teachers
- Alejandro Bello Pintado
- Esteban Lafuente
- Mario Pansera
Prerequisites
No specific preconditions although some general knowledge in statistics are more than welcome.
Objectives and Contextualisation
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.
Competences
- Analyse and summarise large amounts of complex quantitative and qualitative information using statistical, econometric and mathematical programming techniques.
- Be able to evaluate inequalities for reasons of sex and gender to design solutions.
- Develop a critical and a constructive attitude to one's work and that of others.
- Develop an ethical, social and environmental commitment.
- Explain and motivate the analyses, interpret the results and present all these clearly and concisely in English.
- Identify the relevant sources of information and their content for subsequent analysis.
- Leadership and decision-taking capability.
- Make use of quantitative documentary sources that are significant for the economic analysis of organisations from a critical perspective.
- Master the technical and IT tools needed to carry out applied studies.
- Present research results to various audiences using the different media available.
- Recognise the problems associated with the comparability of different organisational situations in empirical international research
- Understand qualitative models of the firm and interpret their results.
- Understand the application of theoretical models to real business problems.
- Work in multidisciplinary international teams.
Learning Outcomes
- Choose the most appropriate theoretical model for the objectives set by the business situation under study.
- Develop a critical and a constructive attitude to one's work and that of others.
- Develop an ethical, social and environmental commitment.
- Distinguish between the effect of variables in sex and gender in both theoretical and empirical analyses.
- Explain and motivate the analyses, interpret the results and present all these clearly and concisely in English.
- Further investigate the differences between different organisational situations.
- Identify the aspects that differentiate the theoretical models.
- Identify the relevant sources of information and their content for subsequent analysis.
- Identify the sources of data at international level.
- Know and distinguish the characteristics of the different business databases.
- Know different statistical, econometric and mathematical programming techniques.
- Leadership and decision-taking capability.
- Master the technical and IT tools needed to carry out applied studies.
- Present research results to various audiences using the different media available.
- Resolve the models of probability and statistics, econometrics and mathematical programming.
- Select the most appropriate techniques to analyse both quantitative and qualitative information.
- Show mastery of the analysis of experimental data and survey data.
- Work in multidisciplinary international teams.
Content
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.
Methodology
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.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Lectures, discussions and case presentations | 100 | 4 | 6, 11, 10, 17, 1, 7, 9, 15, 16 |
| Type: Supervised | |||
| Training and monitoring of work in progress and cases | 15 | 0.6 | 6, 11, 10, 17, 1, 7, 9, 15, 16 |
| Type: Autonomous | |||
| Reading related cases and practical preparation, study and preparation of schemes | 95 | 3.8 | 6, 12, 18, 14, 11, 10, 3, 2, 13, 17, 1, 5, 7, 8, 9, 15, 16 |
Assessment
The system followed in the module considers 3 elements to assess the performance of the students:
1. Class participation.
2. Assignments.
3. Test.
This subject/module does not offer the option for comprehensive evaluation.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Assignments | 40% | 30 | 1.2 | 6, 12, 18, 14, 11, 10, 3, 2, 4, 13, 17, 1, 5, 7, 8, 9, 15, 16 |
| Class participation | 5% | 0 | 0 | 6, 12, 18, 14, 11, 10, 3, 2, 13, 17, 1, 5, 7, 8, 9, 15, 16 |
| Test | 55% | 10 | 0.4 | 6, 11, 10, 3, 13, 17, 1, 5, 7, 9, 15, 16 |
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