• Descripció

One of the main objectives of this course is to obtain a good knowledge of some of the most relevant quantitative techniques, their advantages and disadvantages, their applicability according to the type of data and subjects of study, and their complementarity. With these techniques, we will do different activities by using different statistical packages (such as LISREL), discussing possible relationships of dependence or interdependence between variables. I hope it will be useful in your research activity.

Although it is a practical course, where we will apply each technique to particular cases, with real data, you will also have basic references in both, web format and recommended bibliography, to understand the theoretical foundations of each technique.

• L'assignatura en el conjunt del pla d'estudis

This course complements the knowledge that is developed in previous quantitative courses of the Master Programme in Information and Knowledge Society.

• Objectius i competències

S1: Good knowledge of the most relevant quantitative and qualitative techniques, their advantages and disadvantages, their applicability according to the type of data and subjects of study and their complementarity.

S2: Ability to determine the feasibility and reliability, the strengths and weaknesses of different methods and techniques.

S3: Awareness of the possibilities, opportunities and issues posed by empirical analysis of the Internet and other ICTs.

S4: Mastering of a statistical suite that facilitates the application of statistical techniques, analysis of data and drawing of conclusions.

• Continguts

1. Sampling methods

1.1. Universe (population) and sample

1.2. Most used sampling methods 

2. Topics in econometrics

2.1. Assumptions for the Multiple Linear Regression Model

2.2. Model misspecification

2.3. Sample inadequacy: Multicolinearity & Outliers

2.4. Usual problems with the error term: Heteroscedascity & Autocorrelation

3. Structural equations modeling (SEM)

3.1 Introduction to SEM

3.2 Scale and construct validation

3.3 Analyzing results: goodness of fit

4. Neural Networks (NN)

3.1 Introduction to Neural Networks

3.2 NN optimization process

3.3 Training of artificial NN

3.4 Goodness of fit of the network

• Consulta dels materials de què disposa l'assignatura

• Bibliografia i fonts d'informació

Unit 1: Sampling methods 

- Kalton, G. (1983) Introduction to survey sampling. SAGE.

- Thomson, S.K. (2002) Sampling. 2nd edition. Wiley.

- Weisberg, H.F., Krosnick, J.A, and Bowen, B.D. (1996) An introduction to survey research, polling, and data analysis. SAGE

Unit 2: Topics in econometrics

- Green, W.H. (2003) "Econometric analysis" 5th edition. Prentice-Hall

- Johnston, J.; Dinardo, J. (2001) "Econometric Methods" 4th edition. McGraw-Hill.

- Maddala, G.S. (2001) "Introduction to econometrics". 3rd edition. John Wiley & Sons Ltd.

- Wooldridge, J.M. (2009) "Introductory Econometrics: A Modern Approach". 4th edition. South- Western Cengage Learning.

Unit 3: Structural Equations Modelling (SEM) 

- Blunch, N. (2008) "Introduction to structural equation modelling. Using SPSS and AMOS". Ed. Sage publishers.

- Dunson, D. et al. (2005) "Bayesian Structural Equation Modelling"

- http://faculty.chass.ncsu.edu/garson/PA765/structur.htm

- http://www.amosdevelopment.com/

Unit 4: Neural Networks

- Berthold, M. R. (2007) "Intelligent Data Analysis", Chap. 8: Neural Networks. 2nd Edition. Ed. Springer

- Gurney, K. (2005) "An introduction to neural networks". UCL Press.

- Haykin, S. (1998) "Neural Networks: A Comprehensive Foundation". 2nd Edition. Ed. Prentice Hall.

- Heaton, J. (2012) "Introduction to the Math of Neural Networks". Ed. Heaton Research.

- Heaton, J. (2010) "A Non-Mathematical Introduction to Using Neural Networks". http://www.heatonresearch.com/content/non-mathematical-introduction-using-neural-networks

• Metodologia

In the framework of the research project "Wikipedia for higher education" (http://oer.uoc.edu/wiki4HE/), developed by a group of professors of both Universitat Oberta de Catalunya (UOC) and Universitat Politècnica de Catalunya (UPC), through this course we will activelly use Wikipedia as a learning tool. Although Wikipedia is broadly used by students, at any academic level, it is difficult to find courses in Higher Education that are designed considering the great possibilities of this free encyclopedia.

The learning methodology of this course is based on the work it has to be developed in each continuous assessment activity. This continuous assessment is a perfect strategy integrated in the learning process, conceived as a mechanism to learn and give reciprocal feedback. This course is an applied course, and we will be especially interested in showing how can be used each technique to prove different research hypothesis.

There are 4 assessment activities, one per each part of the course. To solve the questions proposed in each activity, the student will have the following learning resources:

1. Theoretical part:

• Wikipedia: we will use this free encyclopedia to introduce different theoretical concepts.
• Learning materials: basically some parts of books, or other web materials. They will be used to give to the students the foundations of each statistical technique. These materials will introduce the student to the basic concepts that are associated to each technique.

2. Applied part:

• A research article: There will be given a research article where it is shown how the statistical technique is used in order to prove the hypothesis. The discussion of the article, through the questions stated in each problem set, will be the centre of each assessment activity, and will permit to learn its benefits, and also its disadvantages.
• A statistical package and data: Since this course is oriented to the application of the proposed techniques, we will need to have a statistical package in order to do computations. We will use different statistical packages (such as LISREL), depending on the Unit. All these packages will be free versions that you colud directly download from the web. The data that will be used in the computations will permit to complete the discussion of the reference article.

• Informació sobre l'avaluació a la UOC

La Normativa acadèmica de la UOC disposa que el procés d'avaluació es fonamenta en el treball personal de l'estudiant i pressuposa l'autenticitat de l'autoria i l'originalitat dels exercicis fets.

La manca d'originalitat en l'autoria o el mal ús de les condicions en què es fa l'avaluació de l'assignatura és una infracció que pot tenir conseqüències acadèmiques greus.

L'estudiant serà qualificat amb un suspens (D/0) si es detecta manca d'originalitat en l'autoria d'alguna activitat avaluable (pràctica, prova d'avaluació contínua (PAC) o final (PAF), o la que es defineixi al pla docent), sigui perquè ha utilitzat material o dispositius no autoritzats, sigui perquè ha copiat textualment d'internet, o ha copiat d'apunts, de materials, de manuals o d'articles (sense la citació corresponent), d'altres estudiants, o per qualsevol altra conducta irregular.

La qualificació de suspens (D/0) en les qualificacions finals d'avaluació contínua pot comportar l'obligació de fer l'examen presencial per a superar l'assignatura (si hi ha examen i si superar-lo és suficient per a superar l'assignatura segons indiqui el pla docent).

Quan aquesta mala conducta es produeixi durant la realització de les proves d'avaluació finals presencials, l'estudiant pot ser expulsat de l'aula, i l'examinador farà constar tots els elements i la informació relatius al cas.

D'altra banda, aquesta conducta pot donar lloc a la incoació d'un procediment disciplinari i l'aplicació, si escau, de la sanció que correspongui.

La UOC habilitarà els mecanismes que consideri oportuns per a vetllar per la qualitat de les seves titulacions i garantir l'excel·lència i la qualitat del seu model educatiu.

• Consulta del model d'avaluació

• Avaluació continuada

There are 4 assessment activities, one per each part of the course.