Advanced Quantitative Methods in Knowledge Society Research Code:  63.502    Credits:  5
View general information   Description   The subject within the syllabus as a whole   Learning objectives and results   Content   View the UOC learning resources used in the subject   Additional information on bibliography and information sources   Methodology   Guidelines on assessment at the UOC   View assessment model   Continuous assessment  
This is the course plan for the first semester of the academic year 2024/2025. To check whether the course is being run this semester, go to the Virtual Campus section More UOC / The University / Programmes of study section on Campus. Once teaching starts, you'll be able to find it in the classroom. The course plan may be subject to change.
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 dependency or interdependence between variables. I hope it's useful in your research activity.

 

Although it is a workshop, where we will apply each technique to specific 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.

Amunt

This course complements the knowledge developed in previous quantitative courses of the Master's Degree in Information and Knowledge Society.

Amunt

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: Trained to determine the feasibility and reliability, 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 a statistical set that facilitates the application of statistical techniques, data analysis and drawing of conclusions.

Amunt

1. Sampling methods

1.1. Universe (population) and shows

1.2. Most commonly used sampling methods 

2. Topics in econometrics

2.1. Hypothesis for multiple linear regression model

2.2. Malpecification of the model

2.3. Sample insufficiency: Multicolinearity & Outliers

2.4. Common issues with the error term: Heteroscedascity & Autocorrelation


3. Modelling Structural Equations (SEM)

3.1 Introduction to SEM

3.2 Scale and validation construction

3.3 Analyse the results: goodness of fit

4. Neural Networks (NN)

4.1 Introduction to neural networks

4.2 NN optimization process

4.3 Artificial NN training

4.4 Goodness of network adjustment

Amunt

Material Support
Introduction to sampling methods Web
Introduction to sampling methods DAISY
Introduction to sampling methods HTML5
Sampling PDF
Structural equation systems PDF
Topics in econometrics PDF
Unit 2. Econometrics autocorrelation practice PDF
Unit 2. Econometrics functional form practice PDF
Unit 2. Econometrics multicollinearity practice PDF
Unit 3. SEM practice PDF
Unit 4. NN practice PDF

Amunt

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: Modeling Structural Equations (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"




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".

Amunt

As part of the Wikipedia for higher education (https:In.Wikipedia.org/wiki/Higher_education), developed by a group of professors from both the Universitat Oberta de Catalunya (UOC) and the Universitat Politacnica de Catalunya (UPC), through this course we will actively use Viquipedia as a learning tool. Although Viquipedia is widely used by students, at any academic level, it is difficult to find Higher Education courses that are designed taking into account the great possibilities of this free encyclopaedia.

The learning methodology of this course is based on the work that has to be carried out in each continuous evaluation activity. This continuous evaluation is a perfect strategy integrated into the learning process, conceived as a mechanism for learning and giving reciprocal feedback. This course is an applied course, and we will be especially interested in showing how each technique can be used to demonstrate different research hypotheses.

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

1. Theoretical part

  • Viquipedia: 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 students the foundation of each statistical technique. These materials will introduce the student to the basics associated with each technique.

2. Applied part

  • A research article: A research article will be made showing how statistical technique is used to demonstrate the hypothesis. The discussion of the article, through the questions indicated in each set of problems, will be the center of each evaluation activity, and will allow to learn its benefits, and also its drawbacks.
  • A statistical package and data: Since this course is oriented to the application of the proposed techniques, we have to have a statistical package to make calculations. We will use different statistical packages, depending on the Unit. All these packages will be free versions that you can download directly from the web. The data to be used in the calculations would allow the discussion of the reference article to be completed.

Amunt

The assessment process is based on the student's personal work and presupposes authenticity of authorship and originality of the exercises completed.

Lack of authenticity of authorship or originality of assessment tests, copying or plagiarism, the fraudulent attempt to obtain a better academic result, collusion to copy or concealing or abetting copying, use of unauthorized material or devices during assessment, inter alia, are offences that may lead to serious academic or other sanctions.

Firstly, you will fail the course (D/0) if you commit any of these offences when completing activities defined as assessable in the course plan, including the final tests. Offences considered to be misconduct include, among others, the use of unauthorized material or devices during the tests, such as social media or internet search engines, or the copying of text from external sources (internet, class notes, books, articles, other students' essays or tests, etc.) without including the corresponding reference.

And secondly, the UOC's academic regulations state that any misconduct during assessment, in addition to leading to the student failing the course, may also lead to disciplinary procedures and sanctions.

The UOC reserves the right to request that students identify themselves and/or provide evidence of the authorship of their work, throughout the assessment process, and by the means the UOC specifies (synchronous or asynchronous). For this purpose, the UOC may require students to use a microphone, webcam or other devices during the assessment process, and to make sure that they are working correctly.

The checking of students' knowledge to verify authorship of their work will under no circumstances constitute a second assessment.

Amunt

This subject can only be passed through a continuous assessment. The final mark on the continuous assessment will be the final mark for the subject.The subject's accreditation formula is as follows: CA

Weighting of marks

Option to pass the course: Continuous assessment

Final course mark: Continuous assessment

Amunt

There are 4 evaluation activities, one for each part of the course.

Amunt