Checking date: 30/04/2024

Course: 2024/2025

Survey Research Methodology II
Master in Computational Social Science (Plan: 472 - Estudio: 375)

Coordinating teacher: TORRE FERNANDEZ, MARGARITA

Department assigned to the subject: Social Sciences Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Data Programming (19138) Statistics and Data Science I (19140) Statistics and Data Science II (19141) Survey Research Methodology I (19137)
- Ability to analyze survey data. - Ability to solve frequent problems in survey analysis, such as dealing with missing cases. - Ability to work with aggregate, multilevel and longitudinal data.
Skills and learning outcomes
Description of contents: programme
1. Survey Data Analysis 1.1. Operationalization and data cleaning 1.2. Cross-sectional data analysis 1.3. Panel data analysis 1.4. Multilevel analysis 2. The use of survey data in forecasting 2.1. Predicting versus explaining 2.2. Performance evaluaciĆ³n 3. Dealing with missing data 3.1. Complete case analysis 3.2. Mean/median substitution 3.3. Multiple imputation 4. Data reporting 5. Work examples 5.1. Implementation of acquired knowledge to the analysis of social reality, in line with the Sustainable Development Goal on gender equality, equity, and non-discrimination.
Learning activities and methodology
Training Activities: - Theoretical-practical classes - Group work - Individual student work - Partial and final examinations Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Critical reading of texts recommended by the subject professor: Press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate knowledge of the subject. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group. - Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the subject, as well as practical case studies. - Developing pieces of work and reports, individually or in group.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50

Basic Bibliography
  • Alisson, Paul. Missing Data. Sage Publications. 2001
  • Brown, J. D. . Using Surveys in Language Programs. Cambridge University Press. 2001
  • Gelman, A.; Hill, J,; Vehtari, A.. Regression and other stories. Cambridge University Press. 2020
  • Luke, D.A. . Multilevel Modeling. Sage Publications. 2019
  • Wickham, H. & Grolemund, G.. R for Data Science. O'Reilly Media, Inc. 2016
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Allison, P. . Fixed Effects Regression Models. Sage Publications. 2009
  • Finch, W. H.; Bolin, J. E. & Kelley, K. . Multilevel Modeling Using R. Crc Press. 2019
  • Long, J. S. SAGE publications. Regression models for categorical and limited dependent variables. Sage Publications. 1997
  • Stevens, J.S.. Applied Multivariate Statistics for the Social Sciences. Routlegde. 2009
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.