Checking date: 18/05/2022

Course: 2022/2023

Survey Research Methodology II
Study: M. Computational Social Science (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)
Core Competences: - Having and understanding the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context - Students know how to apply their acquired knowledge and problem-solving skills in new or unfamiliar settings within broader (or multidisciplinary) contexts related to their field of study. - Students are able to integrate knowledge and to face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. - Students know how to communicate their conclusions and the knowledge and ultimate reasons behind them to specialised and non-specialised audiences in a clear and unambiguous way. - Students have the learning skills that will enable them to continue studying in a way that will be largely self-directed or autonomous. General Competences: - Ability to compile and analyze existing knowledge in the different areas of computational social sciences, and to propose possible solutions to the problems raised. - Ability to apply theoretical and methodological knowledge of computational social sciences to the analysis and resolution of specific cases and empirical problems. - Ability to address issues raised under new or unfamiliar environments, within the context of computational social sciences. - Ability to plan and carry out research in the field of computational social sciences in an autonomous way. - Ability to communicate and present, in a clear, precise and rigorous manner, concepts and results related to computational social science activities to both specialized and non-specialized audiences. Specific Competences: - Ability to understand and analyze the main theoretical-methodological approaches of computational social sciences, their potentials and limitations, and to apply them to the analysis of specific social problems. - Ability to develop an experimental/causal research design appropriate to the research questions. Learning Outcomes: - Knowledge of the principles of scientific research. - Knowledge of different research designs: variable-based, case-based, comparative. - Ability to combine different research designs. - 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
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.