Mandatory (Obligatoria)

Mandatory (Obligatoria)

Social researcher

1. An Introduction to Quantitative Analysis.
2. Describing Data.
3. Plotting Relationship and Conditional Distribution.
4. Causation and Models of Causal Effects.
5. Probability.
6. Sampling Distribution and Inference.
7. Statistical Tests for:

• Associations
• Differences between groups
• Predictions
• Data or dimension reduction

To ensure the acquisition of each of the theoretical aspects listed, each topic is supported by a practical activity that includes the use of statistical data processing software.

CB2. That students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are often demonstrated through the development and defense of arguments and problem solving within your area of study.

CB3. Students have the ability to gather and interpret relevant data normally within their area of study of to make judgments that include a reflection on relevant social, scientific, or ethical issues.

CB4. To enable students to convey information, ideas, problems, and solutions to a specialized audience as unskilled.

CB5. Students have developed those learning skills necessary to undertake studies with a high degree of autonomy.

CG1 - Apply the knowledge acquired to solve specific problems in new or unfamiliar environments within of broader (or multidisciplinary) contexts.

CG2 - To know and apply the different research methods in the field of social sciences to analyze problems in relation to their specialty.

CE19 - Mastering advanced political and social research techniques and operating with research data quantitative and/or qualitative

Critical thought.

Develop capacity to collect and analyze raw data.

Ability to understand and interpret scientific research results.

Basic text book:

Dietz, T., & Kalof, L. (2009). Introduction to Social Statistics: the logic of statistical reasoning. John Wiley & Sons.

Additional References:

Box-Steffensmeier, Janet M., Henry E. Brady, and David Collier, eds. (2010). The Oxford Handbook of Political Methodology. 1 edition. Oxford New York: Oxford University Press.

Imai, K. (2018). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.

Martin, J. L. (2017). Thinking through methods: A social science primer. University of Chicago Press.

Morton, R. (1999). Methods and Models: A Guide to the Empirical Analysis of Formal Models in Political Science. Cambridge: Cambridge University Press.

Osborne, J. W. (Ed.). (2008). Best practices in quantitative methods. Sage.

Spiegelhalter, D. (2019). The art of statistics: learning from data. Penguin UK.

Theobald, O. (2017). Statistics for Absolute Beginners: A Plain English Introduction. Scatterplot Press.

Tokunaga, H. T. (2018). Fundamental statistics for the social and behavioral sciences. Sage Publications.

Wheelan, C. (2013). Naked statistics: Stripping the dread from the data. WW Norton & Company.

The evaluation aims at verifying the advance of students during the entire semester. The knowledge taught is progressive. It ranges from the simplest to the most complex items.

The final grade will be the sum of all the practices carried out during the course (70%).

 A final research report on the application of any of the methods or techniques learned during the course (30%).

The instruments used for the configuration of the practices are:

• Preparation of reports where the techniques learned are applied in the processing of real data and where various research questions are answered.
• Conducting short tests on the Studium platform to verify the knowledge acquired.

Attend classes, follow the course closely trying to achieve the objectives in each unit and practice at home.

All content will be evaluated in the second call. It will consist on a research paper where the student will have to demonstrate that he or she has acquired all the skills of the course. Grades from previous exercises, such as reports or tests, will not be considered at this stage.