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2021/2022

Research Methods

Code: 101102 ECTS Credits: 6
Degree Type Year Semester
2500259 Political Science and Public Management OB 3 1
The proposed teaching and assessment methodology that appear in the guide may be subject to changes as a result of the restrictions to face-to-face class attendance imposed by the health authorities.

Contact

Name:
Guillem Rico Camps
Email:
Guillem.Rico@uab.cat

Use of Languages

Principal working language:
catalan (cat)
Some groups entirely in English:
No
Some groups entirely in Catalan:
Yes
Some groups entirely in Spanish:
No

Teachers

Daniel Baliñas Pérez

Prerequisites

Students should have acquired basic concepts of research methods. It is highly advisable that they have completed the compulsory course of Methodology of Political Analysis. They must be able read English and work with spreadsheets (Excel).

Objectives and Contextualisation

The aim of this course is that students familiarize with the main social science research techniques and learn how to use them. The bulk of the course is devoted to linear regression analysis and its extensions. We will prioritize practical training and the interpretation and presentation of results over mathematical issues. At the same time, the course will introduce students to the R language of statistical computing through RStudio, to provide the essential skills for data management, exploratory data analysis, data visualization, reproducibility, and effective communication of results. Throughout the course we will work with real-world, socially- and politically-relevant data, while also encouraging a critical and responsible usage of open data. 

Competences

  • Applying the different behaviour analysis techniques and political actors to real cases from the internal and international political arena.
  • Applying the discipline's main theories and different fields to real practical and professional problems.
  • Arguing from different theoretical perspectives.
  • Demonstrating good writing skills in different contexts.
  • Demonstrating the comprehension of the logic behind the scientific analysis of political sciences.
  • Designing data collection techniques, coordinating the information processing and meticulously applying hypothesis verification methods.
  • Managing the available time in order to accomplish the established objectives and fulfil the intended task.
  • Managing the methodological foundations of politic sciences.
  • Realising effective oral presentations that are suited to the audience.
  • Showing a good capacity for transmitting information, distinguishing key messages for their different recipients.
  • Synthesizing and critically analysing information.
  • Using the main information and documentation techniques (ICT) as an essential tool for the analysis.
  • Working autonomously.
  • Working by using quantitative and qualitative analysis techniques in order to apply them to research processes.

Learning Outcomes

  1. Arguing from different theoretical perspectives.
  2. Critically assessing the usage of inductive, deductive and comparative methods.
  3. Critically assessing the use of analytical instruments to validate the hypothesis raised.
  4. Demonstrating good writing skills in different contexts.
  5. Demonstrating the comprehension of the logic behind the scientific analysis of political sciences.
  6. Designing and planning an investigation in the field of political sciences.
  7. Designing data collection techniques, coordinating the information processing and meticulously applying hypothesis verification methods.
  8. Identifying main actors of the political system, inspecting their interactions and assessing their behaviour in their environment and in the political system from a theoretical and practical perspective.
  9. Managing the available time in order to accomplish the established objectives and fulfil the intended task.
  10. Managing the methodological foundations of politic sciences.
  11. Realising effective oral presentations that are suited to the audience.
  12. Showing a good capacity for transmitting information, distinguishing key messages for their different recipients.
  13. Synthesizing and critically analysing information.
  14. Using a database of political data using in each case specific basic techniques of descriptive statistics.
  15. Using the main information and documentation techniques (ICT) as an essential tool for the analysis.
  16. Working autonomously.
  17. Working by using quantitative and qualitative analysis techniques in order to apply them to research processes.

Content

1. Data visualization and exploratory data analysis
2. Data management
3. Simple linear regression
4. Multiple regression
5. Categorical independent variables
6. Regression models for categorical dependent variables

Methodology

There are two types of directed activities:

  1. Lectures
  2. Lab and in-class exercises

 

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Activities

Title Hours ECTS Learning Outcomes
Type: Directed      
Lab and in-class exercises 19.5 0.78 1, 5, 4, 6, 7, 11, 9, 8, 12, 13, 17, 16, 10, 15, 14, 3, 2
Lectures 30 1.2 1, 5, 4, 6, 7, 11, 9, 12, 13, 17, 10, 15, 14, 3, 2
Type: Supervised      
Tutorials 15 0.6 1, 5, 4, 6, 7, 11, 9, 8, 12, 13, 17, 16, 10, 15, 14, 3, 2
Type: Autonomous      
Study 83.5 3.34 1, 5, 4, 6, 7, 9, 8, 12, 13, 17, 16, 10, 15, 14, 3, 2

Assessment

The evaluation will be based on the following activities:

  • In-class exercises (10%). Attendance to the corresponding class is mandatory to pass an exercise. No late submissions will be accepted. This part of the evaluation is not recoverable.
  • Lab assignments (40%). Attendance to the corresponding class is mandatory to pass an assignment. No late submissions will be accepted. This part of the evaluation is not recoverable.
  • Final exam (50%). A closed-book exam covering the course content.

To pass the course, it is required that all of the following conditions are met:

  1. Having been previously been evaluated for at least two thirds of the total evaluation activities of the subject.
  2. Achieving a grade greater than or equal to 4 in the exam.
  3. Achieving a final grade greater than or equal to 5.

Retake process

Only the exam is recoverable; in-class exercises and lab assignments are excluded from the retake process.

To be eligible to participate in the retake process, it is required that both these conditions are met:

  1. Having been previously been evaluated for at least two thirds of the total evaluation activities of the subject.
  2. Achieving a final grade greater than or equal to 3.5.

Important considerations

  • The fact of taking the exam or handing in an exercise or an assignment exempts the student from the “Not assessable” grade.
  • In accordance with article 117.2 of the UAB Academic Regulation, the evaluation of those students who have been enrolled before may consist of a single synthesis examination. The students who wish to be evaluated this way should contact the professor at the beginning of the semester (first week of October at the latest).
  • Evidence of plagiarism or any other irregularity that could leadto a significant variation in the grade of an activity involves failing the correspondingevaluation with a grade of 0. In case of multiple irregularities in the evaluation of the same subject, the final gradeof this subject will be 0.

Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Final exam 50% 2 0.08 1, 5, 4, 6, 7, 9, 8, 12, 13, 17, 16, 10, 15, 14, 3, 2
In-class exercises 10% 0 0 1, 5, 4, 6, 7, 11, 9, 8, 12, 13, 17, 16, 10, 15, 14, 3, 2
Lab assignments 40% 0 0 1, 5, 4, 6, 7, 11, 9, 8, 12, 13, 17, 16, 10, 15, 14, 3, 2

Bibliography

Basic

Complementary

  • Chang, Winston. 2018. R Graphics Cookbook: Practical Recipes for Visualizing Data. Second edition. Beijing; Boston: O’Reilly. Freely available at r-graphics.org.
  • Ismay, Chester, & Albert Young-Sun Kim. 2020. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. Chapman & Hall/CRC the R Series. Boca Raton: CRC Press / Taylor & Francis Group. Freely available at moderndive.com.
  • Riba, Clara, & Anna Cuxart. 2013. Regresión Lineal Aplicada. Barcelona: Documenta Universitaria.
  • Wickham, Hadley, & Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Sebastopol, CA: O’Reilly. Freely available at r4ds.had.co.nz. Spanish version: es.r4ds.hadley.nz.

Software