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Research Methods

Code: 101102
Credits: 6
2026/2027
Degree programme Type Course
Political Science and Public Management OB 3

Contact lecturer

Name :
Danislava Milkova Marinova
Email :
dani.marinova@uab.cat

Teaching staff

Juan Pérez Rajó

Group languages

You can consult this information at the end of the document.

Prerequisites

It is required that students have passed the compulsory course Methodology of Political Analysis from the second year of the undergraduate degree.

Objectives

This course is the third and most advanced in the data analysis sequence of the degree, and it builds directly on the foundations laid by Introducció a l'Anàlisi de Dades and Metodologia de l'Anàlisi Política. Whereas the previous courses introduced students to handling and describing data and to the logic of inference and bivariate regression, the aim of this course is for students to extend their analytical toolbox and learn to apply a wider range of social science research techniques autonomously.


The first part of the course develops linear regression beyond the bivariate case (analysis of variance, regression with nominal predictors, multiple regression, and interactions) and addresses advanced data management (reshaping, appending, and merging) and the critical construction of measures, including composite indices. The second part turns to applied survey research, with particular attention to the sampling, weighting, and design effects involved in analysing real microdata, and to the communication and dissemination of results through technical reports. The course closes with an overview of further families of models and an introduction to causal inference and impact evaluation, equipping students to pursue these on their own.


Throughout, we prioritise practical training and the interpretation and presentation of results over mathematical issues, consolidate the use of the R statistical computing language through RStudio, and work with real-world, socially and politically relevant data while encouraging a critical and responsible use of open data.

Learning outcomes

  1. Synthesizing and critically analysing information.
  2. Interpreting and applying English texts in an academic way.
  3. Arguing from different theoretical perspectives.
  4. Working by using quantitative and qualitative analysis techniques in order to apply them to research processes.
  5. Using the main information and documentation techniques (ICT) as an essential tool for the analysis.
  6. Demonstrating good writing skills in different contexts.
  7. Showing a good capacity for transmitting information, distinguishing key messages for their different recipients.
  8. Realising effective oral presentations that are suited to the audience.
  9. Managing the available time in order to accomplish the established objectives and fulfil the intended task.
  10. Working autonomously.
  11. Designing data collection techniques, coordinating the information processing and meticulously applying hypothesis verification methods.
  12. Designing and planning an investigation in the field of political sciences.
  13. Critically assessing the usage of inductive, deductive and comparative methods.
  14. Critically assessing the use of analytical instruments to validate the hypothesis raised.
  15. Demonstrating the comprehension of the logic behind the scientific analysis of political sciences.
  16. Managing the methodological foundations of politic sciences.
  17. Develop critical thought and reasoning and be able to communicate them effectively, both in your own language and second or third languages.
  18. Develop strategies for autonomous learning.
  19. Act with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values.
  20. Make changes to the methods and processes of the area of knowledge to provide innovative responses to the needs and wishes of society.
  21. Assess the social, economic and environmental impact when acting in this field of knowledge.
  22. Take sex- or gender-based inequalities into consideration when operating within one's own area of knowledge.
  23. Analyse political databases in each case using the appropriate basic techniques of descriptive statistics and inferential statistics.
  24. Apply the corresponding statistical techniques to distinct case studies and interpret the results obtained.
  25. Use computer tools to collect, import, manipulate, visualise, describe, and model data of all kinds, and present the results.

Contents

  1. Bivariate regression with a continuous independent variable (review)
  2. ANOVA: one-way and factorial
  3. Regression with a nominal independent variable
  4. Multiple regression
  5. Interactions (moderation) and the distinction from mediation
  6. Data management: reshaping, appending, and merging databases
  7. Sampling, weighting, and design effects in the analysis of microdata with R
  8. Construction of composite indices
  9. Communicating and disseminating results through technical reports
  10. Overview of other model families
  11. Models for causal inference and impact evaluation

Learning activities and methodology

Title Hours ECTS Learning outcomes
Lectures 30 1.2
Lab and in-class exercises 19.5 0.78
Study 83.5 3.34
Tutorials 15 0.6

In-person sessions include two types of activities:

  1. Lectures delivered by the teaching staff. These sessions present the theoretical and methodological foundations of each thematic block (bivariate and multiple regression, ANOVA, interactions, data management and merging, sampling and weighting, construction of composite indices, models for causal inference, among others), always illustrated with applied examples and using the R statistical software.
  2. Exercises and hands-on practice in class. These are predominantly applied, computer-based sessions in which students work with real or simulated datasets to put into practice the concepts presented in the lectures, with the direct support and supervision of the teaching staff. These sessions allow for immediate feedback on the use of R software and the interpretation of results, and give rise to the corresponding small graded assignments.


Beyond the in-person sessions, students must devote independent study time to:

  • Studying and consolidating theoretical content, necessary to prepare for the midterm and final exams.
  • The progressive development, in groups of two, of the group practical assignment, which involves the original collection or merging of data, analysis using a multiple regression with interaction (or an equivalent model), and the preparation of a technical report of results. This work is carried out throughout the course and may be supported by faculty guidance through office hours or in-class consultations during the practical sessions.


This combination of theoretical instruction, guided in-class practice, and independent work (both individual and group-based) aims to ensure that students not only understand the statistical methods presented, but are also able to apply them independently using R, as required by the assessment activities.

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.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Final exam 45% 0.5 0.02 1, 2, 3, 6, 7, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
Mid-term exam 35% 0.5 0.02 1, 2, 3, 4, 6, 7, 8, 9, 10, 14, 15, 16, 17, 23, 24, 25
In-class exercises 20% 1 0.04 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25

Assessment

Assessment will be based on the results of the following activities:

  1. In-class exercises and practicals (20% of the final grade). Throughout the course, various in-class exercises and practicals will be carried out, each of which involves a short submission at the end of the session. Attendance at the session is mandatory in order to be assessed on the corresponding activity: submissions will not be accepted from students who did not attend, nor after the established deadline. This activity is non-recoverable, except in duly justified cases for medical reasons or force majeure.
  2. Midterm exam (35% of the final grade). A written exam taken on a computer, with access to course materials, covering the first half of the syllabus. This activity is non-recoverable, except in duly justified cases for medical reasons or force majeure.
  3. Final exam (45% of the final grade). A written exam on the whole syllabus. No support materials may be consulted. This activity is non-recoverable, except in duly justified cases for medical reasons or force majeure.

The exams and exercises are non-recoverable, except in duly justified cases for medical reasons or force majeure. Students who need to recover a project or the midterm exam will do so on the date set by the Faculty for the recovery exam.


To pass the course, all three of the following requirements must be met:

  • Having been assessed in a set of activities representing at least 50% of the total grade,
  • Obtaining an overall grade equal to or higher than 5, and
  • Obtaining an average grade on the exams (midterm and final) equal to or higher than 4.


Remedial exam

The remedial exam is graded as pass or fail. If the student passes the exam, they will receive a final grade of 5 for the course.

To access recovery, it is necessary to:

  • Have been assessed in a set of activities representing at least two-thirds of the total grade, and
  • Have an overall grade equal to or higher than 3.5.


Synthesis test

In accordance with Article 117.2 of the UAB Academic Regulations, repeating students may, if the teaching staff so decides, take a single synthesis test. Only those who have obtained the teaching staff's permission at the start of the course (before October) may take the synthesis test.

The synthesis test will consist of the following parts, with their corresponding weight in the final grade for the course:

  • Final exam: 50%
  • Project: 20%. To be submitted on the day of the final exam.
  • Oral defense of the project: 30%. To be held on the day of the final exam.


Single assessment

This course does not allow single assessment, as established by the Faculty Board, since regular class attendance is indispensable for achieving the teaching objectives.


Use of AI

The use of artificial intelligence (AI) tools can be useful in the context of an applied statistics course, especially for detecting syntax errors or identifying possible improvements in the code. However, these tools cannot replace autonomous study or the student's real understanding of the code. The aim of the course is for students to understand and be able to apply statistical methods on their own.



Further considerations

Submitting any activity or sitting any test exempts the student from the "Not Assessable" grade.

Final exams and recovery tests will not be scheduled outside the official dates established by the Faculty. Likewise, continuous assessment tests will be carried out exclusively on the dates set by the teaching staff.

Any irregularity in an assessment activity (academic fraud, plagiarism, or improper use of AI, unless such use is expressly authorized in the course guide) that may lead to a significant variation in the grade will result in that exam or piece of work being graded 0. If the course guide stipulates that passing the course requires obtaining a minimum grade in that assessment, or if several irregularities occur in the assessment activities of the same course, the final grade for the course will be 0. In addition, a disciplinary process may be initiated against any student who commits one of these irregularities. The teaching staff reserves the right to conduct oral exams or an alternative test in order to verify the effective acquisition of knowledge and skills and, therefore, the validity of the assessment tests carried out by the students.

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

 

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 1 Catalan first semester morning-mixed
(SEM) Seminars 1 Catalan first semester morning-mixed
(TE) Theory 51 Catalan first semester afternoon
(SEM) Seminars 51 Catalan first semester afternoon