Curso 2025/2026 | guias.usal.es

Curso Académico 2025/2026

METHODS AND TECHNIQUES

Estudios Globales / Global Studies

1. Subject Information

(Date last modified: 27-05-25 13:18)

Code: 140506
Plan: 405
ECTS: 6.00

Type: Basic
Year: 1
Duration: Second semester

Language: ENGLISH

Area

Departament: Derecho Público General

Virtual platform: Campus Virtual de la Universidad de Salamanca

Professor Information

Profesor/a: Rodrigo Rodrígues Silveira
Group/s: Único

Centre: Fac. Derecho

Office: 117

Office hours: Under request by e-mail

Web address: http://campus.usal.es/~acpa/member/rodrigo-rodrigues-silveira/ http://www.acpa-usal.com/

E-mail: rodrodr@usal.es
Phone: 923294500 Ext. 1617

Professor: Eduardo Barreto Martín
Group/s: Único

Centre: Fac. Derecho

Office: Planta Jardín

Office hours: -

Web address: -

E-mail: eduardobarretomartin@usal.es
Phone: -

Professor: Sofía Anabell Martínez Osorio
Group/s: Único

Centre: Fac. Derecho

Office: Planta jardín

Office hours: -

Web address: -

E-mail: sofiaosorio@usal.es
Phone: -

2. Association of the subject matter within the study plan

Curricular area to which the subject matter pertains.

Formación Básica (Basic Training)

Purpose of the subject within the curricular area and study plan.

Mandatory

Professional profile.

Undergraduate students.

3. Prerequisites

4. Learning objectives

The course “Methods and Techniques” aims at introducing students to the main subjects related to research. They will be informed about what means of performing research, what are the methods available in the social sciences, and the techniques employed to perform scientific work. The course also introduces many new concepts such as data analysis, inductive and deductive research, data collecting and validation, as well as serves as an introduction to some basic methods and techniques such as comparative and case analysis, or statistical and content analysis. During the course, students will perform practical sessions to assess their knowledge of the subjects discussed and practice with some real data and scenarios. At the end of the course, students will be capable of making a clear distinction between qualitative and quantitative methods, different types of data, and determining what is the best method(s) and technique(s) for a given research problem.

5. Contents

Theory.

## About the course

This course is designed to guide students through the methods and techniques used in data analysis. Its purpose is to introduce you in the basic steps on how to solve analytical problems and decision-making challenges with data. It is divided into 14 sessions:

**1. Introduction.** We will discuss the characteristics of the course and how important data analysis has become in the last years for any professional both inside and outside academia. I must put clearly that I expect they use quantitative data analysis during the course.

**2. "Houston, we've had a problem."** This famous quotation from the Apollo 13 mission will be the starting point for our discussion on how to identify and define a problem in data analysis. We can use another famous citation from Alice in Wonderland to illustrate the importance of defining the problem before collecting data. In a passage where she is lost in the forest, Alice asks the Cheshire Cat which way she should go. The cat asks her where she wants to go. Alice says she doesn't know. The cat replies that it doesn't matter which way she goes if she doesn't know where she wants to go. The same is true for data analysis. If you don't know what you want to find, you will never find it or, at best, you will eventually find something. In this session, we will start learning how to define a problem that can be solved using data analysis.

**3. Prompt "engineering" or how to avoid AI frying your brain.** Most students use AI tools almost every day. Nonetheless, they have not received proper training to do so. They also are not conscious about the limitations, biases, and risks posed by these tools. In this practical session students will learn how to create useful prompts to guide LLM (Large Language Models, i.e., chatGPT and substitutes) to help them in their academic tasks. They will learn how to design prompts obtain information and help their work and to avoid using AI as a replacement for their brains.

**4. Getting help.** In this session you will learn how to find information about the problem you are trying to solve. Here we will discuss how to search for literature and data that can assist you in your quest. We will also get a hint on how to use LLM prompts to guide us through the process. We will also discuss how to cite properly. In this class, I will introduce the Chicago author-date style and some tools to help you with citations. I will also provide you with a list of useful resources to help you in your research.

**5. Literature review**. In this session, you will learn how to write a literature review, summarize the main findings of different academic texts, and identify gaps in the literature. You will also learn how to write a literature review that is clear, concise, and well-organized. You will also learn how to use LLMs to help you evaluate and improve your literature review (not doing it for you).

**6. Working with data.** In this session, you will learn the basics of working with data sets. Observations, variables, formulas... Here, we will start developing the essential skills for working with data. You will learn the very basics of working with spreadsheets (Excel, Google Sheets, Numbers): how to enter data, remove unnecessary information, use formulas, and create charts. I will show you how to use raw data to create relative indicators and other measures of interests. You will also learn how to explore the information to extract patterns and how to build a table.

**6. Data collection.** In this session, you will learn how to collect data. In particular, we will explore how to build a database using information available in scientific databases and international organizations repositories. It is a development of the previous task, but now we will focus on the data collection and manipulation process. Students will be given a subject and will be required to collect data from different sources. We will also discuss how to use LLMs to help you in the process of collecting data.

**7. Data visualization principles.** This class will introduce students to the principles of data visualization. We will learn the grammar of graphics and how to use different visual elements (such as color, size, shape, font, etc.) to facilitate discovery and communication of patterns in data. We also will introduce the Dear Data Project. In our practice, we will learn how to identify interpret different types of chart.

**8. Data visualization in practice.** During this section, students will be required to create different data visualizations (charts and tables). The purpose is to develop their skills to represent different types of data and to communicate the information effectively. We will also discuss how to use LLMs to help you in the process of creating data visualizations.

**9. Computational Thinking.** Here, you will learn how to think algorithmically. We will discuss how to break down a problem into smaller parts and how to solve each part separately. We will also discuss how to use algorithms to solve problems and how to evaluate the efficiency of an algorithm. Performance, optimization, alternative solutions, and complexity will be discussed.

**10. Principles of Data analysis.** In this session, you will learn how to analyze data. We will discuss the different types of data analysis (descriptive, inferential, predictive, etc.) and how to choose the right type of analysis for your problem.

**11. Data analysis in practice.** In this session, you will be required to analyze some charts and tables. The purpose is to develop your skills to interpret different types and to identify patterns in the data. We will also focus on connecting multiple pieces of empirical evidence into a coherent narrative. As in previous sections, we will also discuss how to use LLMs to help you in the process of analyzing data.

**12. Storytelling with data.** In this session, you will learn how to tell a story with data. We will discuss how to use data to support your arguments and how to present your findings concisely. We will also discuss how to use LLMs to help you in the process of storytelling with data.

**13. What's next?** In this session, I will show you how data analysis really works in a research context. I will introduce you with R and show you some basic analyses using more complex datasets. I will also introduce you to more complex data structures, such as networks and relational data.

**14. Overall review of the course.** In this session, we will revise the knowledge acquired in class during the semester and evaluate the course for improvements and future developments.

## What this course is not

There are some vital knowledge for data analysis that we will not cover in this course. We will not learn statistics here (beyond some very basic concepts). We also will not cover data analysis programming languages such as R or Python. Statistics + data analysis tools are essential for any professional data analysis. However, this course is designed to be a first step for those who are not familiar with data analysis. You can find more advanced courses in the internet for free. For those interested in learning more and completing their training with skills that will not be taught in the university, I can recommend some courses and books.

Practice.

All sessions will be practical, combining both theory and its practical application, as detailed in the previous section

6. Competences acquired

Basic / General.

The course will promote the following skills:

Understanding what data analysis is
Developing basic analytical capacities
Critical thinking about research
Being able to locate and generate useful information for the analysis

Specific.

The specific skills fostered are the following:

The capacity to classify information and measurement types
Ability to differentiate between quantitative and qualitative methods
Ability to recognize the adequate method and technique for the analysis

Transversal.

Critical thought and data analysis
Structured reading of scientific texts
Autonomy in finding bibliographical resources
Autonomy in performing research work

7. Teaching methods

## The work dynamics

**The course format is hybrid.** I combine both flipped class and classical lecture formats. In flipped classes, you study the material and prepare BEFORE the class and the class is used to discuss the material and practice. In classical lectures, I will present the material, and you will be required to study it later. I will explain how we will be going to work during the semester in the first session of the course.

**The course is designed to be practical, and attendance is mandatory.** You will learn by doing. Some sessions will have a theoretical part followed by a practice. Other will be entirely practical. You will be required to work alone, in small groups, in class, and at home. You are required to assist classes. Most of the evaluations will be done in class. I will not accept any excuse for not attending class if not justified and properly documented. Travels, holidays and extra-class activities are not valid excuses.

**Work consistently, day-by-day.** Avoid leaving things behind. It is important not to leave everything until the day of the task. Watch the videos and visit the links I provide before coming to class. I guarantee that you will be far more prepared to discuss the material and to perform the practical exercises.

**Use my skills.** I’m here to support you every step of the way. With over 30 years of experience in data analysis, both in academia and beyond, I'm happy to offer tutoring, guidance, and feedback. I truly enjoy helping others grow. Remember, learning is a journey that takes time, effort, and most importantly, the right support.

**Leverage the strengths of your peers!** You are not on this journey alone—you are part of a group of students experiencing the same learning process. Collaborate by asking for help, seeking guidance, and sharing feedback with your colleagues. Similarly, you can support them in their learning. Remember, learning is a collaborative and social experience. While teamwork is encouraged, it's important to focus on your own growth and efforts rather than simply replicating others' work. With dedication and commitment, you'll achieve meaningful progress!

**Embrace technology! ** Together, we can learn how to make the most of chatGPT and other LLMs to support you in your research tasks. Technology is a powerful ally, and we will use it to enhance our productivity and creativity. While it is absolutely not about letting ChatGPT do everything for us, the quality of the results will reflect your curiosity, critical thinking, and creativity. Remember, the true potential of AI lies in how intelligently it is used by you. With thoughtful application, AI can assist in producing outstanding research, helping you unlock new possibilities!

8. Anticipated distribution of the use of the different teaching methods

9. Resources

Reference books.

This is just a basic reading list. The complete bibliography will be posted on the webpage of the course at the beginning of the second semester:

Adcock, Robert, and David Collier. 2001. “Measurement Validity: A Shared Standard for Qualitative and Quantitative Research.” American Political Science Review 95 (3): 529–46.

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.

Carmines, Edward G., and Richard A. Zeller. 1979. Reliability and Validity Assessment. Vol. 17. Quantitative Applications in the Social Sciences. London: Sage Publications.

Coppedge, Michael. 2012. Democratization and Research Methods. Cambridge ; New York: Cambridge University Press.

Few, Stephen. 2012. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Second edition. Burlingame, Calif: Analytics Press.

Few, Stephen. 2015. Signal: Understanding What Matters in a World of Noise. Burlingame, California: Analytics Press.

George, Alexander L., and Andrew Bennett. 2007. Case Studies and Theory Development in the Social Sciences. Cambridge: MIT Press.

Gerring, John. 1999. “What Makes a Concept Good? A Criterial Framework for Understanding Concept Formation in the Social Sciences.” Polity 31 (3): 357–93.

Gerring, John. 2007. Case Study Research: Principles and Practices. Cambridge: Cambridge University Press.

Gerring, John. 2012. Social Science Methodology: A Unified Framework. 2 edition. Cambridge ; New York: Cambridge University Press.

Goertz, Gary, and James Mahoney. 2012. A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton, N.J: Princeton University Press.

Goertz, Gary. 2006. Social Science Concepts: A User’s Guide. Princeton: Princeton University Press.

Kabacoff, Robert. 2015. R in Action: Data Analysis and Graphics with R. Second edition. Shelter Island: Manning Publications.

King, Gary, and Sidney Verba. 1994. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton, N.J.: Princeton University Press.

Levine, David, and David Stephan. 2009. Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics. 2 edition. Upper Saddle River, N.J: FT Press.

Lupi, Giorgia, Stefanie Posavec, and Maria Popova. 2016. Dear Data. New York: Princeton Architectural Press.

Nisbet, Robert, and Paul Edward Gottfried. 2001. Sociology as an Art Form. 0002–Revised edition ed. New Brunswick, N.J: Transaction Publishers.

Ragin, Charles C. 2014. The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. First Edition, With a New Introduction edition. Oakland: University of California Press.

10. Assessment

Assessment criteria.

Consideraciones Generales

The evaluation aims at verifying the advancements of students during the entire semester.

Criterios de evaluación

The evaluation will consist in the delivery of 10 (ten) tasks, most of them made in class. Each task will be performed individually, but you can ask for help from your colleagues and from me. You can work in groups, but the final product must be individual. Very similar products will be penalized severely on their evaluation. Please, do not copy from your colleagues. You will thank me later.

**Rabbids Coding Contest (Optional)** Students who wish to earn an extra point can install and play the game Rabbids Coding (available on both Android and iOS devices for free). The game is designed to teach programming concepts, and it is a fun way to learn while playing.

Any student who completes all levels of the game will receive the extra point. You need to provide a screenshot of the final level completed and show me your device (phone, tablet or computer in class) to receive the point. **Remember: you need BOTH to send me the screenshot by mail to rodrodr@usal.es AND to show me your devices in class**. Additionally, this extra point will be used to break ties when awarding a "Matrícula de Honor" in cases where two students have both received a final grade of 10. **The deadline is the last class (session 14)**, but I strongly advise you to do it as soon as possible, so you will have more time to study for the final exams of other courses.

**IMPORTANT NOTES:**

Submitting each practice does not automatically guarantee the full grade. Each practice will be evaluated based on its quality and level of development. As a result, the final grade can range from 0 (not submitted or insufficiently developed) to 1 (perfect and well-developed). For instance, a literature review that is incomplete but well-written may receive half of the total grade. In this case, the final mark for the exercise would be calculated as follows: 1 × 0.5 = 0.5/10.

The final grade will be the sum of the grades of each task and will range between 0 and 10. The minimum to pass the course is 5/10.

Evaluation systems.

The evaluation will be as following:

Ten Individual assignments performed during the semester and delivered through Studium.

Assessment recommendations.

Read all texts and attend classes.

Recomendaciones para la recuperación.

All content will be evaluated in the second exam. The qualifications of previous exercises, such as the reading reports or bibliographic essays, will not be considered in this phase.

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