The course is designed with a continuous teaching and learning dynamic, which involves following the course pace and the various content that has been designed according to the different scheduled learning activities.

The course content has a common thread linked to the research process, and therefore, the continuity of learning is justified by the progressive incorporation of concepts and tools, as well as by problem-solving and questions, based on the assimilation and practice of each subject matter.

Since the goal of the training is for students to learn how to conduct research in sociology from a quantitative perspective, the teaching methodology and the course's formative activities are a combination of lectures with problem-solving exercises and classroom practices that allow the application of acquired concepts, as well as follow-up tutorials and independent work.

In this regard, the theoretical and practical content are equally weighted in this course, meaning the number of sessions is divided equally between theory and practice, which is also reflected in the assessment.

This course does not provide for a single-assessment system.

Overall, the course assessment consists of five components: course participation and continuous assessment (5%), group project (35%), assessment of instrumental skills (20%), a written exam on data collection (20%), and a written exam on data analysis (20%).

Continuous Assessment

Throughout the semester, students will be required to submit a number of practical exercises. These may be completed either during class sessions or as work outside the classroom.

Although this component cannot be retaken, exceptional and duly justified circumstances will be taken into account, especially when the exercises are carried out during class.

Group Project

The research project will be completed in groups of 4–5 students and consists of two parts: data collection (20%) and data analysis (15%).

• Data Collection. Based on a research question, students must design a survey questionnaire capable of addressing the proposed research question, collect the data, and prepare a data matrix. This part of the project cannot be retaken.
• Data Analysis. Students will conduct an initial univariate analysis of the data, including exploratory, descriptive, and inferential analyses. This part may be revised or repeated to achieve the minimum overall passing grade.

To pass the group project, students must obtain a minimum score of 4 out of 10 across the two submissions. If the final project grade is below 4, students will not be eligible to sit the final examination, and the final course grade will be 2 out of 10.

Instrumental Skills

Around the middle of the semester, students' proficiency in spreadsheet software and their ability to use RStudio for data and file management will be assessed.

Students who obtain a score below 4 must take the final examination.

Data Collection

Around the middle of the semester, a written examination will assess students' knowledge of data collection methods.

Students who obtain a score below 4 must take the final examination.

Data Analysis

On the last day of class, a written examination will assess students' knowledge of univariate data analysis.

Students who obtain a score below 4 must take the final examination.

FINAL EXAMINATION

Students may sit the final examination only if they have obtained a minimum score of 4 on the group project.

The final examination is compulsory for students who score below 4 in any of the following components: instrumental skills, data collection, or data analysis; and for those whose weighted overall average is below 5.

Non-Assessable Students

Students who fail to submit both parts of the group project by the established deadlines will be considered non-assessable.

Academic Integrity and Plagiarism

Students are reminded that, upon enrolment, they agreed to the following commitment:

"I DECLARE that the Universitat Autònoma de Barcelona has informed me that (...) Plagiarism is the act of disseminating, publishing, or reproducing all or part of a work under the name of someone other than its true author. This constitutes the misappropriation of ideas created by another person without explicitly acknowledging their origin. Such appropriation infringes the intellectual property rights of that person and is never permitted, regardless of the context, including examinations, assignments, or practical work. Therefore, I COMMIT to respecting the regulations governing intellectual property rights in relation to the teaching and/or research activities carried out at the UAB as part of my studies."

If plagiarism is detected, the corresponding assignment, examination, individual work, or group project will receive a grade of 0.

Use of Artificial Intelligence

The use of Artificial Intelligence (AI) technologies is permitted in this course only for support tasks such as literature and information searches, proofreading, translation, and occasional assistance in the use of RStudio. In the latter case, students must justify how AI was used and demonstrate that they understand the syntax or code provided by the AI.

Any academic misconduct during an assessment activity (including academic fraud, plagiarism, or unauthorized use of AI beyond what is explicitly permitted above) that results in a significant alteration of the assessment outcome will lead to a grade of 0 for that assessment and, consequently, a final course grade of 0. In addition, disciplinary proceedings may be initiated against students who commit such violations. The teaching staff reserves the right to conduct oral interviews or alternative assessments to verify the actual acquisition of knowledge and competencies and, therefore, the validity of the assessment submitted by the student.