Degree | Type | Year |
---|---|---|
Sociocultural Gender Studies | FB | 1 |
You can view this information at the end of this document.
This course has no prerequisites, although it is recommended to have previously completed the course Tools for Analysis I: Methodology and Design.
The primary interest of the subject is to provide the students with the theoretical foundations and technical instruments for the application of qualitative and quantitative techniques in the phase of the empirical comparison of the investigation, in particular, data analysis. The course will focus especially on the methods and qualitative techniques of observation and analysis of qualitative data (content analysis and discourse analysis). And from a quantitative perspective, the subject focuses on univariate and bivariate descriptive statistical analysis techniques.
TRANSVERSAL MODULE
Topic 1. The bibliographic search
• Support tools
• Construction of a bibliography
• Bibliographic citation in APA7 style
• Software for information management
QUALITATIVE MODULE
Topic 1. Observation techniques: direct observation
• Conceptual and terminological clarification
• Aspects of the design, field and execution of direct observation
• Advantages and limitations of observation
Topic 2. The in-depth interview
• The interview from a feminist perspective
• The design of the interview script
• Typological grid and case file
• The execution of the interview
Topic 3. Content analysis and qualitative thematic analysis
• The epistemic framework
• The elements of analysis and research strategies
• Methods and analysis techniques
• Support tools for qualitative analysis
QUANTITATIVE MODULE
Topic 1. The structure of quantitative data
• The question, operationalization and model of analysis
• Metrics
• Dimensionality and heterogeneity
• Procedure map
Topic 2. Univariate descriptive analysis
• Measures of central tendency, position and dispersion
• Frequency tables
• Rates, ratios, proportions and index
• Graphical representation of a variable
Topic 3. Bivariate descriptive analysis
• Comparison of means and grouped box diagrams
• Correlation, regression line and dispersion diagram
• Contingency tables and stacked bar diagrams
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Master lecture | 37 | 1.48 | |
Workshops | 15 | 0.6 | |
Type: Supervised | |||
Programmed group supervision | 15 | 0.6 | |
Type: Autonomous | |||
Group work | 23 | 0.92 | |
Individually writting papers | 11 | 0.44 | |
Preparing individually written tests | 22 | 0.88 | |
Reading | 23 | 0.92 |
Given that the subject is fundamentally oriented towards learning the basic techniques of quantitative and qualitative analysis, the teaching methodology and formative activities of the subject place it at the center of the teaching-learning process. Thus, the teaching methodology will combine: expository sessions (to guide and clear doubts about the mandatory readings), face-to-face practices (in seminars, and in classrooms to guide and clear doubts about the mandatory readings), face-to-face practices (in seminars, and in computerized classrooms ). This teaching format allows you to apply the acquired concepts and explained techniques, combining throughout the course with follow-up tutorials and independent work. Next, the different activities are specified, with their specific weight within the distribution of the total time that the student must dedicate to the subject.
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.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
participation and continuous assessment | 10% | 2 | 0.08 | CM10, CM16, KM21, SM15, SM16, SM17 |
Qualitative module: final test | 20% | 0.5 | 0.02 | KM21, SM15, SM16, SM17 |
Qualitative module: mid-term | 20% | 0.5 | 0.02 | CM16, KM21, SM15, SM16, SM17 |
Quantitative module: final exam | 20% | 0.5 | 0.02 | CM16, KM21, SM15, SM16, SM17 |
Quantitative module: mid-term | 20% | 0 | 0 | CM16, KM21, SM15, SM16, SM17 |
Transversal module: mid-term | 10% | 0.5 | 0.02 | CM16, KM21, SM15, SM16, SM17 |
This course requires active student participation and considers regular class attendance as a means of integrating the different learning activities.
To pass the course, a minimum final grade of 5 is required, calculated as a weighted average of the 6 assessment activities. See the table below for the weight distribution of each activity.
For the calculation of this weighted average, the following criterion will be applied depending on course attendance:
Students will be considered “Not assessable” if they do not attend the partial exams and have not completed at least 50% of the continuous assessment.
This course/module does not include a single assessment system.
The resit will consist of a single exam covering all assessment blocks in which a grade below 4 was obtained or which were not completed by the established deadline. The maximum grade that can be obtained in the resit is 7.
We remind you that, upon signing your enrollment, you agreed to the following:
“I DECLARE that the Universitat Autònoma de Barcelona has informed me that (...) Plagiarism is the act of disclosing, publishing, or reproducing a work or part of it under the name of an author other than the original one, which constitutes an appropriation of ideas created by another person without explicitly acknowledging their origin. This appropriation infringes on that person’s intellectual property rights, and I am not authorized to do so under any circumstances: exams, assignments, practicals… Therefore, I COMMIT to respecting the regulations regarding intellectual property rights in relation to the teaching and/or research activities carried out by the UAB in the studies I am pursuing.”
In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of the development of assignments, provided that the final result reflects a significant contribution from the student in terms of analysis and personal reflection. The student must clearly identify which parts were generated using this technology, specify the tools used, and include a critical reflection on how these influenced the process and the final outcome of the activity. Lack of transparency in the use of AI will be considered academic dishonesty and will be treated as plagiarism, potentially resulting in a failing grade for the activity.
Exams: If students are found to have copied unauthorized content, all individuals involved will automatically fail without the possibility of resitting.
In cases of plagiarism in written assignments, each case will be evaluated individually and, in extreme cases, may result in a direct fail without the option to resit.
Document processor: LibreOffice Writer, or MicroSoft-WORD
Support for presentations: LibreOffice Impress, or MicroSoft-POWERPOINT
Spreadsheet: LibreOffice Calc, or MicroSoft-EXCEL
Quantitative data transformation and analysis: RStudio/jamovi
Treatment of qualitative data: RQDA
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PAUL) Classroom practices | 1 | Catalan/Spanish | second semester | morning-mixed |
(TE) Theory | 1 | Catalan/Spanish | second semester | morning-mixed |