This version of the course guide is provisional until the period for editing the new course guides ends.

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Social Analysis Tools II: Techniques and Data Analysis

Code: 106979 ECTS Credits: 6
2025/2026
Degree Type Year
Sociocultural Gender Studies FB 1

Contact

Name:
Irene Cruz Gomez
Email:
irene.cruz@uab.cat

Teaching groups languages

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


Prerequisites

This course has no prerequisites, although it is recommended to have previously completed the course Tools for Analysis I: Methodology and Design.


Objectives and Contextualisation

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.


Learning Outcomes

  1. CM10 (Competence) Put teamwork skills into practice: a commitment to the team, regular collaboration, encourage problem solving, apply the ethics of care and provision.
  2. CM16 (Competence) Assess and correct your own work based on the evaluation of previous studies and having detected and identified specific needs for social intervention.
  3. KM21 (Knowledge) Analyse the theoretical framework in question and the presence or absence of the gender perspective in existing research, projects or experiences of psychosocial, educational and community intervention.
  4. SM15 (Skill) Design proposals, spaces and resources for feminist socio-educational action that involve citizen participation.
  5. SM16 (Skill) Select the appropriate methodology, tools and data collection techniques in order to diagnose and interpret gender-focused intervention needs in different contexts and situations.
  6. SM17 (Skill) Select qualitative and quantitative data to assess the intersectionality between factors such as gender, class, age, ethnicity, disability, etc.

Content

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


Activities and Methodology

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.


Assessment

Continous Assessment Activities

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

1. Assessment Model

This course requires active student participation and considers regular class attendance as a means of integrating the different learning activities.

2. Requirements to Pass the Course

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 who do not attend class regularly (attendance below 70%): the average will only be calculated if the grade for each and every part is at least a 5.

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.

SINGLE ASSESSMENT

This course/module does not include a single assessment system.

3. Resit

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.

4. Policy on Plagiarism in Academic Work or Written Exams

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.


Bibliography

  • Andréu, J. (2001). Técnicas de análisis de contenido: Una revisión actualizada. Fundación Centro de Estudios Andaluces. Departamento Sociología Universidad de Granada. España.
  • Gill, R. (2000). Discourse analysis. Qualitative researching with text, image and sound, 1, 172-190.
  • Doucet, A., & Mauthner, N. (2008). Qualitative interviewing and feminist research. The SAGE handbook of social research methods, 328-343.
  • Folgueiras Bertomeu, Pilar (2016). Técnica de recogida de información: La entrevista. Documents de treball / Informes (Mètodes d'Investigació i Diagnòstic en Educació). http://hdl.handle.net/2445/99003
  • Hueso, A., & Josep, M. (2012). Cuadernos docentes en procesos de desarrollo, n. 1. Metodología y técnicas cuantitativas de investigación. Valencia: Universidad Politécnica de Valencia. [URL: Cuadernos de Investigación en Proceso de Desarrollo]
  • Kawulich, B. B. (2005). La observación participante como método de recolección de datos. Qualitative Social Research, V6, N2, A43. http://biblioteca.udgvirtual.udg.mx/jspui/handle/123456789/2715
  • Lewin, C. (2005). Elementary quantitative methods. Research methods in the social sciences, 12(2), 215-225.
  • López-Roldán, P., & Fachelli, S. (2015). Análisis de tablas de contingencia. Metodología de la investigación social cuantitativa, cap-III.
  • McConnell, E. A., Clifford, A., Korpak, A. K., Phillips II, G., & Birkett, M. (2017). Identity, victimization, and support: Facebook experiences and mental health among LGBTQ youth. Computers in Human Behavior, 76, 237-244.
  • Oleson, K., & Arkin, R. (2006). Reviewing and evaluating a research article. The Psychology Research Handbook: A Guide for Graduate Students and Research Assistants, 2nd ed., SAGE Publications, Thousand Oaks, CA, 59-75
  • Pita Fernández, S., & Pértega Díaz, S. (2001). Estadística descriptiva de los datos. Unidad de Epidemiología Clínica y Bioestadística. Complexo Hospitalario Juan Canalejo. A Coruña, 5.
  • Rayrnond, Quivy., & Luc Van, Campenhoudt. (2005). Manual de la investigación en ciencias sociales. México DF: Limusa SA. Capítol 4
  • Research Methods for Society and Mental Health (2025). Research Methods Toolkit: Correlation. https://researchmethodstoolkit.com/quantitative-methods/correlation-analysis/
  • Sanjuán Núñez, Lucía. (2019). L'anàlisi de dades en recerca qualitativa. Materials de la Universitat Oberta de Catalunya. http://hdl.handle.net/10609/146889
  • Schweingruber, D., & McPhail, C. (1999). A method for systematically observing and recording collective action. Sociological methods & research, 27(4), 451-498.

Software

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


Groups and Languages

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