Teaching plan for the course unit (Short version)
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General information

 

Course unit name: Social Research Techniques III

Course unit code: 360920

Academic year: 2025-2026

Coordinator: Jose Luis Condom Bosch

Department: Department of Sociology

Credits: 6

Single program: S

Estimated learning time

Total number of hours 150

 

Face-to-face and/or online activities

60

	-  Lecture with practical component	Face-to-face		30
	-  Group tutorial	Face-to-face		15
	-  IT-based class	Face-to-face		15

Supervised project

40

Independent learning

50

Learning objectives

Referring to knowledge General objective The aim of this course is to make the fundamental step from descriptive statistical analysis of relationships to a sociological understanding of explanatory and causal models of social reality. Students are introduced to advanced statistical techniques for use in explanatory/causal sociological analysis and to enhance understanding of social phenomena. Sociological statistical analysis thus becomes a central component of the theoretical development process. Knowledge-related objectives Understand the logic and process of analysing explanatory and causal models in sociology. Be able to apply multivariate explanatory and causal analysis techniques according to the nature of the variables. Recognize and apply different types of control variables. Understand the sociological and interpretative approach to multivariate explanatory analysis. Be able to construct and analyse explanatory and causal models. Be able to conduct comparative analyses. Understand the general linear model.   Referring to abilities, skills Formulate and test theoretical models. Interpret and write reports based on a multivariate analysis. Know how to work with IBM SPSS when analysing data. Use secondary data to test theoretical models. Apply appropriate analytical techniques to construct, analyse, and interpret explanatory and causal models. Use indices and typologies in causal analysis. Compare groups and sub-populations in explanatory data analyses.

 

 

Teaching blocks

 

1. The Logic of Causal Analysis

*  The logic of sociological analysis requires studying the process of causal investigation, highlighting the role of empirical analysis in the theoretical advancement of the discipline. Emphasis is placed on maximising impact in the presentation of research findings.

2. Identification of Causality

*  Causal identification begins with exploring types of relationships, detecting potential spurious relationships, and their analysis. The study of causality starts with contingency tables and is extended through correlation analysis.

3. Analysis of Relationships Between Multiple Variables

*  Methods for studying relationships between variables include the analysis of variance, standard and hierarchical multiple regression, and logistic regression for the study of linear additive causal models. The focus is on constructing, testing and improving models.

4. Causal Analysis

*  Analysis of complex causal models by means of path analysis, including model construction, testing, and refinement.

 

 

Official assessment of learning outcomes

 

Assessment evidence: 1. Scheduled group assignments submitted via the Virtual Campus, and scheduled group presentations of the weekly exercises. 2. Tests and brief theoretical and practical quizzes administered throughout the course. One of the tests may not be scheduled in the course calendar and another is a final integrative test to be taken on the day scheduled by the Academic Council at the end of the semester. All tests are completed using Moodle and the SPSS statistical software. All assessment tests are carried out using the required statistical software, in-person, and within the Virtual Campus environment. Any fraudulent conduct, identity impersonation, or irregular copying during these tests means students are automatically failed. Any practical assignment submitted after the deadline or not adhering to the required format cannot be assessed. All submissions must be uploaded via the Virtual Campus and in PDF format. An assessment rubric is available on the Virtual Campus for all exercises Plagiarism control and assessment. Any submission with a percentage of plagiarism greater than 50% as detected by URKUND (plagiarism-detection software) is automatically awarded a zero. Submissions with a percentage of plagiarism between 30 and 49% are deducted one point. The plagiarism regulations of the Faculty of Economics and Business are applied rigorously. For any submission, the lecturer may request oral validation. If the student does not demonstrate authorship or sufficient knowledge, the assignment will be awarded a zero. The use of AI tools is the student’s responsibility. If such tools are used, students must ensure the accuracy of the content. Any “hallucinations” or incorrect results included in an assignment will be considered the student’s responsibility; if they are not corrected, the assignment will be awarded a zero. Citing AI as an author or tool does not exempt the student from responsibility. The final grade consists of: Average of group assignments completed during the course: 40%. Average of individual tests and short quizzes completed during the course: 60%. In accordance with the UB’s regulations on assessment, students have the right to be re-evaluated on a date set by the Academic Council. This repeat assessment takes the form of the single mode of assessment for all students, regardless of the assessment opted for during the semester. No grades previously awarded can be carried forward.   Examination-based assessment Students may choose to renounce continuous assessment and opt for the single mode of assessment. This request must be made via the Virtual Campus by responding to the corresponding questionnaire, which remains open until three days before the exam set by the Academic Council. On the scheduled date, the student must take a final examination (theoretical and practical, via the Virtual Campus, in person in the rooms designated by the Faculty, and using sociological statistical software). The exam accounts for 100% of the final grade. The single mode of assessment is also eligible for repeat assessment.

Reading and study resources

Check availability in Cercabib

Book

Hayes, Andrew F. (2018) Introduction to mediation, moderation, and conditional process analysis : a regression-based approach, Nueva york, The Guilford Press

Catàleg UB  

AGRESTI, Alan. Categorical Data Analysis. Nueva York : Wiley, 2002

Catàleg UB  

ASHER, Herbert B. Causal Modeling. California : Sage, 1983

Catàleg UB  

BERRY, William Dale; FELDMAN, Stanley. Multiple regression in practice. Newbury Park (Calif.): Sage, 1985

Catàleg UB  

CEA D’ANCONA, María Angeles. Análisis Multivariable. Teoría y práctica en la investigación social. Madrid: Síntesis, 2002

Catàleg UB  

DÍEZ MEDRANO, Juan. Métodos de análisis causal. Madrid: CIS, 1992

Catàleg UB  

GUILLEN, Mauro F. Análisis de regresión múltiple. Madrid: CIS, 1992

Catàleg UB  

HELLEVIK, Ottar.  Introduction to Causal Analysis. Oslo : Norwegian University Press, 1988

Catàleg UB  

JOVELL, Albert J. Análisis de regresión logística. CIS : Madrid, 1995

Catàleg UB  

SÁNCHEZ CARRIÓN, Juan Javier. Análisis de tablas de contingencia. Madrid: CIS/Siglo XXI, 1989

Catàleg UB  

SÁNCHEZ CARRIÓN, Juan Javier (ed.) Introducción a las técnicas de análisis multivariable. Madrid: CIS, 1984

STEVENS, James Paul. Applied multivariate statistics for the social sciences. New York, Routledge, 2009

VISAUTA VINACUA, Bienvenido. Técnicas de investigación social: Modelos Causales. Barcelona: Hispano Europea, 1986

Electronic text

IBM® SPSS® Statistics 21 (2012)

IBM SPSS Advanced Statistics
IBM SPSS Categories
IBM SPSS Custom Tables
IBM SPSS Data Preparation
IBM SPSS Regression
IBM SPSS Statistics Base
IBM SPSS Statistics Brief Guide
IBM SPSS Statistics Core System User’s Guide
 En línea [consulta: 14 de juliol de 2017] Disponible a: <http://www-01.ibm.com/support/docview.wss?uid=swg27024972>