Checking date: 04/06/2021

Course: 2021/2022

Quantitative social research methods
Study: Bachelor in Sociology (208)

Coordinating teacher: TORRE FERNANDEZ, MARGARITA

Department assigned to the subject: Department of Social Sciences

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Basic knowledge of statistics
At the end of the course, students must be proficient in the following tasks: 1) operationalization of research hypotheses 2) handling and preparation of data 3) use of the main quantitative techniques in social research: a. Selecting the most appropriate technique for each type of research question and data b. Data Analysis c. Interpretation of the analyses 4) a working knowledge of Stata/R/SPSS and basic programming skills
Skills and learning outcomes
Description of contents: programme
Quantitative research techniques are a key element in the training of future professionals. This course delves into the learning of quantitative social research techniques from an applied perspective. All topics will be approached in a theoretical/practical way, using the statistical package Stata The course is structured as follows: 1. Introduction to quantitative social research 2. Descriptive Analysis 3. Bivariate analysis 4. Multivariate Analysis: 4.1. Linear Regression 4.2. Logistic Regression 5. VisualizaciĆ³n and reporting
Learning activities and methodology
Master Classes (3 ECTS credits): Lecture on the theoretical content of the subject. Reduce Classes (3 ECTS credits): Practical classes in the computer room using Stata/R/SPSS
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Calendar of Continuous assessment
Basic Bibliography
  • Cameron, Colin A. & Pravin K. Trivedi. Microeconometrics using Stata. Stata Press. 2010
  • Long, Scott J. & Jeremy Freese. Regression Models for Categorical Dependent Variables Using Stata. Stata Press. 2014
Additional Bibliography
  • James, Gareth, Daniel Witten, Trevor Hastie, & Robert Tibshirani. An introduction to Statistical Learning with applications in R. Spinger. 2013
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.