Quantitative Social Research Methods I, Statistics I, Statistics II.

At the end of the course, students must be proficient in the following tasks: 1. Analyzing the different techniques, as well as their relevance and limitations when solving problems of a contrasting nature. 2. Applying advanced techniques with rigor and sophistication for data analysis. 3. Interpreting the analyses and identifying the most relevant results. 4. Reporting the results correctly (good design of tables, graphs, etc.). In addition, the student must have: 5. Basic knowledge of the statistical package Stata/R

K5: To know the basic components of cultural differences and social inequalities. K6: Know and value the economic, temporal and human resources available for sociological research. K8: Identify the objectives of social and political analysis as well as the populations subject to social research K9: Understand and synthesize the plurality of approaches and concepts of the subdisciplines linked to sociology. S1: To know of basic humanistic contents, oral and written expression, following ethical principles and completing a multidisciplinary training profile. S2: Use information interpreting relevant data avoiding plagiarism, and in accordance with the academic and professional conventions of the area of study, being able to assess the reliability and quality of such information. S3: Identify and apply interpersonal skills on responsibility, negotiation and emotional intelligence. S4: Demonstrate good communication and ability to work in multidisciplinary and international environments. S5: Develop personal autonomy in the work and professional sphere in order to identify one's own learning needs. S6: Compose and write speeches following a logical order, providing accurate information and in accordance with the different established standards. S7: Apply the knowledge acquired to identify the sociological perspective in the analysis of social, political and economic processes. S8: Know the tools and instruments of application in the field of sociological analysis in the company S9: Be able to formulate, debate and defend critical reasoning, using precise terminology of the discipline and methodologies of the discipline. S10: Be able to manage, identify, gather and interpret relevant information on the economic, political and social field of teaching and research. S11: Knowledge of quantitative and qualitative research techniques, and the ability to discern the most appropriate ones to apply in the specific field of the discipline. C1: Know and be able to handle interpersonal skills on initiative, responsibility, conflict resolution, negotiation, etc., which are required in a professional environment. C2: Know and apply sampling and field work techniques for the different fields of sociological research C3: Possess skills and abilities in developing, using, and interpreting social indicators and social measurement instruments

Quantitative research techniques are a key element in the training of future professionals, who will need to obtain, manage, and analyze data in their respective careers. 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/R. The course is structured in 3 large blocks, each composed of different themes: 1. Inferential exploratory techniques: 1.1. Factor Analysis: This is a technique to reduce the dimensionality of data, and aims to find a minimum of dimensions with which to explain as much information as possible. 1.2. Cluster Analysis: This is a multivariate statistical technique that groups elements to achieve maximum homogeneity in each group, with the largest difference being between groups. In the course, we address two clustering strategies: 1.2.1. Clustering using partitioning algorithms. 1.2.2. Clustering using hierarchical algorithms. 2. Advanced Multivariate statistical techniques: 2.1. Logistic regression: This is a multivariate research technique, in which the main objective is to model how certain variables influence the probability of the occurrence of an event (dichotomous dependent variable). 2.2. Multinomial logistic regression: This is an extension of logistic regression for cases where the dependent variable is of a polytomous nature. 2.3. Introduction to Multilevel Analysis: Multilevel models are an extension of classical linear regression models, suitable for processing hierarchical data. 2.4. Introduction to time series: A time series is a sequence of observations ordered in time or space. 3. Treatment of Lost Cases In this part of the course, we will address alternative strategies for the treatment of missing data: 3.1. Simple methods: deletion methods, single imputation. 3.1. Multiple Imputation Methods.

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.