Multivariable Data Analysis
Code: 101148 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2500262 Sociology | OB | 3 | 1 |
Contact
- Name:
- Pedro López Roldán
- Email:
- Pedro.Lopez.Roldan@uab.cat
Use of Languages
- Principal working language:
- catalan (cat)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- Yes
- Some groups entirely in Spanish:
- No
Teachers
- Ancor Mesa Mendez
Prerequisites
In order to be able to take this course, it is advisable to have successfully followed up the Quantitative Methods of Social Research and Analysis Methods.
Objectives and Contextualisation
This is an introductory course to the techniques of multivariate statistical data analysis that is proposed as a continuation of the quantitative perspective of social research initiated in the degree. The procedures, methods and techniques already discussed so far will be expanded to consider what we can generally call the transition from bivariate analysis procedures to multivariate analysis procedures.
In the context of the itinerary of technical and methodological subjects, which seek to offer a complete overview of the different procedures of the sociological scientific activity, and given the extension and variety of the analysis procedures in the field of social sciences, it entails directing the teaching towards the selection of a few topics or instruments considered as some of the most fundamental and of greatest interest in the practice of sociological research.
Specifically, the subject aims to:
1) From the point of view of the students, the construction of their learning will be carried out from:
- Knowledge and understanding of the main concepts associated with the multivariate analysis of statistical data, exemplified by sociological concepts.
- The ability to apply technical instruments for the advanced analysis of statistical data considered in the course.
- Know how to use statistical software for statistical analysis bivariate and multivariable.
- Know how to interpret the statistical results of a data analysis from the technical and substantive point of view according to some knowledge and study objectives of the social reality.
2) From the general conditions of a subject of this type in relation to the use of students it is about:
- Facilitate the understanding, management and interpretation of a basic algebraic and statistical conceptual system to assimilate the use of techniques that involve the quantification and formalization of social phenomena.
- Framing in a balanced, comprehensive and integratingway the contents ofthis subject within the set of the usual methods in sociology.
Given the exceptional situation arising from the Covid-19 pandemic and the uncertainty of the health situation for the next academic year, the teaching dynamics and evaluation criteria may be adapted to the needs of each moment. This Teaching Guide collects the usual information with face-to-face teaching. In the case of having to organize teaching and learning differently, in particular, combining face-to-face and online formats, the Teaching Guide will be modified in accordance with the guidelines given by the Faculty and the University.
Competences
- Applying the main quantitative and qualitative methods and techniques of social research to a specific topic.
- Describing social phenomena in a theoretically relevant way, bearing in mind the complexity of the involved factors, its causes and its effects.
- Designing a social research project by defining a comprehensive theoretical framework with clearly defined concepts, formulating consistent and significant hypothesis, choosing suitable investigation techniques for the adopted concepts, and analysing the empirical results obtained with those techniques.
- Developing critical thinking and reasoning and communicating them effectively both in your own and other languages.
- Developing self-learning strategies.
- Enumerating the methodology and investigation techniques that support the main hypothesis about social relationships, the positions and practices of individuals in a social structure and the social changes.
- Searching for documentary sources starting from concepts.
- Students must be capable of assessing the quality of their own work.
- Students must be capable of managing their own time, planning their own study, managing the relationship with their tutor or adviser, as well as setting and meeting deadlines for a work project.
- Working in teams and networking in different situations.
Learning Outcomes
- Defining concepts of analysis.
- Developing critical thinking and reasoning and communicating them effectively both in your own and other languages.
- Developing self-learning strategies.
- Explaining the methodological basis of these quantitative and qualitative methods and techniques.
- Formulating a hypothesis with these concepts.
- Identifying the main quantitative and qualitative methods and techniques.
- Indicating their dimensions, their possible quantitative indicators and the significant qualitative evidence in order to empirically observe them.
- Measuring a social phenomenon with these instruments on the basis of a theoretical framework of analysis.
- Mentioning the main concepts of sociology.
- Obtaining conclusions from the information obtained with this tool.
- Preparing an analytical tool that is significant to this hypothesis.
- Relating them with the different approaches of sociology.
- Searching for documentary sources starting from concepts.
- Students must be capable of assessing the quality of their own work.
- Students must be capable of managing their own time, planning their own study, managing the relationship with their tutor or adviser, as well as setting and meeting deadlines for a work project.
- Using the advanced multivariate statistical tools.
- Using the appropriate software to the advanced multivariate statistical tools.
- Using the appropriate software to the basic multivariate statistical tools.
- Using the appropriate software to the univariate statistical tools.
- Using the basic multivariate statistical tools.
- Using the univariate statistical tools.
- Working in teams and networking in different situations.
Content
General introduction
- Objectives of the subject, contents, course dynamics and evaluation
- Multivariate analysis: characteristics and classification of techniques
- Software for the analysis of statistical data
PART I. Analysis of interdependence with qualitative variables
Unit 1. Analysis of contingency tables
- Classic analysis of multidimensional contingency tables
Unit 2. Log-linear analysis
- General linear logarithmic analysis
PART II. The dependence analysis
Unit 3. Analysis of variance
- One-way analysis of variance
- Analysis of multivariate variance
Unit 4. Regression analysis
- Simple regression analysis
- Multiple regression analysis
PART III. The analysis of interdependence for the construction of typologies
Unit 5. Factor analysis
- Mathematical foundations of multivariate data analysis
- Factor analysis of principal components
- Factor analysis of correspondences
Unit 6. Cluster analysis
- Cluster analysis and the construction of typologies
- Automatic cluster analysis
Methodology
The course is presented with a continuous dynamic of teaching and learning, which implies tracking the rhythms of the course and the various contents that have been designed in accordance with the different scheduled activities. The contents of each unit have a thread linked to the research process and the continuity of the learning of concepts and instruments that are incorporated progressively, as well as the resolution of problems and questions, which are based in the assimilation and practice of each previous topic of each unit.
Since the objective of the training is that students learn to research sociology using advanced statistical techniques, the teaching methodology and the training activities of the subject result from the combination of expositive sessions with problem solving exercises and practices in the classroom that allow to apply the acquired concepts and explained techniques, as well as tutorials of follow-up and autonomous work.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Classroom practices | 14 | 0.56 | 1, 11, 5, 8, 10, 18, 20, 16 |
| Individual preparation of practical exercises | 30 | 1.2 | 3, 4, 8, 10, 18, 20, 16 |
| Master classes | 30 | 1.2 | 1, 11, 9, 4, 5, 6, 7, 8, 18, 20, 16 |
| Programmed group tutorials | 4 | 0.16 | 14, 1, 11, 5, 8, 22, 18, 20 |
| Type: Supervised | |||
| Programmed group tutorials | 2 | 0.08 | 14, 13, 1, 11, 5, 15, 8, 10, 22, 18, 20, 16 |
| Type: Autonomous | |||
| Readings | 30 | 1.2 | 4, 6, 8, 18, 20, 16 |
| Work in group | 30 | 1.2 | 13, 1, 11, 5, 15, 8, 10, 22, 18, 20, 16 |
Assessment
The course is evaluated continuously. The subject will be passed if the final weighted average score of the assessment activities is equal to or greater than 5 out of 10.
In the evaluation, three aspects are combined:
1) The analysis works (75%): will consist in the realization in groups of 3 people of 2 works of sociological analysis of quantitative data. The specific characteristics of the work are detailed in a specific section of the program. A minimum score of 4 out of 10 is required for each job. The works must be presented previously in a seminar and will be co-evaluated by students during the session. The contents are the following:
1. Analysis of multidimensional and log-linear contingency tables:
- Evaluation of the previous presentation of the work in the seminar (4,5%).
- Job evaluation (33%).
2. Typological analysis combining factor analysis and classification:
- Evaluation of the previous presentation of the work in the seminar (4,5%).
- Job evaluation (33%).
2) The practical exercises (15%) will consist of the individual realization in the computer room of 6 exercises of follow-up of the subject and of learning of the different techniques of data analysis taught in the subject, in which they will be applied, with the help of statistical software , the treatment of statistical data following some guidelines that will guide the exercise and on which there will be answers to several questions of a questionnaire. A final average score of 4 out of 10 is required for the set of exercises. The correspondent exercises contained in these 6 techniques:
Analysis of multidimensional contingency tables (2,5%)
Log-linear analysis (2,5%)
Analysis of variance (2,5%)
Regression analysis (2,5%)
Factorial analysis of principal components and multiple correspondences (4%)
Cluster analysis (2,5%)
3) The follow-up and participation (10%) duringthe course. Continuous attendance and participation in classroom activities is required in addition to constant independent work and compliance with the deadlines for the various activities. In particular, a minimum attendance of 80% of the sessions is required. A maximum of 1 will be scored depending on the follow-up and whether attendance is between 80 and 100%. If 80% is not reached, it will be considered that there has been no continuous assessment and abandonment of the course. Excused and documented absences will be excluded as long as the final attendance is at least 65%.
If the follow-up is not unjustifiably fulfilled, or one of the evaluation activities is not carried out, this will mean the abandonment of the course, regardless of the mark obtained in the other evaluation activities.
Compensatory evaluation
- The compensatory evaluation will be eligible only if continuous evaluation has been followed.
- To recover the works it will be necessary to comment them obligatorily in a tutorial to indicate the aspects that will have to be corrected or improved.
- If, in an exceptional and justified manner, one of the practical exercises has not been done, or the score of theseis less than 4, they can be recovered. The recovery will consist of reproducing an exercise of data analysis with the software, of the same characteristics as that carried out in the practical class adapted to other chosen data, with the results of tables and graphs and a brief comment on the interpretation of the results.
- The recovery of both the work and the exercises will be scored on a maximum of 7.
In accordance with article 117.2 of the UAB Academic Regulations, the assessment of repeat students may consist of a single synthesis test. Repeating students who want to take advantage of this possibility will need to contact the teachers at the beginning of the course. " If you want to consult the UAB Academic Regulations, you can find them here.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Analysis works | 75,0% | 0 | 0 | 14, 13, 1, 3, 2, 11, 9, 4, 5, 15, 6, 7, 8, 10, 12, 22, 17, 18, 19, 20, 16, 21 |
| Follow-up of the course | 10,0% | 0 | 0 | 1, 3, 2, 11, 9, 4, 5, 15, 7, 8, 10, 12, 17, 18, 19, 20, 16, 21 |
| Practical exercices | 15,0% | 10 | 0.4 | 14, 13, 1, 3, 2, 11, 9, 4, 5, 15, 6, 7, 8, 10, 12, 22, 17, 18, 19, 20, 16, 21 |
Bibliography
Basic bibliography
López-Roldán, P.; Fachelli, S. (2015). Metodología de la investigación social cuantitativa. Bellaterra (Barcelona): Dipòsit Digital de Documents, Universitat Autònoma de Barcelona. 1a. edición.
http://ddd.uab.cat/record/129382 | http://pagines.uab.cat/plopez/content/misc
López-Roldán, P. (2015). Recursos para la investigación social. Dipòsit Digital de Documents. Bellaterra (Barcelona): Universitat Autònoma de Barcelona.
http://ddd.uab.cat/record/89349 | http://pagines.uab.cat/plopez
Further reading
The manual Metodología de la investigación social cuantitativa (MISC) contains in each chapter a list of specific bibliographic references that complement the basic bibliography..
Selected bibliographical references:
Aldás, J.; Uriel, E. (2017). Análisis multivariante aplicado con R. Madrid: Paraninfo.
Ato García, M.; López García, J. J. (1996). Análisis estadístico para datos categóricos. Madrid: Síntesis.
Bailey, K. D. (1994). Typologies and Taxonomies. An Introduction to Classification Techniques. Thousand Oaks (California): Sage.
Bouso, J. (2013). El paquete estadístico R. Madrd: CIS.
Cea d’Ancona, M. A. (2002). Anàlisis multivariable. Teoría y pràctica en la investigación social. Madrid: Sintesis.
Christensen, R. R. (1997). Log-linear models and logistic regression. New York: Springer-Verlag.
Correa Piñero, A. D. (2002). Análisis logarítmico lineal. Madrid: La Muralla.
Greenacre, M. J. (2008). La práctica del análisis de correspondencias. Madrid: Fundación BBVA. http://www.fbbva.es/TLFU/tlfu/esp/publicaciones/libros/fichalibro/index.jsp?codigo=300
García Ferrando, M. (1987). Socioestadística. Introducción a la estadística en sociología. 2a edició amp. Madrid: Alianza. Alianza Universidad Textos, 96.
Guillén, M. F. (1992). Análisis de regresión múltiple. Madrid: Centro de Investigaciones Sociológicas.
Hernández Encinas, L. (2001). Técnicas de taxonomía numérica. Madrid: La Muralla.
Joaristi Olariaga, L.; Lizasoain Hernandez, L. (1999). Análisis de correspondencias. Madrid: La Muralla.
López-Roldán, P.; Fachelli, S. (2018). Metodología de construcción de tipologías para el análisis de la realidad social. Bellaterra (Cerdanyola del Vallès): Dipòsit Digital de Documents, Universitat Autònoma de Barcelona. 2a. edición.
Marradi, A. (1990). Classification, typology, taxonomy. Quality & Quantity, 24, 129-157.
MacFarland, T. W. (2012). Two-Way Analysis of Variance: Statistical Tests and Graphics Using R. New York: Springer.
Pardo, A; Ruiz, M. A.; San Martín, R. (2015). Análisis de datos en ciencias sociales y de la salud. Madrid: Síntesis.
Powers, D. A.; Xie, Y. (2008). Statistical Methods for Categorical Data Analysis. Bingley, U.K.: Emerald. 2a. edició.
Sánchez Carrión, J.J. (1999). Manual de análisis estadístico de los datos. Madrid: Alianza. Manuales, 055.
Sánchez Carrión, J. J. (Ed.) (1984). Introducción a las técnicas de multivariable aplicadas a las ciencias sociales. Madrid: Centro de Investigaciones Sociológicas.
Sánchez Carrión, J. J. (1989). Análisis de tablas de contingencia. El uso de los porcentajes en ciencias sociales. Madrid: Centro de Investigaciones Sociológicas-Siglo XXI.
Tejedor, F. J. (1999). Análisis de varianza: introducción conceptual y diseños básicos. Madrid: La Muralla.
VV.AA. (1996). La construcció de tipologies. Exemples. Monogràfic de Papers. Revista de Sociologia, 48. http://ddd.uab.cat/search?cc=papers&f=issue&p=02102862n48&rg=100&sf=fpage&so=a&ln=en