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2023/2024

Multivariable Data Analysis

Code: 101148 ECTS Credits: 6
Degree Type Year Semester
2500262 Sociology OB 3 1

Contact

Name:
Jose Pedro Lopez Roldan
Email:
pedro.lopez.roldan@uab.cat

Teaching groups languages

You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.


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.

 


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

  1. Defining concepts of analysis.
  2. Developing critical thinking and reasoning and communicating them effectively both in your own and other languages.
  3. Developing self-learning strategies.
  4. Explaining the methodological basis of these quantitative and qualitative methods and techniques.
  5. Formulating a hypothesis with these concepts.
  6. Identifying the main quantitative and qualitative methods and techniques.
  7. Indicating their dimensions, their possible quantitative indicators and the significant qualitative evidence in order to empirically observe them.
  8. Measuring a social phenomenon with these instruments on the basis of a theoretical framework of analysis.
  9. Mentioning the main concepts of sociology.
  10. Obtaining conclusions from the information obtained with this tool.
  11. Preparing an analytical tool that is significant to this hypothesis.
  12. Relating them with the different approaches of sociology.
  13. Searching for documentary sources starting from concepts.
  14. Students must be capable of assessing the quality of their own work.
  15. 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.
  16. Using the advanced multivariate statistical tools.
  17. Using the appropriate software to the advanced multivariate statistical tools.
  18. Using the appropriate software to the basic multivariate statistical tools.
  19. Using the appropriate software to the univariate statistical tools.
  20. Using the basic multivariate statistical tools.
  21. Using the univariate statistical tools.
  22. 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.

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.


Activities

Title Hours ECTS Learning Outcomes
Type: Directed      
Classroom practices 16 0.64 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
Type: Supervised      
Programmed individual tutorials 2 0.08 14, 13, 1, 11, 5, 15, 8, 10, 22, 18, 20, 16
Type: Autonomous      
Individual work 30 1.2 13, 1, 11, 5, 15, 8, 10, 22, 18, 20, 16
Readings 30 1.2 4, 6, 8, 18, 20, 16

Assessment

The course is evaluated continuously. Regular attendance at class sessions is important to ensure proper learning and assessment.

A final weighted average score of assessment activities equal to or greater than 5 out of 10 is required.

In the evaluation, three aspects are combined:

1) The analysis works. 2 individual works of sociological analysis of quantitative data from a database chosen by the students with the help of software, and in relation to the topics of:

1. Analysis of multidimensional and log-linear contingency tables
a) Selection of data and formulation of hypotheses (5%)
b) Full analysis work (45%)

2. Typological analysis combining factor analysis and classification
a) Selection of data and formulation of hypotheses (5%)
b) Full analysis work (45%)

- A minimum grade of 5 out of 10 is required for each work.
- They will be prepared in content and form as an academic research article according to a defined format and with a maximum length of 3,500 words of writing, attached separately.
- Failure to submit assignments or late submission without justification will result in the subject being abandoned.
- Assignments with a grade lower than 5 can be recovered at any time before January 31. Recovery will be scored out of a maximum of 6.
- People who present a work that is a copy, even if it is partial, of another will have a suspension from the subject.

2) The practical exercises. There will be 6 practice sessions that will consist of carrying out in the computer room exercises to apply the techniques of data analysis treated:

    Statistical software
    Analysis of multidimensional contingency tables and log-linear
    Analysis of variance
    Regression analysis
    Factorial analysis of principal components and multiple correspondences
    Cluster analysis

The evaluation will consist of the participation in the sessions during the 2 hours of class, and up to 0.6 points can be added to the final mark of the subject.

3) In addition, if the subject is followed by more than 75% of the activities proposed each day of class, it will be possible to add up to 0.6 point on the final grade.

This subject does not provide for the single assessment system.


Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Analysis works 100,0% 0 0 14, 13, 1, 3, 2, 11, 9, 4, 5, 15, 6, 7, 8, 10, 12, 22, 17, 18, 19, 20, 16, 21
Practical exercices 0,0% 12 0.48 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:

Aldas, J.; Uriel, E. (2017). Análisis multivariante aplicado con R (2.ª ed.). 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.

Brown, B. L.; Hendrix, S. B.; Hedges, D. W.; Smith, T. B. (2011). Multivariate analysis for the biobehavioral and social sciences. Agraphical approach. Hoboken: John Wiley & Sons.

Cea d’Ancona, M. A. (2002/2014). Anàlisis multivariable. Teoría y práctica en la investigación social. Madrid: Síntesis.

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.

Everitt, B.; Hothorn, T. (2011). An introduction to applied multivariate analysis with R. New York: Springer.

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.

Hahs-Vaughn, D. L. (2017). Applied multivariate statistical concepts. Nueva York: Routledge.

Hair, J. F., Black, W. C.; Babin, B. J.; Anderson, R. E. (2013). Multivariate data analysis. Pearson new international edition (7.ª ed.). Harlow: Pearson.

Hernández Encinas, L. (2001). Técnicas de taxonomía numérica. Madrid: La Muralla.

Harlow, L. L. (2014). The essence of multivariate thinking. Basic themes and methods (2.ª ed.). Nueva York: Routledge.

Joaristi Olariaga, L.; Lizasoain Hernandez, L. (1999). Análisis de correspondencias. Madrid: La Muralla.

Lévy Mangin, J. P.; Varela Mallou, J. (2003/2008) Análisis multivariables para las ciencias sociales. Madrid. Pearson-Prentice Hall.

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.

MacFarland, T. W. (2012). Two-Way Analysis of Variance: Statistical Tests and Graphics Using R. New York: Springer.

Marradi, A. (1990). Classification, typology, taxonomy. Quality & Quantity, 24, 129-157.

Mateos-Aparicio, G.; Hernandez Estrada, A. (2021). Analisis multivariante de datos: Cómo buscar patrones de comportamiento en Big Data. Madrid: Pirámide.

Meneses, J. (2019). Introducción al análisis multivariante. Barcelona: UOC

Miller, J. E. (2013). The Chicago guide to writing about multivariate analysis (2.ª ed.). Chicago: The University of Chicago Press.

Pituch, K. A.; Stevens, J. P. (2016). Applied multivariate statistics for the social sciences (6.ª ed.). Nueva York: Routledge.

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.

Tabachnick, B. G.; Fidell, L. S. (2019). Using multivariate statistics (7.ª ed.). Nueva York: Pearson.

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

 


Software

The course will use the IBM SPSS Statistics software for statistical data analysis.

In addition, Moodle, MS-Office (Word, Excel) and Adobe Acrobat will be used.