ECTS credits ECTS credits: 4.5
ECTS Hours Rules/Memories Student's work ECTS: 74.2 Hours of tutorials: 2.25 Expository Class: 18 Interactive Classroom: 18 Total: 112.45
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Social, Basic and Methodological Psychology
Areas: Behavioural Science Methodology
Center Faculty of Psychology
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable
Course Objectives
Thanks to the advances in computer science in recent decades, researchers have been able to go beyond the traditional univariate approach to data analysis, and progressively incorporate multivariate techniques into our daily work. Together with the rest of the subjects in the area of Methodology, this course aims to contribute to the training of students, allowing them to optimise the analysis and interpretation of large data sets. It is also clear that nowadays Multivariate Analysis techniques form a basic body of knowledge for any professional who analyses quantitative information. Therefore, starting with a brief review of the fundamentals of data analysis, we will try to describe the range of multivariate techniques available to you, their conditions of use and applications, with emphasis on those that may be of greater applied interest. We will also give an important role to the procedures that allow the researcher to make an adequate examination or exploratory analysis of the data, prior to the application of the different statistical techniques.
OBJECTIVES
With regard to the specific objectives of the course Multivariate Models in Psychology, there are basically eight:
To review the concepts of Statistics and Data Analysis and their contribution to Psychology.
To define the Data Process and to contextualize it within the general research process and the scientific method.
Emphasize the idea that there are different levels of analysis or ways to approach empirical reality, placing multivariate analysis and statistical modeling in the right place.
To define both concepts (multivariate analysis and statistical modelling) as two meanings of the same tool and to justify the relevance that it currently has in social research.
To make an integrated summary of the range of existing techniques (from those considered classical to the most innovative), as well as an attempt to classify them, so that the student can have an overall view and understand their main similarities and differences.
6.To characterize in a clear and concrete way each one of the techniques approached, emphasizing their usefulness, application conditions and mathematical foundations.
Familiarize the student with the use of computer tools that allow him/her to apply these techniques in the resolution of specific problems in the field of psychology or, more broadly, in the social sciences or health.
A final objective, closely related to the approach of exploratory data analysis that has fortunately become fashionable in recent years, is to transmit to the student the importance of the quality of the data itself, as well as the need for previous study and the possibility of transforming them to adapt their mathematical properties to the demands of the multivariate techniques to be used. All this with the implications it has on the correct application of the analysis techniques and, consequently, on the results of the research. In short, under this approach, we intend to decisively integrate the subject matter that concerns us, within the different methodological contents that have been presented throughout the course of the course, emphasizing the importance of the methodology itself, as an inseparable whole.
Contents
In accordance with the approach that is intended to be given to the subject, the selected topics are organized into three large blocks. The first of them is an introductory block of two topics, which aims to: (1) contextualize the subject within the study plan and, more specifically, within the contents of the Behavioral Sciences Methodology area. (2) justify its relevance within social research. (3) review and/or update some conceptual foundations so that the student can assimilate the contents of the subject. (4) propose a definition, classification and characterization of the multivariate techniques in order to allow the student to have an overall idea and, at the same time, be able to clearly differentiate between the main techniques. (5) make the student aware of the importance, in any empirical investigation, of always carrying out an exploratory or preliminary analysis of the data, for which we will use the statistical package SPSS. In the second block, some of the classic techniques known as dependency techniques will be exposed at a certain level of depth. More specifically, multiple linear regression analysis, logistic regression analysis, survival analysis, and conjoint analysis. Finally, the third block will consist of 5 practical cases that will have to be solved through the use of SPSS, during the interactive classes. The teaching of Multivariate Analysis in Psychology takes place in the second quarter of the 2nd year of the Degree. Its total workload is 4.5 credits (2.5 theory and 2 practice). Thus, 45 hours are available to teach the selected contents.
BLOCK SUBJECT TITLE
I 1 Contextualization and general review of the Multivariate Techniques
I 2 Preliminary analysis of the data
II 3 Multiple Linear Regression Analysis
II 4 Logistic Regression Analysis
II 5 Survival Analysis
II 6 Joint Analysis
BLOCK SUBJECT TOTAL PRACTICAL THEORY
I 1 5 - 5
I 2 4 4 8
Subtotal Block I 9 2 11
II 3 4 4 8
II 4 4 4 8
II 5 4 4 8
II 6 4 4 8
TOTAL 25 20 45
BASIC
The basic bibliography will consist of three manuals:
Rial, A. and Varela, J. (2008). Practical Statistics for Health Science Research. A Coruña: Netbiblo.
Picón, E.; Varela, J. and Braña, T. (2006). Joint analysis. Madrid: La Muralla
Rial, A.; Varela, J. and Rojas, A. (2001). Preliminary Data Cleansing and Analysis in SPSS. Madrid: Ra-Ma.
COMPLEMENTARY
Ato, J., López and Benavente, A. (2013). A classification system for research designs in Psychology. Annals of Psychology, vol. 29, nº3. 2013
Catena, A., Ramos, M. and Trujillo, H. (2003). Multivariate analysis. A manual for researchers. Madrid: Biblioteca Nueva. Cea, M.A. (2002). Análisis multivariable. Teoría y práctica en la investigación social. Madrid: Editorial Síntesis.
Díaz de Rada, V. (2002). Técnicas de Análisis Multivariante para Investigación Social y Comercial. Madrid: Ra-Ma.
Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (2000). Multivariate Analysis. Madrid: Prentice Hall.
Lévy, J.P. and Varela, J. (2003). Análisis Multivariable para las Ciencias Sociales (Multivariate Analysis for the Social Sciences). Madrid: Prentice Hall.
Martínez Árias, R. (1999). Multivariate analysis in scientific research. Madrid: La Muralla.
Pardo, A. and Ruíz, M.A. (2002). SPSS11. Quick guide to data analysis. Madrid: McGraw-Hill.
Peña, D. (2002). Analysis of multivariate data. Madrid. McGraw-Hill.
Visauta, B. and Martori, J.C. (2003). Statistical analysis with SPSS for Windows (vol. II). Madrid: McGraw-Hill.
Competence
To know the research methods and designs as well as the different procedures for data analysis in Psychology.
To be able to identify the most relevant traits of individuals, groups, organizations and contexts by using appropriate psychological techniques and instruments.
To promote health and life quality through professional psychological methods in groups, communities and organizations in the different fields of Psychology: educational, clinical and health, work and organizational, group and community contexts.
To be able to select and apply adequate and specific psychological intervention procedures and instruments.
To be able to set goals and to plan intervention procedures according to recipients’ needs and demands and to be able to assess intervention results.
To elaborate psychological reports addressed to professionals and other recipients in the different professional fields.
To conform with the deontological duties of Psychology.
Teaching methodology
The general scheme to be followed in the theoretical sessions, where each of the selected multivariate techniques is addressed individually, is as follows:
In the first instance, the student is presented with a real research problem for which he must suggest which technique or techniques can be applied, always taking into account the scheme presented in the introductory block that addresses both the objectives of the research, as well as the number and level of measurement of the intervening variables. Further details of the specific investigation or problem, as well as the procedure followed for its resolution, are provided below. The ideal is to resort to research carried out from the area of knowledge itself. In this way, it is easier for the teacher to illustrate each technique and resolve particular doubts about the research in which it was applied. This last aspect is of great importance if we want to highlight the idea of data processing as a continuum within all research. One of our concerns is to emphasize the methodological rather than the purely statistical, so that the student understands the importance of each of the different phases of the investigation, beyond the statistical-computer analysis of a specific data set.
Secondly, and always referring to the example presented, the definition of the technique, its general objective and particular uses, as well as its application conditions, will be reviewed. This section will close by urging students to put new examples where they consider the use of the technique in question pertinent.
Next, the mathematical foundations of the technique and the phases established in its application are addressed.
Fourth, it shows in a very general way how it is implemented in SPSS (how to do it in SPSS), the main menus, dialog boxes and options, as well as the most relevant aspects of the results tables. It should be noted that this part is still integrated into what would be the theoretical classes, but that it can be carried out because the classrooms have a PC and a video camera.
Ultimately, students are proposed to solve a practical case in the data processing laboratory. This last section would already configure what the practical classes are. It should also be noted that at the end of each topic the student is usually asked to read (on a voluntary basis) a research article in which the technique in question has been the main data analysis tool. In this sense, although in the bibliography of the subject exclusively manuals and monographs are used, in the case of the recommended readings the intention is to establish a bridge with the practical classes, providing readings in which the use of the technique is perfectly illustrated. in specific investigations. An additional benefit that is expected to be derived from this strategy is that the student definitely makes contact with other documentary sources, such as specialized magazines.
While the duration of the theoretical classes is one hour, the practical one is two hours. As its own name indicates, they allow students to consolidate through practice the knowledge acquired in theoretical classes. The objective is none other than learning the keys to carry out a correct application of the different multivariate techniques included in the program. To do this, real research problems and data files (as far as possible) are used. The questions that the students must answer, during each practice, are the usual questions that the researcher must solve when using a multivariate technique, from the preparation and previous analysis of the data, the verification of certain assumptions, the reasoned choice of certain analysis options, the correct interpretation of the results and the preparation of the report.
The students have the material corresponding to each practice on the Virtual Campus, in which a brief description of it is made, the objectives, the data file to be used and the variables it consists of, as well as the different questions that must be resolved. , covering the sections, tables or tables indicated for it.
Although in the practical classes the emphasis is placed on the applied aspect of each technique and, therefore, it is the student who must work with the program during the two hours, it is appropriate to begin each session with a brief contextualization of the technique, refreshing important concepts and insisting on the most relevant aspects of interpretation. Although it must be the student who, individually, must become aware of the importance of acquiring sufficient skills to solve on his own the analyzes that are presented to him, he is encouraged to discuss the results with his classmates and to ask the teacher any kind of doubt.
Being able to have adequate support materials is essential for both the teacher and the student to achieve their objectives. Within what is understood as "support material" several types could be distinguished. The first of them is the bibliographic material. Although its existence has greatly facilitated the work of teachers (since in general they start from an applied approach of multivariate analysis), we must also say that, on occasions and when it comes to addressing certain topics, the student still does not have the sufficient preparation to assimilate the contents easily or, simply, the mathematical complexity that they entail is excessively high. In this sense, it is the teacher's responsibility to be sensitive to the bibliographic novelties that are appearing regarding the subject they teach, in order to select and recommend the references or specific chapters that best suit the teaching needs. Within the bibliographic material, it is also convenient that the consultation and reading of specialized magazines be incorporated into the student's daily work, facilitating access to articles of an applied nature, which serve to illustrate the use of multivariate techniques in solving specific problems of investigation. The different recommended readings are discussed in class, encouraging students to point out those aspects that have most attracted their attention, as well as to raise doubts and suggestions.
The second of the support materials is made up of the audiovisual media that the teacher himself uses in class. In this sense, the use of Powerpoint has become widespread in recent years. The fact that in all the classrooms of our faculty there is a PC and a video camera, makes it extremely comfortable for teachers to make use of this strategy. Although it is tremendously useful, audiovisual support should not become an end in itself, focusing the student's attention and distracting him from the true contents of the subject. For this, the slides must be simple, with clear, synthetic content and as graphic as possible. At no time should they replace the explanations and reflections of the teacher. They should simply be a reference script, which serves to mark the milestones or key elements on which the theoretical argument should be based. At the same time, they are very useful to engage or focus the student's attention on relevant content that must be marked, in the literal sense of the word.
The third of the support materials to which we want to refer is made up of the classic notes of the subject. From our experience, we consider it important to prepare a basic didactic material, available to the student even before receiving the theoretical classes and on which he can (during the course of these) incorporate his annotations, clarifications or comments, undoubtedly interesting for Prepare the evaluation of the subject. Our particular experience is that of using the Powerpoint slides themselves (printed as notes pages), so that the student can have them in the faculty's photocopier and take notes on them throughout the class, wit hout worrying about copy everything on the slide, not everything the teacher says. From the first day of class, the student is insisted that topic by topic, week by week, he will have in advance the material that will be used in class, so that during the class he must focus his attention on trying to understand the content that is intended to be transmitted, raising any doubts and concerns that it deems appropriate and incorporating its own comments and clarifications to the notes pages.
As a fourth support element, reference should be made to the possibility of executing certain analyzes with SPSS, in order to better illustrate some content. At certain times (and without necessarily being foreseen in the script) it may be interesting to open an example data file, to show the specific appearance of the input data (for the application of a certain technique), or to comment with a real case the main results that it provides. Obviously, although the practical classes are basically focused on the execution through SPSS of different techniques, in the theoretical sessions it may also be appropriate to make use of the program.
Lastly, we must not forget that more traditional strategies such as the use of the blackboard, markers, transparencies, as well as group work, continue to be of great help to invigorate teaching and consolidate knowledge. Our experience has given us evidence of how productive (and even fun) it is to invest a class at the end of the course, asking students to draw a boxplot, a dendrogram, a scatterplot or a positioning map on a transparency, and Illustrate with examples of your own harvest and using the overhead projector, what each of them is for.
FINAL EXAM (THEORETICAL-PRACTICAL):
The exam will consist of 75 multiple choice questions with 4 answer alternatives, where only one is correct. Of these 75 questions, the first 45 refer to the six topics seen in the lectures; and the next 30 will be about the five practices or interactive classes. All questions have the same weight when correcting the exam. The correction formula is = 8 x (Hits – (Failures/4-1))/Number of Questions
An error in the answer is considered the same as an omission (non-response), therefore it is recommended to answer all the questions even if it is random.
Applying the following formula = 8 x (Hits – (Failures/4-1))/75 we will know what grade we get out of a maximum of 8 points. Specifically, if we have 47 correct answers, this corresponds to the passing grade with a 4 out of a maximum of 8, 54 correct answers is equivalent to 5 out of 8, 61 correct answers is equivalent to 6 out of 8, 68 correct answers is equivalent to 7 out of 8 and, finally, 75 hits at 8 out of 8.
Remember that the empirical work supposes up to a maximum of 2 points out of 10, so in this exam the maximum grade is 8.
DELIVERY COMPLEMENTARY EMPIRICAL WORK:
Up to a maximum of 2 points for the empirical Research Work to be carried out during the academic year. For more information and details, see the attached file posted on the virtual campus entitled: "Multivariate Analysis coursework.pdf". However, in the first days of class, all kinds of instructions will be given about this work.
Obviously, the FINAL GRADE of the subject will be out of 10. This is reached with the 8 points of the final exam + 2 points of the empirical work
IMPORTANT:
In order to pass the subject, it is mandatory to have attended 80% of the interactive classes and to deliver, at the end of it, the corresponding practical report. These practices are available on the Virtual Campus. They must be printed and brought to each of the 5 interactive classes.
Based on academic criteria, it is considered that this subject cannot be taken with a waiver
Personal work and study time
Below are the hours dedicated to study that the student must dedicate at least on a personal level, independently of the theoretical and practical classes.
BLOCK SUBJECT TITLE
I 1 General review of multivariate techniques: 20 hours
I 2 Preliminary analysis of data: 20 hours
II 3 Multiple Linear Regression Analysis: 10 hours
II 4 Logistic Regression Analysis: 10 hours
II 4 Survival Analysis: 10 hours
II 6 Joint Analysis: 10 hours
COMPLEMENTARY READINGS: 10 hours
TOTAL: 90 HOURS
Regular attendance at theoretical and practical sessions, delivery of the sheets relating to the 5 practices or interactive classes, completion of the recommended complementary readings and participation in the class dynamics.
Tutorial hours: Monday, Tuesday and Wednesday from 10-12h.
Based on academic criteria, it is considered that this subject cannot be studied with a waiver
Antonio Rial Boubeta
- Department
- Social, Basic and Methodological Psychology
- Area
- Behavioural Science Methodology
- Phone
- 881813912
- antonio.rial.boubeta [at] usc.es
- Category
- Professor: University Lecturer
Jesus Varela Mallou
Coordinador/a- Department
- Social, Basic and Methodological Psychology
- Area
- Behavioural Science Methodology
- Phone
- 881813706
- jesus.varela.mallou [at] usc.es
- Category
- Professor: University Professor
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| 13:00-14:00 | Grupo /CLE_02 | Spanish | Classroom 6 |
| 14:00-15:00 | Grupo /CLE_01 | Spanish | Classroom 5 |
| Wednesday | |||
| 12:00-13:00 | Grupo /CLE_02 | Spanish | Classroom 6 |
| 13:00-14:00 | Grupo /CLE_01 | Spanish | Classroom 5 |
| 05.26.2025 12:30-15:00 | Grupo /CLE_01 | Classroom 3 |
| 05.26.2025 12:30-15:00 | Grupo /CLE_02 | Classroom 3 |
| 05.26.2025 12:30-15:00 | Grupo /CLE_01 | Classroom 5 |
| 05.26.2025 12:30-15:00 | Grupo /CLE_02 | Classroom 5 |
| 05.26.2025 12:30-15:00 | Grupo /CLE_01 | Classroom 6 |
| 05.26.2025 12:30-15:00 | Grupo /CLE_02 | Classroom 6 |
| 05.26.2025 12:30-15:00 | Grupo /CLE_01 | Classroom 7 |
| 05.26.2025 12:30-15:00 | Grupo /CLE_02 | Classroom 7 |
| 06.30.2025 12:30-15:00 | Grupo /CLE_02 | Classroom 3 |
| 06.30.2025 12:30-15:00 | Grupo /CLE_01 | Classroom 3 |
| 06.30.2025 12:30-15:00 | Grupo /CLE_02 | Classroom 6 |
| 06.30.2025 12:30-15:00 | Grupo /CLE_01 | Classroom 6 |
| 06.30.2025 12:30-15:00 | Grupo /CLE_01 | Classroom 7 |
| 06.30.2025 12:30-15:00 | Grupo /CLE_02 | Classroom 7 |