Degree | Type | Year |
---|---|---|
Logopedia | FB | 2 |
You can view this information at the end of this document.
The student is assumed to have knowledge about the basic concepts of research methods that are taught in the subject Introduction to scientific methodology and psychological processes.
The student is not assumed to have special knowledge of mathematics except to know the basic notions of data analysis taught in the Access to University Course and/or in secondary education in the different curricula. However, it is essential to have basic user computer knowledge
At the end of the course the student will be able to:
1. Principles of research methodology
Quantitative and qualitative methods, designs, and techniques in speech therapy research
Evidence-based practice
2. Experimental designs
Unifactorial between-subject vs. within-subject experimental designs
Factorial experimental designs
3. Quasi-experimental designs
Experiment vs. Quasiexperiment
Pre-experimental and quasi-experimental designs
4. Single case designs
5. Ex post facto" designs
6. Survey designs
7. Observational method
8. Qualitative and mixed methods
9. Data processing
Structure of a data matrix
Reading and defining variable properties
Creation of variables
Case selection
10. Data analysis
Univariate statistical description
Bivariate statistical description
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Class lessons 1/1 group | 34.5 | 1.38 | KM17, SM09, SM12, KM17 |
Class lessons 1/4 group | 10 | 0.4 | SM09, SM09 |
Type: Supervised | |||
Review of integrated problems | 5 | 0.2 | SM09, SM12, SM09 |
Tutorship | 5 | 0.2 | |
Type: Autonomous | |||
Abstracts, diagrams and conceptual maps | 11.5 | 0.46 | KM17, SM09, SM12, KM17 |
Assessment. Critical reading | 3 | 0.12 | SM12, SM12 |
Comprehensive and critical reading of materials | 36 | 1.44 | SM12, SM12 |
Tutorial-based training in software: data process and analysis | 30 | 1.2 | SM09, SM09 |
Virtual tutorials with teachers and peers | 12 | 0.48 |
On this course we propose different activities based on active learning methodologies focused on the student. In this way a "hybrid" approach is outlined in which we combine traditional didactic techniques with other resources aimed at encouraging meaningful learning.
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.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Evidencia 1 (Assessment 1). Written individual test. In-person. Contents: Foundations of Research methods. First assessment period. | 4,5 points | 1.5 | 0.06 | KM17, SM12 |
Evidencia 2 (Assessment 2). Individual written test. Moodle. Contents: Data processing and analysis. Second assesment period. | 5 points | 1.5 | 0.06 | KM17, SM09 |
Evidencia 3 (Assessment 3). Practical exercise. Individual, written, virtual. Foundations of Research methods. First assessment period. | 0,5 points | 0 | 0 | SM12 |
Below, we indicate the learning tests to be taken by the students, both in the case of continuous assessment (CA) and single assessment (SA):
Evidences
Type 1
• Assessment 1 (Ev1; CA: 1st assessment period; SA: 2nd assessment period).
• Assessment 2 (Ev2; CA & SA: 2nd assessment period).
Exceptionally, students who do not attend one of these evidences (Ev1 or Ev2) due to compelling circumstances may be allowed to provide the missing evidence during the reassessment week. They must provide documentary proof of the circumstances that justify their absence, and the decision on whether they are allowed to reset the examination will be taken by the teaching team.
Type 2
• Assessment 3 (Ev3; CA: 1st assessment period; SA: 2nd assessment period). This activity is designed to set the pace of work, to consolidate concepts in a practical way and to generate doubts before taking Ev1.
Feedback
Feedback |
Evidence |
Week |
Digital |
Ev2 Ev3 AU |
19 Week before the 1st assessment period 19 |
Classroom |
Ev1 |
Week after the 1st assessment period |
Use of Artificial Intelligence (AI): In this subject, the use of Artificial Intelligence (AI) technologies is not allowed in any of its phases. Any work that includes fragments generated with AIwill be considered a lack of academic honesty and may lead to a partial or total penalty in the grade of the activity, or greater sanctions in serious cases.
Definition of evaluable student
A student is considered evaluable when he/she has submitted evidence of learning with a weight equal to or greater than 4,0 points.
Definition of passing grade
A student has passed the course when he/she meets the following two conditions:
a) He/she has obtained a minimum score of 5,0 points.
b) In each of the type 1 evidence (Ev1, Ev2) he/she has obtained a minimum score of 3,0 points out of 10. In case of not reaching these requirements, the maximum score to be recorded on the student’s academic transcript ("actas") will be 4,8 points.
Reassessment
On the date set by the Faculty, type 1 evidences will be reassessed, by means of a theoretical-practical test of individual authorship. The following two conditions must be met for students to be eligible for reassessment:
a) Not reaching the criteria established to pass the subject, but achieving a final grade of at least 3,5 points
b) Have submittedevidence with a weight equal to or greater than 2/3 of the total grade.
The grade of the evidence reassessed will be Pass if the score is greater than or equal to 5,0 points.
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Single assessment (SA)
- Individual written test of the FDI block (Ev1; SA: 2nd assessment period).
- Individual written test of the AD block (Ev2; SA: 2nd assessment period).
- Evidence 3. Practical exercise of the FDI block (Ev3; SA: 2nd assessment period).
In the case of the SA, the Ev1 and Ev2 evidences will be done on the same day and in the same place as the evidence of the 2nd evaluation period and will have the same duration as these evidences have in the AC; first the Ev2 will be done and then the Ev1; the Ev3 will also be delivered on the same day.
-The same reassessment system will be applied as for the continuous assessment.
-The revision of the final grade follows the same procedure as for the continuous assessment.
The single assessment is requested electronically (e-form) in the specific period (more information on the faculty's website).
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** The delivery of the translation of the face-to-face assessment tests will be carried out if the requirements established in article 263 are met and your request is made in week 4 electronically (e-form) (more information on the faculty's website).
** No unique final synthesis test for students who enrole for the second time or more is anticipated.
** In this link you can check the evaluation guidelines of the Faculty of Psychology: https://www.uab.cat/web/estudiar/graus/graus/avaluacions-1345722525858.htm
Basic bibliography:
Portell, M., & Vives, J. (2019). Investigación en psicología y logopedia: introducción a los diseños experimentales, cuasi-experimentales y ex post facto. Servei de publicacions. Universitat Autònoma de Barcelona.
Losilla, J.M. & Vives, J. (2024). Análisis de datos con Jamoνi. Universitat Autònoma de Barcelona. https://ddd.uab.cat/record/273258
Complementary bibliography:
Babbie, E. (2000). Fundamentos de la investigación social. Thomson.
Gambara, H. (2002). Métodos de investigación en Psicología y Educación. Cuaderno de prácticas (3ª Ed.). McGraw Hill.
Hernández, R. & Mendoza, C. P. (2018). Metodología de la investigación: Las rutas cuantitativa, cualitativa y mixta. McGraw-Hill.
León, O. & Montero, I. (2015). Métodos de investigación en Psicología y Educación (4ª ed.). McGrawHill.
Moreno, R., Martínez, R.J. & Chacón, S. (2000). Fundamentos metodológicos en psicología y ciencias afines. Pirámide.
Shaughnessy, J.J, Zechmeister, E.B & ZechMesiter, J.S (2007). Métodos de investigación en Psicología (7a Ed.). McGraw Hill
Solanas, A., Salafranca, L., Fauquet, J. & Núñez, M.I. (2005). Estadística descriptiva en Ciencias del Comportamiento. Thomson
Data analysis block: Jamovi
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(SEM) Seminars | 111 | Catalan | first semester | morning-mixed |
(SEM) Seminars | 112 | Catalan | first semester | morning-mixed |
(SEM) Seminars | 113 | Catalan | first semester | morning-mixed |
(TE) Theory | 1 | Catalan | first semester | morning-mixed |