Data Analysis
Code: 102571 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2502443 Psychology | OB | 2 | 1 |
Contact
- Name:
- Jordi Fauquet Ars
- Email:
- Jordi.Fauquet@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
- Albert Bonillo Martín
- Diego Redolar Ripoll
- Sonia Lorente Sanchez
Prerequisites
Students are assumed to have already passed the following course: "Methods, Designs and Research Techniques".
Objectives and Contextualisation
Data Analysis is the second methodological course of the syllabus of the Psychology degree and belongs to the subject Research Methods and Psychometrics. It is taught in the first semester of the second year and represents the continuation of the course Methods, Designs and Research Techniques, taught in the first year of the Psychology degree. The course integrates some of the methodological competences transferred from the Statistics subject and incorporates, and develops new ones, with the purpose of allowing the students to expand their competence base in preparation for the subsequent methodological courses.
Specifically, the objectives to be achieved are the following.
1. Familiarise students with the underlying conceptual foundations of the main statistical techniques used in answering the questions typically faced by Psychology practitioners and/or researchers.
2. Enable students to explain and correctly interpret the results obtained after applying the statistical techniques usually used and published in prestigious scientific journals.
3. Enable students to integrate the concepts developed in the framework of statistical thinking, as a formal structure of reasoning that facilitates the study of natural and psychological phenomena.
At the end of the course, the student must be able to do the following.
- Reason within the framework of statistical thinking.
- Know and correctly use the vocabulary of data analysis.
- Understand the concept of uncertainty and probability in the field of psychological phenomenology.
- Propose the statistical hypotheses correctly.
- Identify the fundamental elements that define statistical inference.
- Understand the type of reasoning implicit in the process of inferential statistics.
- Analyse and understand the risk implicit in a statistical decision.
- Decide which statistical technique is the most appropriate to test the statistical hypothesis.
- Plan correctly the most appropriate analysis strategy in each of the problem situations posed.
- Properly interpret the results obtained in a statistical analysis.
Competences
- Distinguish between the design of research, procedures and techniques to evaluate hypotheses, contrast them and interpret the results.
Learning Outcomes
- Adequately interpret the results obtained from the application of univariable or bivariable statistical testing.
- Evaluate and contrast models, instruments and techniques and decide which are the most appropriate for carrying out univariable or bivariable statistical analysis.
- Identify the main models and techniques for univariable or bivariable statistical analysis and interpret the results obtained adequately.
- Write up reasoned conclusions from the results obtained after applying univariable or bivariable statistical methods and techniques which offer an answer to a research hypothesis.
Content
1. Probability distributions
2. Statistical inference (I): estimation
3. Statistical inference (II): hypothesis testing
4. Relationship between two categorical variables
5. Comparing the means of two independent groups
6. Comparing more than two means: analysis of variance
7. Correlation model
8. Analysis of repeated measures
Methodology
The course Data Analysis counts for 6 ECTS credits that represent a total student workload of 150 hours. Of this total, 45 hours will be dedicated to training activities based on face-to-face lectures and practical sessions consisting of problem solving. A total of 18 hours will be devoted to reviewing problems in seminars and personal tutorials with the teachers. Various autonomous activities will be organized, which will involve 72 hours of student dedication, based on the reading of documents recommended by the teachers and cooperative learning (group resolution of cases and problem situations). The remaining nine hours to complete the course will be dedicated to evaluation activities.
To achieve the objectives of the course, the following taxonomy of activities will be used: (1) teaching methodology: formal lectures, supervised practical’s, problem-based learning, and presentation / discussion of cases, and (2) educational activities: case studies and technical-scientific readings.
N.B. The proposed teaching and assessment methodologies may experience some modifications as a result of the restrictions on face-to-face learning imposed by the health authorities. The teaching staff will use the Moodle classroom or the usual communication channel to specify whether the different directed and assessment activities are to be carried out on site or online, as instructed by the Faculty.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Practical classes | 18 | 0.72 | 4, 3, 1, 2 |
| Theoretical classes | 27 | 1.08 | 4, 3, 1, 2 |
| Type: Supervised | |||
| Review of problems | 10 | 0.4 | 4, 3, 1, 2 |
| Tutorials | 11 | 0.44 | 4, 3, 1, 2 |
| Type: Autonomous | |||
| Cooperative learning | 33 | 1.32 | 4, 3, 1, 2 |
| Reading scripts | 45 | 1.8 | 4, 3, 1, 2 |
Assessment
Students’ final grades are based on a continuous process that assesses the degree of acquisition of competences associated with the course. The evaluation process is based on the delivery of various evidences of learning, associated with different evaluation modes, at different times during the semester. This format provides students and teachers with immediate feedback on the level of skills acquired throughout the course and the degree of improvement in academic performance.
To pass the course, students must produce a satisfactory performance in two compulsory integrative tests (EV2 and EV4) taken individually, and two tests of instrumental or practical evaluation (EV1 and EV3) taken as a group. The integrative tests (EV2 and EV4) will be carried out in person at the date and place set by the Faculty. The instrumental or practical evidences (EV1 and EV3) will be evaluated on the dates set in the schedule of the course. EV1 and EV3 consist of the presentation and subsequent resolution of a situation-problem that each work team must prepare throughout the semester. In addition, a limited final test may be taken individually to opt for the Distinction grade (EV5); the characteristics of this test (EV5) will be explained during the course.
The final grade of the course (FG) is determined by the weighted contributions of the respective learning evidences: FG = EV1 (0.10) + EV2 (0.35) + EV3 (0.10) + EV4 (0.45). Therefore, EV1 contributes to the FG of the course by 10%, EV2 by 35%, EV3 by 10%, and EV4 by 45%.
Students who have submitted evidence of learning with a weight equal to or greater than 4 points (40%) may not be listed in acts as “not assessable
The subject will be considered passed if at least 50% of the expected maximum grade is obtained, which implies a grade of 5 points on a scale of 0 to 10). Students who, after having followed the continuous evaluation process (e.g., having presented evidences with a minimum weight of 2/3 during the continuous AKA evaluation EV2 and EV4), obtain a FG equal to or higher than 3.5 points and lower than 5 points (on a scale of 0 to 10), may take a re-evaluation test (EV6), individually and in writing. The re-evaluation test will be in two different parts corresponding to the two thematic blocks of the course (EV2 and/or EV4); thus, depending on his/her results, the student may choose to resist EV2, resit EV4, or resit both EV2 and EV4. The mark(s) obtained in the re-evaluation test will replace those obtained initially in the previous evaluations made during the course (EV2 and/or EV4).
Students who have passed the subject by obtaining a FG equal to or greater than 5 points may not take a re-evaluation test to increase the FG. Students with a final grade lower than 3.5 points may not take a re-evaluation test and their final grade will be "Fail". Students who have obtained a grade equal to or higher than 9 points will be eligible for the grade of Distinction. These students must complete a supplementary evaluation test (EV5), in face‑to‑face and oral format. This test will take place at the same time as the re-evaluation test. It is not expected that students of second enrollment or later will be evaluated through a single non-recoverable test.
It is expected that students of second or subsequent enrollment may request as an evaluation modality the realization of a final synthesis test, non-recoverable, which will include the entire program. The student who opts for the evaluation by means of a single test, and who meets the criteria of being in a situation of second enrollment or later, must process his application by e-mail to the coordinator of the subject.
On the following link: https://www.uab.cat/web/estudiar/graus/graus/avaluacions-1345722525858.html you can consult the evaluation guidelines of the degrees of the Psychology Faculty 2020-21.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| EV1 Practical evaluation test | 10% | 0 | 0 | 4, 3, 1, 2 |
| EV2 Integrative test | 35% | 3 | 0.12 | 4, 3, 1, 2 |
| EV3 Practical evaluation test | 10% | 0 | 0 | 4, 3, 1, 2 |
| EV4 Integrative test | 45% | 3 | 0.12 | 4, 3, 1, 2 |
| EV5 Distinction-grade | Qualitative assessment | 0 | 0 | 4, 3, 1, 2 |
Bibliography
Reference manuals (fundamental bibliography):
Pardo, A., Ruiz, M.A., i San Martín, R. (2009). Análisis de datos en ciencias sociales y de la salud (I) (2ª ed.). Madrid: Editorial Síntesis.
Pardo, A., i San Martín, R. (2010). Análisis de datos en ciencias sociales y de la salud (II) (2ª ed.). Madrid: Editorial Síntesis.
Complenetary bibliography:
Cumming, G. i Jageman, C. (2016). Introduction to the new statistics. New York: Taylor & Francis.
Ellis, P.D. (2010). The essential guide to effect sizes. Cambridge: Cambridge University Press.
Kline, R.B. (2009). Becoming a behavioral science researcher. London: The Guilford Press.
Kline, R.B. (2013). Beyond Significance Testing: Statistics Reform in the Behavioral Sciences. Washington, DC: American Psychological Association.
Peña, D. (2002). Regresión y diseño de experimentos. Madrid: Alianza Editorial.
Peña, D. (2001). Fundamentos de estadística. Madrid: Alianza Editorial.
Peró, M., Leiva, D., Guàrdia, J., i Solanas, A. (2012). Estadística aplicada a las ciencias sociales mediante R y R-Commander. Madrid: Garceta.
Solanas, A., Fauquet, J., Salafranca, Ll. i Núñez, M.I. (2005). Estadística Descriptiva en Ciencias del Comportamiento. Madrid: Internacional Thomson Editores.