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2023/2024

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

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.

Teachers

Jose Blas Navarro Pastor
Jennifer Morata Serrano
Marina Bosque Prous
Albert Bonillo Martin
Alfredo Pardo Garrido
Diego Redolar Ripoll

Prerequisites

In order to take advantage of the course "Data Analysis" it is necessary to have satisfactorily completed the subject  "Research Methods, Designs and Techniques" ("MEDITI").


Objectives and Contextualisation

"Data Analysis" is the second methodological subject of the Psychology syllabus and belongs to the subject "Research Methods and Psychometrics". It is taught in the first semester of the second year and represents the natural continuation of the subject "Research Methods, Designs and Techniques", imparted in the first year of the degree. The course collects some methodological competences transferred in the subject "Statistics" and incorporates and develops new ones, with an aim for the students to broaden their skills base, an aspect that will allow them to complete the ensuing methodological subjects.

The general formative objectives of the course are:

  1. To train students in the understanding of the conceptual foundations underlying the main statistical techniques that allow them to answer the questions that are usually posed by the professional and/or researcher in Psychology.
  2. To rationally and correctly interpret the results derived from statistical analyses usually performed and published in prestigious scientific journals.
  3. To integrate the concepts developed within 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:

  1. Reason within the framework of statistical thinking.
  2. Know and use the vocabulary of data analysis correctly.
  3. To understand the concept of uncertainty and probability in the field of psychological phenomenology.
  4. Correctly formulate statistical hypotheses.
  5. Identify the fundamental elements of statistical inference.
  6. To understand the type of reasoning implicit in the statistical inference process.
  7. Analyze and understand the risk involved in a statistical decision.
  8. Decide which is the most appropriate statistical technique to test a hypothesis.
  9. Correctly plan the most appropriate analysis strategy in each of the problem situations raised.
  10. Adequately 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

  1. Adequately interpret the results obtained from the application of univariable or bivariable statistical testing.
  2. Evaluate and contrast models, instruments and techniques and decide which are the most appropriate for carrying out univariable or bivariable statistical analysis.
  3. Identify the main models and techniques for univariable or bivariable statistical analysis and interpret the results obtained adequately.
  4. 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 theory: fundamentals and diagnostic tests
  2. Probability models
  3. Statistical inference (I): estimation of parameters
  4. Statistical inference (II): contrast of hypothesis
  5. Inference with two categorical variables
  6. Inference with a categorical and a quantitative variable: comparison of two means in independent samples.
  7. Inference with two quantitative variables: correlation model
  8. Data analysis in repeated measurement designs

 


Methodology

The course "Data Analysis" implies the completion of 6 ECTS credits that represent a total of 150 hours for the student. Of this total, 45 hours will be dedicated to directed formative activities based on theoretical lectures and practical classes consisting in problem solving activities. A total of 18 hours will be dedicated to the revision of problems in seminar rooms and personal tutorials with the teachers. Different autonomous activities will be organized, including 72 hours of student dedication based on the reading of documents recommended by the professors and cooperative learning (group resolution of cases and problem-situations). The remaining 9 hours comprising the course will be dedicated to evaluation activities.

In summary: (1) teaching methodology: lectures, supervised practices, problem-based learning, and case exposition/discussion, and (2) training activities: case studies and technical-scientific lectures.

Note: 15 minutes of a class will be reserved, within the calendar established by the center/degree, for the completion of the evaluation surveys about the teacher's performance and the evaluation of the subject/module by the students.

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      
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 13 0.52 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

Continuous assessment: The marking of the students is based on a continuous assessment process that allows the evaluation of the degree to which they acquire the competences trained in the course. It is based on the performance and delivery of a series of learning evidences associated to different assessment modalities at different times along the semester. This format provides students and teachers with immediate feedback on the level of competencies acquired throughout the course and the degree of improvement in academic performance.

In order to complete the course, students must prove satisfactory performance in two compulsory tests (EV2 and EV4)  of individual authorship and two instrumental or practical evaluation tests (EV1 and EV3) of group authorship.

The EV2 and EV4 tests are carried out in person at the place and date set by the Faculty in the first and second evaluation period respectively. These tests evaluate, by means of multiple-choice tests consisting of 40-45 questions, all the contents of the subject considering both the theoretical and instrumental aspects. EV2 evaluates topics 1 to 4 of the syllabus and EV4 evaluates the remaining topics (5 to 8).

The instrumental or practical evidences, EV1 and EV3, will be carried out approximately one or two weeks before EV2 and EV4, respectively. EV1 and EV3 consist of the exposition and subsequent resolution of a problem-situation that each work team, the members of which will be chosen by the teachers at the beginning of the course within each group of practices, will have been working on during the course. In this sense, EV1 will evaluate the practical aspects of topics 1 to 4 and EV3 those corresponding to topics 5 to 8. In order to be allowed to hand in each of these evidences, the work team will need to have completed 33% of the practical dossiers, that is to say, a minimum of one.

To determine the final grade of the course (FG) the weighted contributions of the respective learning evidences will be considered, so that EV1 contributes 10% to the final grade of the course, EV2 35%, EV3 10%, and EV4 45%. Therefore: FG=EV1(0.10)+EV2(0.35)+EV3(0.10)+EV4(0.45). The students who have completed the learning evidences with a weight equal to or higher than 4 points (40%) cannot be recorded as "non-assessable". Therefore, in order to pass the course following the continuous assessment, the following criteria must be met:

  1. The weighted sum of all the evidences must be equal or superior to 5 points.
  2. The arithmetic average of EV2 and EV4 must be 4.5 points or higher (on a scale from 0 to 10), otherwise the maximum grade in the course will be 4.5.
  3. In accordance with UAB regulations, students who have not passed the course and who meet the following conditions will be eligible for a reparatory exam:
    1. To have carried out the evidences with a weight of at least 2/3 of the total and to have a continuous evaluation   grade of 3.5 points or higher.
    2. EV2 and/or EV4 evidences can be made up.
    3. The grade of the made up evidence(s) will replace the grade obtained previously and the total grade will be recalculated using the criteria described.

The students with a final grade higher or equal to 5 points will not be allowed to take the reparatory exam in order to increase the final grade obtained during the continuous assessment.

The decision to re-sit one or both evidences (EV2 and/or EV4) is up to the student. The reparatory exam(s) of EV2 and EV4 will have the same format and contents as the original tests. The grade(s) obtained in the reparatory exam(s) will replace the grade(s) obtained initially in the previous evaluations carried out during the course (EV2 and/or EV4).

The students who have obtained a final grade equal to or higher than 9 points will be eligible for the qualification "With high Honors". These students will have to take a complementary evaluation test, inperson and in oral format. This test will be carried out at the same time as the reparatory exam. The conditions, characteristics and dynamics of the test to opt for the qualification of "With high Honors" will be specified to the people involved days before the test.

The teaching team recommends the students of 2nd or subsequent enrollments who cannot easily follow the established continuous assessment procedure, to request the completion of a final non-reparable synthesis test that includes the entire syllabus. The students who wish to take this type of evaluation, and who meet the requirements (second or subsequent enrollment), must request it in writing (email) to the course coordinator before the end of week 4.

Single evaluation: the students who consider it appropiate may opt for the single evaluation modality. This type of evaluation implies that on a single evaluation date all the evidences/learning activities will be carried out, with the same considerations, weightings and requeriment level as those corresponding to the continuous assessment. The students enrolled in this type of assessment can attend classes and some of the sessions may even be compulsory. The single evaluation test will take on the same date as the EV4 evaluation and, if necessary, the students may opt for a reparatory exam onthe date set by the Faculty and the same recovery process as that of the continuous assessment will be applied. The duration of the single evaluation test is estimated between five and six hours. It should be noted that EV1 and EV3 are not recoverable.

Therefore and in summary:

  1. The request for a single evaluation implies the waiver to the continuous assessment and implies the delivery on a single date of the necessary number of evaluative evidences to accredit and guarantee the achievement of the objectives and learning outcomes established in the course.
  2. The single evaluation must have the same requirement level as the continuous assessment. The student has the same rights as with the continuous assessment in relation to teaching, grading and, if necessary, re-sitting.
  3. To apply for the single evaluation, students must submit a reasoned request to the school before week 4 of the four-month period.

At the link https://www.uab.cat/web/estudiar/graus/graus/avaluacions-1345722525858.html you can consult the evaluation guidelines of the Faculty of Psychology degrees 2022-23 and the criteria for the translation of evaluation tests of the Faculty of Psychology.


Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Distinction-grade Qualitative assessment 0 0 4, 3, 1, 2
EV1 Instrumental assessment units 1 to 4 10% 0 0 4, 3, 1, 2
EV2 Theoretical and instrumental assessment, units 1 to 4 35% 2 0.08 4, 3, 1, 2
EV3 Instrumental assessment, units 5 to 8 10% 0 0 4, 3, 1, 2
EV4 Theoretical and instrumental assessment, units 5 to 8 45% 2 0.08 4, 3, 1, 2

Bibliography

Cumming, G., i Calin-Jageman, R. (2016). Introduction to the new statistics. New York: Routledge-Taylor & Francis.

Cumming, G. (2012). Understanding the new statistics. New York: Routledge-Taylor & Francis.

Ellis, P.D. (2010). The essential guide to effect sizes. Cambridge: Cambridge University Press.

Field, A. (2022). An adventure in statistics: the reality enigma (2ª ed.). London: Sage Publications Ltd.

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.

Pardo, A., Ruiz, M.A., i San Martín, R. (2009). Análisis de datos (I) en ciencias sociales y de la salud. (2ª ed.). Madrid: Editorial Síntesis.

Pardo, A., Ruiz, M.A., i San Martín, R. (2010). Análisis de datos (II) en ciencias sociales y de la salud. (2ª ed.). Madrid: Editorial Síntesis.

Peña, D. (2001). Fundamentos de estadística. Madrid: Alianza Editorial.

Solanas, A., Salafranca, Ll., Fauquet, J., i Nuñez, M.I. (2005). Estadística Descriptiva en Ciencias del Comportamiento. Madrid: Thomson.

Urdan, T.C. (2022) (5º ed.). Statistics in plain english. New York: Routledge.


Software

The software used in the course is: (1) STATA program, a fundamental tool for a correct follow-up of the course, and (2) the JASP (https://jasp-stats.org/) and JAMOVI (https: // www .jamovi.org /), of a complementary nature, allow the student to delve into the contents presented in the theory sessions.