Guía docente de Metodología en Neurociencia Cognitiva: Investigación Básica y Aplicada (M30/56/2/27)

PHASE A. PROGRAMMING AN EXPERIMENT:

1. Introduction: Aim of the study, experimental design, formulation of hypotheses
2. Training in experiment programming using E-prime
3. Individual programming of an experiment, and problem-solving throughout the process

PHASE B. STATISTICAL ANALYSIS:

4. Data pre-processing using E-prime and Excel: Filtering, central and dispersion statistics
5. Data Analyses I: Descriptive statistics using statistical software
6. Data Analyses II: Hypothesis testing using statistical software (ANOVA, ANCOVA, regression, statistical assumptions)
7. Data and hypothesis management: Questionable (ex-post facto hypotheses, inadequate sample size, fishing, partial report of measures) and recommendable research practices  (pre-register, open data repositories, null results reports)

• It is recommended for the student to have English skills sufficient to follow lessons and actively participate in them.
• It is recommended for the student to have above-average general software use skills.

• Students must learn to use specialized software for programming experiments and data analyses
• Students must be able to:
  • Critically evaluate the quality of basic and applied research (experimental control, designs, questionable research practices, etc.)
  • Program experiments
  • Work with quantitative data: summarizing, classifying, plotting, and presenting these data to an audience.
  • Manage data, hypotheses and results from an ethical perspective

• Block 1. Programming an experiment with E-Prime or similar open-source software (OpenSesame, PsychoPy etc.).:
  • Unit 1. Introduction: Aim of the study, experimental design, formulation of hypotheses
  • Unit 2. Training in experiment programming
  • Unit 3. Individual programming of an experiment, and problem-solving throughout the process
• Block 2. Statistical analysis:
  • Unit 4. Recommended practices in Psychology and Neuroscience
  • Unit 5.  Data Analyses I: Data preprocessing and descriptive statistics with statistical software
  • Unit 6.  Data Analyses II: inferential analyses with statistical software

All units are simultaneously theoretical and practical, and include tasks to apply the acquired knowledge to simulated cases.

Block 1

• Mathôt, S., Schreij , D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44, 314-324.
• Schneider, W., Eschman, A., & Zuccolotto, Z. (2002). E-Prime user’s guide (Version 1.1). Psychology Software Tools

Block 2

• Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge. https://granatensis.ugr.es/permalink/34CBUA_UGR/1p2iirq/alma991006083639704990  
• Field, A. (2016). An adventure in statistics. SAGE. https://granatensis.ugr.es/permalink/34CBUA_UGR/1egp27c/alma991009308549704990 
• Kurt, W. (2019). Bayesian statistics the fun way. No Starch Press, Inc. https://granatensis.ugr.es/permalink/34CBUA_UGR/1p2iirq/alma991014106019104990

• Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Macmillan International Higher Education.
• Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. SAGE.
• Open Science Collaboration (2015). Estimating the reproducibility of psychological science. Science, 349, aac4716.
• Pownall, M. et al. (2021). Navigating open science as early career feminist researchers. Psychology of Women Quarterly, 45, 526-539.

• American Psychological Association. APA style. https://apastyle.apa.org/
• Danielle Navarro (2020). R for Psychological Science. https://psyr.djnavarro.net/
• Mark Goss-Sampson. (2019). Statistical analysis in JASP: A guide for students. Recovered from:
• https://gala.gre.ac.uk/id/eprint/25585/7/25585%20GOSS-SAMPSON_Statistical_Analys is_In_JASP_A_Guide_For_Students_%28Pub%29_2019.pdf
• The jamovi project (2022). jamovi (Version 2.3) [Computer Software]. Retrieved from https://www.jamovi.org 
• Prado https://prado.ugr.es/ 

[Article 18 of the UGR Assessment Policy and Regulations establishes that the ordinary assessment session (convocatoria ordinaria) will preferably be based on the continuous assessment of students, except for those who have been granted the right to a single final assessment (evaluación única final), which is an assessment method that only takes a final exam into account.]

PHASE A: 50% of the total score

• Participation/attendance in discussion sessions and student attitude in class : 15%
• In class individual assignments 15%
• Final individual assignments 20%

PHASE B. 50% of the total score

• Participation/attendance in discussion sessions and student attitude in class : 15%
• In class individual assignments 15%
• Final individual assignments 20%

Individual assignments must be handed before the due date. Late work will be accepted with a 20% penalty.

A minimum of 25% in each phase is required to pass the course.

If students do not reach the minimum required in each part of the course, their maximum score will be 49% (4,9)  of the total score.

[Article 19 of the UGR Assessment Policy and Regulations establishes that students who have not passed a course in the ordinary assessment session (convocatoria ordinaria) will have access to an extraordinary assessment session (convocatoria extraordinaria). All students may take part in this extraordinary assessment session, regardless of whether or not they have followed continuous assessment activities. In this way, students who have not carried out continuous assessment activities will have the opportunity to obtain 100% of their mark by means of an exam and/or assignment].

For the extraordinary assessment, the student must submit all the previously described individual assignments. The qualification percentages will be kept up to a maximum of 10 (100%), once participation/attendance is disregarded.

[Article 8 of the UGR Assessment Policy and Regulations establishes that students who are unable to follow continuous assessment methods due to justifiable reasons shall have recourse to a single final assessment (evaluación única final), which is an assessment method that only takes a final exam into account. In order to opt for a single final assessment (evaluación única final), students must send a request, using the corresponding online procedure, to the coordinator of the master’s programme, in the first two weeks of the course or in the two weeks following their enrollment (if the enrollment has taken place after the classes have already begun). The coordinator will communicate this information to the relevant teaching staff members, citing and verifying the reasons why the student is unable to follow the continuous assessment system.]

For the single final assessment, the student must submit all the previously described individual assignments. The qualification percentages add up to a maximum of 10 (100%), once participation/attendance is disregarded.

Teaching methodologies:

• MD01 Lectures
• MD02 discussion and debate sessions
• MD03 Problem solving and practical case examples
• MD06 Simulation exercises

Learning activities and timing:

• Classes: 36 h
  •  Teacher presentations: 16 h
  •  Student problem resolution and practical examples: 20 h 
• Autonomous learning: 64 h
  • Student individual work: 64 h

The teaching methodology and assessment will be adapted to students with specific needs (SEN), in accordance with Article 11 of the Regulations on assessment and grading of students at the University of Granada.

This course will follow the recommendations from the UGR Action Plan for Equality regarding the use of nonsexist and inclusive language and visibility of women's and minorities' contributions to the field.

Information about Artificial Intelligence
In this course, the use of generative artificial intelligence (GenAI, such as chatGPT or similar) is allowed for several purposes, for example, to help us with orthography and grammar of written texts, to  help us improving the quality of the texts we write, as well as to  help us with translating texts.
GenIA is NOT allowed as a tool to generate contents (either partially or in full) of course assignments as all the work you submit should be your own original work. Remember that students are accountable for their own work and therefore they have to make sure that the assistance provided by any GenAI tool does not lead to them accepting false or incorrect information or engaging in plagiarism.
Additionally, it is recommended to use UGR tools (such as Microsoft Copilot), to ensure data confidentiality.
If you have used an IAGen tool in some assignments, you are required to acknowledge the use of such tool by adding a specific section at the end of the text, similar to this:
----------------------
Microsoft Corporation has been used in this work. (2024). Microsoft Copilot [Software].
Retrieved from https://www.microsoft.com/copilot for the following tasks: [….]. The author accepts full responsibility for the final document.