Experimental Studies and free software for statistical analysis. Exploratory data analysis. Probability distributions, sampling distributions and confidence intervals. Confirmatory and comparison hypothesis tests. Categorical data analysis and correlation. Simple and multiple linear regression. Analysis of variance and covariance. Repeated measures design. Logistic regression. Survival analysis. Statistical reports.
In any research task, efficient data management is critical. In this course different techniques are provided to the student: from data acquisition point to publication of results. Differente statistical data analysis basic techniques are reviewed in order to study the available data, to provide information about relationships and to answer initial questions proposed by the researcher. We will work on real data about the master issues.
Competencies and learning outcomes
- Update, consolidate, integrate, and evaluate new knowledge in translational neuropsychopharmacology to improve academic, research, and professional activity using continuous self-learning techniques and critical analysis.
- Understand the relevance of the results obtained from animal experimentation in advancing therapeutic management of patients with psychiatric and neurological diseases.
- Possess and understand theoretical-practical and computer knowledge that permits designing studies in the area of translational neuropsychopharmacology.
- Develop sufficient autonomy to join groups of basic and clinical research in the area of neuropsychopharmacology.
- Learn how to express the results obtained in multidisciplinary contexts in the area of translational neuropsychopharmacology orally and in writing.
- Acquire the necessary skills to develop academic, professional, and research activities in the area of translational neuropsychopharmacology.
- Be capable of discussing clinical findings and the use of diagnostic techniques in representative clinical cases of the main psychiatric and neurological diseases.
- Learn to use computer and documentation tools for updating research knowledge in translational neuropsychopharmacology.
- Learn to discriminate and compare scientific information for reviewing studies or developing research that helps to improve scientific knowledge of translational neuropsychopharmacology and make decisions based on scientific evidence.
- Promote continuous learning as a tool for updating knowledge related to translational neuropsychopharmacology.
- Understand the general principles of translational research in neuropsychopharmacology.
- Learn to design and plan clinical trials that permit evaluating the therapeutic use of targets identified in animal modeling studies on neurological and psychiatric diseases.
- Learn to design and plan research projects in the area of translational neuropsychopharmacology.
- Acquire the necessary capabilities to disseminate the knowledge obtained in clinical studies and trials with animal models in the form of scientific articles with an international impact.
- Know and correctly apply the computer tools of scientific documentation and literature to develop research projects and scientific articles in the area of neuropsychopharmacology.
- Know the basic aspects of computerized statistical analysis applied to studies in the area of translational neuropsychopharmacology.
- Assimilate the basic concepts of applied statistics to translational neuropsychopharmacology with specific examples.
- Learn to interpret the results obtained by carrying out animal evaluation models of active drugs in the central nervous system by studying videotapes of psychopharmacological practices.
Objectives (Learning outcomes)
- Communicate and collaborate efficiently.
- Acquire and organize information efficiently.
- Generate efficient audiovisual resources for scientific communication.
- Create efficient reports, presentations and web content for scientific communication.
- Acquire and prepare data for statistical analysis.
- Explore and describe characteristics.
- Explore and describe relationships.
- To know basic concepts and tools for Statistics.
- To know different types of studies and features of experimental studies.
- To know the basics on sampling and experimental design.
- To know the basics on estimation problems. Parametric and non-parametric hypothesis testing problems.
- To solve simple estimation and hypothesis testing problems on a single variable.
- To test relationships for two variables.
- To discover cause-effect relationships for continuous variables.
- To test differences among populations.
- To discover relationships and trends in repeated measurement studies and for survival data.
- To solve effectivity and dose-response studies.
- To synthesize information in multiple variable datasets.
Methodology and grading
- The evaluation system is based on the continuous evaluation through the course. The student must resolve a project based on the statistical analysis of a data set provided by the instructors. Several questions and tasks will be proposed, that should be answered by applying the appropriate statistical technique. Interpretation of results will be critical. The final report, presentation and multimedia content must be included in a personal blog, created by the student.
The task will provide 100% of the grade. The criteria used for evaluating this task will be the following:
1. Adequacy of data processing
2. Adequacy of analysis applied to resolve the proposed issues
3. Correction of the analyses performed
4. Appropriateness of the interpretations and conclusions drawn
5. Writing and spelling of the full report.
6. Quality of presentation.
7. Quality of generated multimedia content.
8. Appropriateness of the structure of the blog, format and design.