Learning objectives
1. Knowledge and understanding. Students will achieve a solid understanding of descriptive statistics and of its use in basic and applied research.
2. Application of knowlege and understanding. Students will be able to use the R programming environment to describe simple data structures and to create graphical presentations.
3. Autonomy of judgment. Students will refine critical thinking and autonomy of judgment in relation to data description in technical reports.
4.Communication skills. Students will be able to communicate the results of descriptive data analyses, both by numeric summary statistics and by graphical tools.
5. Learning skills. Students will develop the ability to learn new techniques for data description especially within the R programming environment.
Prerequisites
none
Course unit content
The course will present basic notions of measurement theory and univariate and bivariate descriptive statistics, with applications to research in psychobiology and cognitive neurosciences. The course also introduces to the R programming environment for statistical analysis and data presentation.
Full programme
Measurement theory. Precision and accuracy. Data. Univariate distribution. Central tendency and dispersion. Histograms and box-plots. Linear correlation. Regression. Scatterplots and bag-plots. Smoothers. Contingency tables. Association with categories. Multidimensional data structures. Central Limit theorem and the law of large numbers, sampling, confidence intervals. Contemporary debate on statistical testing.
Bibliography
All training materials are made available on the Elly page of the course: https://elly2020.medicina.unipr.it/
The student can download the course notes, slides, video recordings, exercises.
Furthermore, the following texts are recommended:
Chiorri, C. (2010). Fondamenti di psicometria. McGraw-HIll. (pp.1-250 e 387-460).
Bruno, N. (2013). Introduzione alla statistica descrittiva con R - Dispensa per il corso di Tecniche di Analisi di Dati I. see personal website of the instructor. (pp. 50).
Venables, W.N., Smith, D.M. and the R Core Team (2012). An introduction to R. http://www.r-project.org/ (optional, recommended for students that do not come to class)
Teaching methods
Teaching will take place in a blended mode: traditional lessons will be delivered mainly at a distance; specific in-depth studies and exercises will be carried out in small groups, in the classroom, guaranteeing the distances required by the safety procedures. All the material will still be videotaped and uploaded to the Elly platform.
Assessment methods and criteria
Written exam, with two open theory questions on the entire program, and an exercise in the R environment. The exercise involves the analysis of data that will be made available (through Elly) no later than 48 hours before the test. The analyzes will include a first part relating to descriptive statistics on the data and two subsequent parts in which inferential hypothesis tests will be required. In addition to the correct execution of the statistics, the ability to adequately interpret and comment on the outputs of the analyzes will be considered an essential part for the purpose of sufficiency. The evaluation out of thirty will be as follows:
first theory question: 0-8 points; second theory question: 0-8 points
exercise: 0-14 points, divided as follows:
first part 0-4 points;
second part 0-5 points;
third part 0-5 points.
If due to the persistence of the health emergency it is necessary to adopt the remote mode for the exams, we will proceed with the written test conducted at a distance (via Teams and Elly).
The student may ask that the exam be supplemented by an oral test, provided that the outcome of the written test is sufficient. The oral exam is structured in a similar way to the written exam: questions related to the contents of the entire program and a short exercise in R environment on data used for the exercises during the course. If it is necessary to adopt the remote modality, the optional oral exam will also be done remotely, using the Teams platform.
Other information
the execution of the proposed exercises is strongly recommended; it is recommended to contact the teacher to verify their correctness.
2030 agenda goals for sustainable development
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