# DATA ANALYSIS I cod. 1005497

Academic year 2015/16
1° year of course - First semester
Professor
Academic discipline
Psicologia generale (M-PSI/01)
Field
Psicologia generale e fisiologica
Type of training activity
Characterising
56 hours
of face-to-face activities
8 credits
hub: PARMA
course unit
in - - -

## 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.

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.

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## Bibliography

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

lectures and in-class problems

## Assessment methods and criteria

For students that come to class, three written partial take-home exams (short answers). Students will solve problems using the R environment and hand in their work during the course. All and only students that have scores on all three partials will be given a grade by this method. Students who do no receive a grade by this method will take an oral exam on the whole program.

## Other information

attending the lectures is highly recommended