Learning objectives
1. Knowledge and understanding. Students will be able to achieve a good knowledge of univariate and bivariate descriptive statistics and the principles of statistical inference, applying it to real data in research contexts.
2. Ability to apply knowledge and understanding. Students will be able to use the R development environment to describe and create graphical presentations of simple data structures.
3. Independent judgment. Students will be able to develop critical skills and independent judgment with respect to the description of data in technical reports and the interpretation of the applied inferential test.
4. Communication skills. Students will be able to communicate the results of data analysis, both in numerical and graphic form, with the appropriate iconographic apparatus and the adequate technical-statistical lexicon, to specialist and non-specialist interlocutors.
5. Learning ability. Students will be able to learn new techniques for the description and inference of data in a predominantly autonomous way, in particular in the R development 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
Basic of R using. Data types. Univariate distributions: central tendency and dispersione measures. Distribution form indexes. Linear and non - linear transformations. Interval extimation. Univariate graphical representations. bivariate distributions: associations, correlations, linear models. Bivariate graphical representations. Effect size coefficients. Hints of probability theory and the statistical inference problem. Central limit theorem and the law of large numbers. Main inferential tests to be associated with the descriptive statistics addressed.
Bibliography
Chiorri, C. (2020). Fondamenti di psicometria. McGraw-HIll. (capitoli da 1 a 15).
Venables, W.N., Smith, D.M. and the R Core Team (2012). An introduction to R. Available at: http://www.r-project.org/
Teaching methods
Lectures will be held on-site in compliance with safety standards. Supporting material will be available on the specific, student-reserved platform (Elly) and will include slide presentations and / or audio-video supports.
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.
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.
Students with SLD / BSE must first contact the Centro di Accoglienza e Inclusione dell'Ateneo (cai@unipr.it)
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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