First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
Course unit
PSO2043915, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
PS1091, Degree course structure A.Y. 2017/18, A.Y. 2019/20
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Department of reference Department of General Psychology
Mandatory attendance No
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge GIOVANNA CAPIZZI SECS-S/01

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 6.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 42 108.0 No turn

Start of activities 07/10/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2017 course timetable

Prerequisites: Students need to have elementary knowledge of probability and basic statistics.
Target skills and knowledge: At the end of the course students will have the necessary skills for:
1) fitting suitable models to relate one or more predictors/experimental factors with a specific bio-medical status. In particular, students will get knowledge and skills for the
1.a) choice of the most "convenient" model (in terms of relevant predictors, parsimony in the number of fitted parameters, their interpretation, evaluation of explanatory and predictive properties of the best subset of identified predictors).
1.b) knowledge on statistical diagnosis of the fitted models, identifying simply anomalous or "influential" statistical units on the model.
2) extending the knowledge on the relationship between location and variability indicators of two populations to the case of multiple populations (possibly associated with different experimental conditions).
3) learning the importance of explorative multivariate data analysis and checking distributional assumptions for a correct interpretation of the p-values of parametric test, understanding the importance of a non parametric approach whenever one or more assumptions can be violated.
4) choosing properly statistical methodologies according to the type of study available (cohort, case-control, cross-sectional, etc.).
Examination methods: The exam is written and takes a minimum of two hours. The entireexamination is carried out in the computer room and largely involves the use of the R software.
Students will have to answer a set of open and multiple-choice questions concerning:
1. The statistical analysis of a set of data.
2. The theoretical justification of the choice of one method of analysis rather than another.
3. General questions on a statistical methodology not necessarily applicable to the analysis of a real data set.

The number of questions is variable (min 36, max 48 between items and sub items) and is related to the number of data sets to be analyzed (max 3) or to the complexity of the only assigned data set.

In any case, a careful balance between the number of questions and the complexity of the data set to be analyzed is guaranteed by examination. When the real data is more complicated to be studied, the number of questions is approximately equal to the minimum indicated above.

The final grade results from a weighted average of the number of incorrect answers and the number of unanswered questions. The weights are from time to time adjusted for the complexity of the question.

At the end of the test students will always have access to their test and to the details of the calculation for the assigned grade.
Assessment criteria: The evaluation of the student's preparation will be based on the
a) understanding of the handled topics;
b) acquisition of concepts, proposed methodologies and an adequate terminology for writing statistical reports on multivariate data sets (with heterogeneous variables observed on several statistical units).
c) ability, based on the analysis of case studies, to identify the most appropriate analysis methodology, to apply it autonomously and consciously, to isolate significant results highlighting positive and critical issues in thei analysis.
Course unit contents: - Univariate and multivariate explorative statistical analysis of collected data.
- Statistical tools for testing association and
dependence among categorial and continuous experimental data in the biomedical framework: model free techniques for multiway contingency tables.
- Generalized Linear Models (linear and logistic multiple regression).
- ANOVA for independent and repeated measures.
- Introduction to non-parametric statistics.
Planned learning activities and teaching methods: Frontal teaching and guided exercises in the computer room in which the results of experimental studies in the biomedical field are presented and analyzed. Computer labs are an integral part of the course. Real case studies are analyzed with R. During the coputer labs we will proceed to an explorative analysis of multivariate data and to fitting convenient univariate and multivariate models to investigate the relationship between several variables and suitably predict the outcome.

Attendance is strongly recommended, especially for computer labs.
Additional notes about suggested reading: Slides of the lessons, detailed statistical analysis, including the R commands, of the case studies treated during the computer labs, are available on the teaching Moodle platform.
Textbooks (and optional supplementary readings)
  • Triola M. M., Triola M. F., ‚ÄúStatistica per le discipline biosanitarie‚Äú. --: Pearson Education It, 2009. Cap. 1-8, 10, 12 Cerca nel catalogo
  • Fox J., Applied regression analysis, linear models, and related methods. --: Sage, 1997. Cap. 5-15 Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Case study
  • Working in group
  • Questioning
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • R (software)