First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
Course unit
PSYCHOMETRICS (Ult. numero di matricola pari)
PSL1000629, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course First cycle degree in
PS2295, Degree course structure A.Y. 2016/17, A.Y. 2019/20
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Number of ECTS credits allocated 12.0
Type of assessment Mark
Course unit English denomination PSYCHOMETRICS
Department of reference Department of Developmental Psychology and Socialisation
E-Learning website
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 LUCA STEFANUTTI M-PSI/03

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Basic courses M-PSI/03 Psychometrics 12.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 12.0 84 216.0 No turn

Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2016 course timetable

Examination board
Board From To Members of the board
6 2019 01/10/2019 30/09/2020 STEFANUTTI LUCA (Presidente)
DE CHIUSOLE DEBORA (Membro Effettivo)
ROBUSTO EGIDIO (Membro Effettivo)
5 2019 01/10/2019 30/09/2020 FINOS LIVIO (Presidente)
ALTOE' GIANMARCO (Membro Effettivo)
MARCI TATIANA (Membro Effettivo)
ROBUSTO EGIDIO (Membro Effettivo)

Prerequisites: Elementary set theory (fundamental relations and operations among sets); elementary algebra and linear equations; elementary geometry (cartesian diagrams, graph of a function); numerical functions; elementary logic.
Target skills and knowledge: Measurement in psychology: empirical and numerical relational systems; qualitative and quantitative variables; measurement scales; scale transformations.

Descriptive analysis of data in psychology: main statistical indexes for data description and their properties; graphical representation of data.

Statistical inference: generalizing from the sample to the population; sample distribution; hypothesis testing; main statistical tests.

Analysis of the relation between psychological variables; contingency tables and analysis of the association between two categorical variables; correlation and bivariate linear regression.
Examination methods: The exam is written and consists of about 15 open-ended questions (e.g. exercises or short problems) and about 5 multiple choice questions. It is possible that a computerized version replaces the paper and pencil exam. The total exam duration is about 3 hours.
Assessment criteria: Both comprehension of the proposed topics and capability of applying the learned material in practical contexts are assessed.
Course unit contents: Measurement in Psychology: introduction and critical aspects of measurement in the psychological sciences; qualitative and quantitative variables; measurement theory; empirical and numerical relational systems; measurement scales and scale transformations.

Statistical description of data: frequency distributions; central tendency indexes; variability indexes; graphical representation of data; central tendency and variability indexes for grouped data; quantiles; z-scores.

Probability: sample spaces and events; probability axioms and theorems; computations with probability; conditional probability; independence.

Random variables (RV): discrete and continuous RV; probability distribution of a RV; expectation and variance of a RV; algebra of expected values.

Probability distributions: binomial distribution; normal distribution; Chi-square distribution; Student t distribution.

Introduction to statistical inference: hypothesis testing; type I and type II errors; statistical tests; power of a statistical test.

Main statistical tests: test of the mean for one and for two samples, with known or unknown variance; test of the mean for independent or dependent samples.

Contingency tables: joint distribution of two categorical variables; hypothesis of independence; the Chi-square test.

Linear correlation and bivariate regression: analysis of the relation between two quantitative variables; scatter plot; linear dependence; least squares; bivariate linear regression; linear correlation; mention to non linear correlation.
Planned learning activities and teaching methods: Lectures will be followed by exercises.

Lectures present the theoretical aspects of the course and their application, in real contexts, to experimental and empirical data, or simulated data.

Exercises will be of two types: class and individual. Class exercises will be the first occasion that the student has to test her learning. Since they require active participation, class exercises will also constitute an interaction session between students and teacher.

Individual exercises instead are aimed at a progressive autonomy of the student in applying the learned notions in practical contexts. Among individual exercises some coputer sessions will be planned.
Additional notes about suggested reading: Besides the textbook, slides and exercises are available to students in the Moodle site of the course.

Moreover, at the url a free e-learning platform is available to the student for improving statistical skills. In an intercative way, Stat-Knowlab establishes what the student knows in statistics and proposes more and more involving exercises.

It is underlined that the availability of such tools is not an alternative to attending the course. This last remains bay far the most effective way to approach the topic and to deep learning its contents.
Textbooks (and optional supplementary readings)
  • Cristante F., Lis A., Sambin M., Fondamenti teorici dei metodi statistici in psicologia. Padova: UPSEL, 1994. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Interactive lecturing
  • Working in group
  • Questioning
  • Story telling
  • Problem solving
  • Peer assessment
  • Auto correcting quizzes or tests for periodic feedback or exams
  • Loading of files and pages (web pages, Moodle, ...)
  • Stat-KNOWLAB

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Latex
  • Mathematica
  • Matlab
  • Stat-Knowlab