
Course unit
PSYCHOMETRICS
PSL1000629, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Basic courses 
MPSI/03 
Psychometrics 
12.0 
Course unit organization
Period 
First semester 
Year 
2nd Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
12.0 
84 
216.0 
No turn 
Examination board
Board 
From 
To 
Members of the board 
9 2019 
01/10/2019 
30/09/2020 
NUCCI
MASSIMO
(Presidente)
BURIGANA
LUIGI
(Membro Effettivo)
PASTORE
MASSIMILIANO
(Membro Effettivo)

Prerequisites:

Basic knowledge in logicmathematics: algebra, operations and functions. 
Target skills and knowledge:

The first part of the course is aimed at acquiring some basic concepts in psychometrics (e.g. finitary relation, function, combinatorics, measurement scales). In the second and third part will cover topics of descriptive and inferential statistics (eg. random probabilities and the probability distribution, sample, variables, statistical test). The student will be asked to apply the concepts in psychology contexts: both during the lectures and during the exam.
The course of Psychometrics is closely related with other quantitative and methodological courses. 
Examination methods:

Written exam with multiple choice questions on the computer is followed of an optional oral.
The ninety exam questions will be theoretical but also applicative, linked to empirical contexts, in order to evaluate both the knowledge and skills acquired by the student. 
Assessment criteria:

The assessment will be based on the acquisition of both theoretical (even formal) and practical content. 
Course unit contents:

The course is divided into three parts
Basic elements for psychometry:
 Logicalmathematical bases
 Combinatorics
 Theory of measurement and measurement scales
Psychometrics, first part:
 Descriptive statistics
 Experiments and random probabilities
 Random variables and the probability distribution
Psychometrics, second part:
 Principles and methods of statistical inference
 Some examples of statistical inference methods
 Correlation, Regression and ANOVA 
Planned learning activities and teaching methods:

All the lessons of the course will be frontal.
During the lessons, each new topic is followed by short exercises (a few minutes each) in order to verify the concepts. 
Additional notes about suggested reading:

Slides and Lecture notes of Psychometry 
Textbooks (and optional supplementary readings) 

Innovative teaching methods: Teaching and learning strategies
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)
 Latex
 R statistical software
Sustainable Development Goals (SDGs)

