First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
DEVELOPMENTAL, PERSONALITY AND INTERPERSONAL RELATIONSHIPS PSYCHOLOGY
Course unit
PSYCHOMETRICS (Ult. numero di matricola dispari)
PSL1000629, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
DEVELOPMENTAL, PERSONALITY AND INTERPERSONAL RELATIONSHIPS PSYCHOLOGY
PS2295, Degree course structure A.Y. 2016/17, A.Y. 2018/19
Dispari
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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 https://elearning.unipd.it/scuolapsicologia/course/view.php?idnumber=2018-PS2295-000ZZ-2017-PSL1000629-DISPARI
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
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

Lecturers
Teacher in charge LIVIO FINOS M-PSI/03
Other lecturers MASSIMILIANO PASTORE 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
Hours of
Individual study
Shifts
Lecture 12.0 84 216.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2016 course timetable

Examination board
Board From To Members of the board
4 2018 01/10/2018 30/09/2019 STEFANUTTI LUCA (Presidente)
DE CHIUSOLE DEBORA (Membro Effettivo)
MANNARINI STEFANIA (Membro Effettivo)
ROBUSTO EGIDIO (Membro Effettivo)
3 2018 01/10/2018 30/09/2019 FINOS LIVIO (Presidente)
ALTOE' GIANMARCO (Membro Effettivo)
PASTORE MASSIMILIANO (Membro Effettivo)
ROBUSTO EGIDIO (Membro Effettivo)

Syllabus
Prerequisites: Elementary set theory (fundamental relations and operations among sets); elementary algebra and 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 open-ended questions (e.g. exercises or short problems) and multiple choice questions. It is possible that a computerized version replaces the paper and pencil exam.
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.
Textbooks (and optional supplementary readings)
  • Piccolo, Domenico, Statistica per le decisioni. La conoscenza umana sostenuta dall'evidenza empirica. Bologna: Il mulino, 2010. Cerca nel catalogo
  • Pastore, Massimiliano, Analisi dei dati in psicologiacon applicazioni in RMassimiliano Pastore. Bologna: Il mulino, 2015. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Video shooting made by the teacher/the students
  • Loading of files and pages (web pages, Moodle, ...)
  • Learning contract/agreement

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