
Course unit
PSYCHOMETRICS (Ult. numero di matricola pari)
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 
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 
Examination board
Examination board not defined
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 openended 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; zscores.
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; Chisquare 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 Chisquare 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 http://statknowlab.unipd.it a free elearning platform is available to the student for improving statistical skills. In an intercative way, StatKnowlab 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.

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, ...)
 StatKNOWLAB
Innovative teaching methods: Software or applications used
 Moodle (files, quizzes, workshops, ...)
 Latex
 Mathematica
 Matlab
 StatKnowlab

