First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
APPLIED COGNITIVE PSYCHOLOGY
Course unit
MULTIVARIATE STATISTICS
PSN1032475, 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
APPLIED COGNITIVE PSYCHOLOGY
PS1978, Degree course structure A.Y. 2017/18, A.Y. 2019/20
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination MULTIVARIATE STATISTICS
Department of reference Department of General Psychology
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 MICHELE VICOVARO M-PSI/03

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses M-PSI/03 Psychometrics 6.0

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

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

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Board From To Members of the board
7 2019 01/10/2019 30/09/2020 VICOVARO MICHELE (Presidente)
DA POS OSVALDO (Membro Effettivo)
MANNARINI STEFANIA (Membro Effettivo)
STEFANUTTI LUCA (Membro Effettivo)
VIDOTTO GIULIO (Membro Effettivo)

Syllabus
Prerequisites: - Good knowledge of basic descriptive statistics (mean, median, variance, etc.).

- Knowledge of the fundamentals of measurement theory (measures on nominal, ordinal, interval, and ratio scales).

- Knowledge of the basics of inferential statistics.

In general, prerequisite knowledge refers to the skills acquired through Psychometrics courses in Bachelor Degree courses.
Target skills and knowledge: THEORETICAL SKILLS: Understanding how to organize and manage a dataset. Choosing the appropriate method of statistical analysis for the various research situations. Understanding how to interpret the results of statistical analyses. Understanding the relationship between research design, method of data analysis, and results interpretation.

PRACTICAL SKILLS: Basic knowledge of Rstudio software for statistical analysis.
Examination methods: Written exam with open questions concerning both theoretical topics and exercises to be solved. The exam lasts 2 hours. The possibility of an optional oral integration will be provided, in the form of a research project to be presented to the class.
Assessment criteria: Specific and critical knowledge about the application of appropriate methods of statistical analysis in specific research situations.
Course unit contents: - Univariate and bivariate distributions of data (identifying and managing the possible presence of ouliers).

- Bivariate and multiple regression analysis.

- Analysis of variance (one-factor and multi-factor ANOVA, analysis of main and interaction effects, between- and within-subject ANOVA).

- Multivariate regression models (elements of structural equation models).

- Effect size and statistical power.

- Fundamentals of the Bayesian approach to data analysis.

- Using the Rstudio software to apply theoretical knowledge to real research contexts.
Planned learning activities and teaching methods: Frontal and interactive lessons, with individual and group exercises.
Additional notes about suggested reading: Slides, exercises and further matherials will be available as learning tools.
Textbooks (and optional supplementary readings)
  • Barbaranelli, Claudio, Analisi dei dati. Tecniche multivariate per la ricerca psicologica e sociale.. Milano: LED, 2007. Cerca nel catalogo
  • Pastore, Massimiliano, Analisi dei dati in psicologia con applicazioni in R. Bologna: Il mulino, 2015. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Interactive lecturing
  • Working in group
  • Questioning
  • Problem solving
  • Flipped classroom
  • Peer feedback

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Top Hat (active quiz, quiz)

Sustainable Development Goals (SDGs)
Quality Education