14PSPSO16 - Multivariate Analysis

Course specification
Course title Multivariate Analysis
Acronym 14PSPSO16
Study programme Psychology
Module
Type of study
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 4.0 Status
    Condition To taking the exam it is necessary for students to pass Introdaction to Statistics (14PSPSO04) and Advanced Statistics (14PSPSO10) exams. Oblik uslovljenosti
    The goal a) Introducing students: - to concepts and operators which are necessary for understanding complex latent (structural and dynamic) relations among psychic phenomena and processes - to algorithms and programs which enable performing basic multivariate statistical procedures b) Train students: - to choose methods of statistical supports for the complex research plots referring to the latent space - to interpret results of the multivariate analyses on their own - to make decisions based on the multivariate analyses’ results
    The outcome At the end of this course, students are expected to be prepared: - to choose adequate statistical methods of the multivariate analysis for supporting the problems typical for psychology investigations - to carry out multivariate data analyses using the specified statistical programs - to interpret results of the multivariate data analyses
    Contents
    Contents of lectures I Basis of the geometry of the vector space and matrix algebra; II Factor and component analysis; III Canonical correlation analysis; IV Discriminant analysis; V Cluster analysis; VI Multidimensional scaling
    Contents of exercises Conducting the multivariate statisical tecniques using adequate statistical softwares and interpretation of the results
    Literature
    1. Kovačić, Z. (1994). Multivariate analysis. Belgrade: Faculty of Economy.
    2. Supplementary literature: StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft.
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    2 2
    Methods of teaching Lectures and exercises
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures 10 Test paper 35
    Practical lessons Oral examination 25
    Projects
    Colloquia 30
    Seminars
    Vrh strane