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(in Polish) Advanced Data Exploration Methods - Dimension Reduction, Exploratory Factor Analysis and Clustering Analysis

General data

Course ID:	2500-PL-PS-SP15-14
Erasmus code / ISCED:	14.4 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0313) Psychology The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	(unknown)
Name in Polish:	Advanced Data Exploration Methods - Dimension Reduction, Exploratory Factor Analysis and Clustering Analysis
Organizational unit:	Faculty of Psychology
Course groups:
ECTS credit allocation (and other scores):	4.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language:	English
Short description:	The course is intended for students that want to learn methods for exploring data structure. The course will consist of theoretical lecture and followed by practical excercises.  The focus of LECTURE part will be on reviewing logic of assumptions, possibilities and limitations of PCA, EFA, MDS and Clustering methods.  The focus of WORKSHOP part will be on practical issues such as selecting the appropriate analysis, preparing data for analysis, interpreting output, and presenting results of a complex nature. The primary goal of the course is to develop an applied and intuitive understanding of the covered statistical material.
Learning outcomes:	Students who successfully complete this course will be able to:  Understand the latent variable measurement problems that can and cannot be addressed using factor analysis  Determine which technique is appropriate for different problems, based on the goals of the research.  Explore multivariate data using visualization techniques (including multi-dimensional scaling) to illuminate the structure in the data.  For clustering problems, be able to choose a suitable metric and technique, depending on the goals of the research, as well as be aware of the strengths and limitations of the various approaches.  For data structure problems, be able to select appropriate method, construct model, run analysis, interpret and report results.

Classes in period "Winter semester 2023/24" (past)

Time span:	2023-10-01 - 2024-01-28	Selected timetable range: weekly course term Navigate to timetable MO TU W TH CW FR
Type of class:	Classes, 30 hours more information
Coordinators:	(unknown)
Group instructors:	Mikołaj Winiewski
Students list:	(inaccessible to you)
Examination:	Course - Grading Classes - Grading
Full description:	The course is intended for students that want to learn methods for exploring data structure (Exploratory Factor Analysis – EFA and PCA; Multidimensional Scaling and Clustering algorithms). The focus will be both on applying it in own research and/or to evaluate the work of others. Covered material will include: Data structure: - PCA and EFA logic and utility - EFA – models, assumptions and diagnostics (tests and plots) - extraction and rotation and methods for establishing number of factors / components Clustering: - Clustering – methods and utilities for social sciences - Different algorithms, similarity and dissimilarity measures, establishing number of clusters - validity of clusters, stability of cluster solutions - practical approaches to clustering Scaling: - purpose of multidimensional scaling (MDS) - proximities and preferences as data - MDS models - goodness of fit Specific topics and amount of covered material will depend in part on the interests of the students and class progress. For instance, if needed, we may want to cover the confirmatory factor analysis.
Bibliography:	Basic Literature (may be subject to change):  Aldenderfer, Mark S. and Roger K. Blashfield (1984). Cluster analysis. Quantitative Applications in the Social Sciences Series No. 44. Thousand Oaks, CA: Sage Publications  Kruskal, Joseph B. & Wish, Myron (1978). Multidimensional scaling. Sage University Paper Series on Quantitaive Applications in the Social Sciences. Beverly Hills, CA: Sage Publications.  Dunteman, George H. (1989). Principal components analysis. Quantitative Applications in the Social Sciences Series, No. 69. Thousand Oaks, CA: Sage Publications  Kim, Jae-On and Charles W. Mueller (1978a). Introduction to factor analysis: What it is and how to do it. Quantitative Applications in the Social Sciences Series, No. 13. Thousand Oaks, CA: Sage Publications  Kim, Jae-On and Charles W. Mueller (1978b). Factor Analysis: Statistical methods and practical issues, Quantitative Applications in the Social Sciences Series, No. 14, Thousand Oaks, CA: Sage Publications Note: Additional literature may be updated on the first class and/or added to the list.

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