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Advanced Data Exploration Methods - Dimension Reduction, Exploratory Factor Analysis and Clustering Analysis

Informacje ogólne

Kod przedmiotu: 2500-EN-S-112 Kod Erasmus / ISCED: 14.4 / (0313) Psychologia
Nazwa przedmiotu: Advanced Data Exploration Methods - Dimension Reduction, Exploratory Factor Analysis and Clustering Analysis
Jednostka: Wydział Psychologii
Grupy: Experimental Social Psychology specialization
specialization courses for 4 and 5 year
Punkty ECTS i inne: 3.00
Język prowadzenia: angielski
Rodzaj przedmiotu:

fakultatywne

Skrócony opis: (tylko po angielsku)

The course is intended for students that want to learn methods for

exploring data structure.

The course will consist of 10h of theoretical lecture and 20h of practical

workshop.

- 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.

Pełny opis: (tylko po angielsku)

The course is intended for students that want to learn methods for

exploring data structure (Exploratory Factor Analisys – 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.

The course will consist of 10h of theoretical lecture and 20h of practical

workshop.

- The focus of LECTURE part will be on reviewing logic of assumptions,

possibilities and limitations of 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.

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.

Literatura: (tylko po angielsku)

Basic Literature:

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

Efekty uczenia się: (tylko po angielsku)

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 multidimensional

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.

Metody i kryteria oceniania: (tylko po angielsku)

Student performance will be assessed base on preparation (reading

assigned literature and home assignments), class activities (class

performance, solving tasks assigned in class, in-class activity) and final

test.

-homework (40%)

-literature quizzes (15%)

-class activities (15%)

-final test (50%)

Attendance rules

Student may have maximum 2 absences.

Zajęcia w cyklu "Semestr zimowy 2018/19" (zakończony)

Okres: 2018-10-01 - 2019-01-25
Wybrany podział planu:


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Typ zajęć: Ćwiczenia, 30 godzin więcej informacji
Koordynatorzy: Mikołaj Winiewski
Prowadzący grup: Mikołaj Winiewski
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Zaliczenie na ocenę
Ćwiczenia - Zaliczenie na ocenę
Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski.