PSY544 Introduction to Factor Analysis

Fakulta sociálních studií
podzim 2017
Rozsah
0/2/0. 4 kr. Ukončení: zk.
Vyučující
doc. Mgr. Stanislav Ježek, Ph.D. (přednášející)
Mgr. Adam Ťápal, M.A. (přednášející)
Garance
doc. Mgr. Stanislav Ježek, Ph.D.
Katedra psychologie – Fakulta sociálních studií
Kontaktní osoba: doc. Mgr. Stanislav Ježek, Ph.D.
Dodavatelské pracoviště: Katedra psychologie – Fakulta sociálních studií
Rozvrh
Po 18:45–20:15 U23, Čt 18:45–20:15 U23
Předpoklady
Students are strongly recommended to have taken at least an elementary course in statistical data analysis. Solid understanding of multiple linear regression is beneficial, as is at least basic knowledge of R. Students with zero or little previous exposure will be given resources and time to catch up and will be expected to do so.
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Předmět si smí zapsat nejvýše 20 stud.
Momentální stav registrace a zápisu: zapsáno: 0/20, pouze zareg.: 0/20, pouze zareg. s předností (mateřské obory): 0/20
Mateřské obory/plány
Cíle předmětu
After successfully taking the course, the student will: Have a deeper understanding of factor analysis than is usual in broader, general courses of statistical data analysis offered in most psychology programs;
Be knowledgeable about the mathematical formulation and the reasoning behind the Common Factor Model;
Know the methods for fitting the model on data and evaluating model fit, common estimation methods and problems related to the model and its use;
Be able to apply unrestricted (exploratory) and restricted (confirmatory) factor models using different software;
Understand the principles of analytical rotation;
Osnova
  • Introduction: What is factor analysis? Objectives, goals and principles.
  • Exploratory vs. Confirmatory factor analysis.
  • Matrix algebra basics: Scalars, vectors and matrices
  • Basic matrix and vector operations and functions.
  • The fundamental equations of factor analysis.
  • Mean, covariance and correlation structures.
  • Methods of fitting the model on data.
  • Model identification and rotational indeterminacy.
  • Fitting the model on population and sample correlation matrices.
  • The communality problem. Iterative and non-iterative estimation – Principal Factors Method, Ordinary Least Squares, Maximum Likelihood.
  • Heywood cases.
  • Evaluating model fit.
  • Fitting the unrestricted model with software.
  • Common rules of thumb and guidelines for choosing the number of factors. Alternative methods - parallel analysis, minimum average partial.
  • Goodness-of-fit tests. Common fit indices.
  • Rotation. The concept of rotation and simple structure.
  • Orthogonal rotations, oblique rotations, target rotation.
  • Restricted (confirmatory) factor analysis.
  • Constraints, restrictions and identification conditions.
  • Parameter matrices. Free and fixed parameters. Path diagrams.
  • Common estimation methods.
  • Fitting the restricted model with software.
  • Goodness of fit in CFA. Methods and processes for evaluating model fit. Common fit indices. Tests of good fit.
  • Comparing different models.
  • Special topics Bi-factor models. Group models. Differences between Principal Components Analysis and Factor Analysis. Classical Test Theory Applications.
Literatura
  • MULAIK, Stanley A. Foundations of factor analysis. Second edition. Boca Raton: CRC Press, Taylor & Francis Group, 2010, xxiii, 524. ISBN 9781420099614. info
Výukové metody
Two 50-minute lectures per week, individual homework assignments
Metody hodnocení
Three homework assignments, final take-home exam, individual examination and discussion over the submitted exam
Vyučovací jazyk
Angličtina
Navazující předměty
Další komentáře
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Předmět je zařazen také v obdobích jaro 2017, podzim 2018.