PSY544 Introduction to Factor Analysis

Faculty of Social Studies
Autumn 2018
Extent and Intensity
0/2/0. 4 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Mgr. Adam Ťápal, M.A. (lecturer)
Guaranteed by
doc. Mgr. Stanislav Ježek, Ph.D.
Department of Psychology – Faculty of Social Studies
Contact Person: doc. Mgr. Stanislav Ježek, Ph.D.
Supplier department: Department of Psychology – Faculty of Social Studies
Timetable
Mon 18:00–18:50 U44; and Wed 19. 9. 18:00–18:50 U43, Wed 26. 9. 18:00–18:50 U43, Wed 3. 10. 18:00–18:50 U43, Wed 10. 10. 18:00–18:50 U43, Wed 17. 10. 18:00–18:50 U43, Wed 24. 10. 18:00–18:50 U43, Wed 31. 10. 18:00–18:50 U43, Wed 7. 11. 18:00–18:50 U43, Wed 14. 11. 18:00–18:50 U43, Wed 21. 11. 18:00–18:50 U43, Wed 5. 12. 18:00–18:50 U43, Wed 12. 12. 18:00–18:50 U43
Prerequisites
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.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20, only registered with preference (fields directly associated with the programme): 0/20
fields of study / plans the course is directly associated with
Course objectives
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;
Syllabus
  • 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.
Literature
  • MULAIK, Stanley A. Foundations of factor analysis. Second edition. Boca Raton: CRC Press, Taylor & Francis Group, 2010, xxiii, 524. ISBN 9781420099614. info
Teaching methods
Two 50-minute lectures per week, individual homework assignments
Assessment methods
Three homework assignments, final take-home exam, individual examination and discussion over the submitted exam
Language of instruction
English
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2017, Autumn 2017.
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