An introduction to an Introduction
PSY544 – Introduction to Factor Analysis
Week 1

First off.....English!
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•This course is taught in English (yay!) – for many reasons
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•All lectures, all homeworks, all e-mails, the exam...
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•Even though I do speak Czech, please no Czech in class or in your coursework
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•Am I too fast? Am I too slow? Do I mumble? Do I sound funny? Tell me.

Course logistics
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•Lecture times are Mon (U44) + Wed (U43), 18:00 – 18:50
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•4 credits
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Course logistics
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•No official requirements, but…
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•At least an elementary stats course (correlation, linear regression, partial correlation, multiple
regression) •Some knowledge of R is great (we’ll need it later on, you have time)
•If you’re not so sure, please catch up/refresh
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Course logistics
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•Math!
•We will learn a bit of matrix algebra, it’s EASY (might be a review for some of you)
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•But yes, this course will be more math-y than most PSYCH courses. Don’t worry, even if you think
you suck at math.
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Course logistics
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•Requirements:
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•Participation (will be somewhat monitored, no strict rules…for the       moment J )
•Homework (three short homework assignments, 20% of grade)
•Exam (take-home, 40% of grade)
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•Grading criteria in the syllabus

Course logistics
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•Academic misconduct – no copying, no teamwork on assignments,
no plagiarism. Pretty please.
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•Course materials:
•Notes (presentations) will be given ahead of time, bring them if you wish
•No other material is necessary, but feel free
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Course logistics
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•A slightly “different” course. Relatively speaking:
•More frequent
•More frontal
•Less time spent on assignments
•NO group projects (does anyone even like those?)
•Narrower scope, but more in-depth

Any questions?
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•Please don’t tear me apart

Course content
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•First:
•Factor analysis at-a-glance
•Definition and review of key terms, ideas and concepts
•A bit of history (a very tiny bit)
•Scalars, vectors and matrices
•Basic vector and matrix operations and functions
• (Assignment 1)
•+ Review your Greek / Γρεεκ J
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Course content
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•Second:
•The model (The Unrestricted [Exploratory] Common Factor Model)
•The methodology (Fitting the model, Estimation, Rotation, Fit)
•The software! (CEFA)
• (Assignment 2)
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Course content
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•Third:
•Still the same old model (The Restricted [Confirmatory] Common Factor Model)
•The methodology (Constraints, Identification, Fit)
•The software! (lavaan)
• (Assignment 3)
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Course content
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•Further (if time permits):
•Special topics and „extras“
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Course objectives
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•At the end of the semester, you will:
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•Have a solid understanding of the theory behind EFA and CFA
•Become an informed data analyst when performing FA
•Be able to use major software for EFA and CFA
•Be able to interpret and communicate EFA and CFA results
•Be able to evaluate other people’s work