Spring 2023  PSYC 402 D200
Advanced Topics in History and Theoretical Psychology (4)
Class Number: 6926
Delivery Method: In Person
Overview

Course Times + Location:
Th 2:30 PM – 5:20 PM
RCB 6152, Burnaby

Instructor:
Michael Maraun
maraun@sfu.ca
1 778 7825685
Office: RCB 4202
Office Hours: TBD There will be weekly office hours. Though optional, regular participation in the office hours is highly recommended. It is expected that, among other things, students will use this time to discuss matters related to the lectures, the readings, workouts, assignments, and computational aspects. That is to say, anything whatsoever related to structural equation modeling.

Prerequisites:
PSYC 201, 210, 308 (or 207), 60 units, and a CGPA of 3.0. Other prerequisites vary by topic offering.
Description
CALENDAR DESCRIPTION:
Course can be repeated for credit. Students may not take this course for further credit if similar topics are covered. See Psychology department website for course description.
COURSE DETAILS:
Provisional Structuring of Substantive Areas
PSYC 402/715 is a proseminar in measurement. Structural equation modelling (sem) will be the focus of the spring 2023 offering. Students will, of course, learn how to fit models using the Lavaan package of R and interpret the output. The theoretical underpinnings of sem matrix and covariance algebra, relation between path diagram and model implied covariance matrix, identification, etc., knowledge of which is necessary in order to employ sem in the service of fruitful science, will, however, receive extensive coverage. Three of the distinct branches of sem will be addressed: observed variable path models, measurement models, and full models involving both measurement and structural, regression, components. Students will gain much experience fitting sem models to actual data, and a diversity of models will be encountered. Some special emphasis will be given to longitudinal models (e.g., latent growth model, crosslag regression model) and test theory models (congeneric, tauequivalent, and parallelstructures, and hierarchical factor structure)
COURSELEVEL EDUCATIONAL GOALS:
PSYC 402/715 will be comprised of two parts:
i. Fundamentals/background
• Introduction [3 classes of model; path diagram; mathematization leading to model implied covariance matrix; identification analysis; testing (estimation, model fit, etc.)]
• Matrix Algebra
• Random variables, random vectors, multivariate distributions
• Expectation algrebra, covariance algebra
ii. Three classes of model
Detailed consideration of each of:
• Observed variable path models (i.e., multivariate regression models)
• Measurement models (i.e., confirmatory factor structures)
• Full models involving both measurement model components, and multivariate regression structures bearing on latent variables
Grading
 5 assignments, each worth 20%: 100%
NOTES:
Class Times: Thursday 2:30pm  5:20pm
Room: Microlab RCB 5201
Undergraduates in the 402 section receive letter grades; graduates in the 715 are graded as acceptable/unacceptable.
Undergraduate grading categories
A+ 95 and higher
A 9095
A 8590
B+ 8085
B 7580
B 7075
C+ 6570
C 6065
C 5560
D 5055
F less than 50
REQUIREMENTS:
Components of course
Lectures and lecture notes
Lectures, and the lecture notes on which they are based, are the primary means by which will be covered, the materials of which the course is comprised. The lectures are structured sequentially in accordance with the lecture notes, and proceed through the theoretical materials in a linear fashion, with occasional departures to address computational and outputinterpretational issues. The midterm and final are based exclusively on the lectures. In each lecture, I will be referring directly to the accompanying lecture notes. However, the notes
comprise but a sketch. Accordingly, elucidation, fleshing out, and expanding upon will be the standard order of business.
Note: lecture notes will be available on Canvas, in accordance with lecture schedule.
Readings
The readings will be drawn primarily, but not exclusively, from the following sources and assigned on an as needed basis.
Bollen, K. (1989). Structural Equations with Latent Variables. New York: John Wiley and Sons.
Hayduck, L. (1987). Structural Equation Modeling with LISREL. Baltimore: The Johns Hopkins Press Ltd.
Joreskog, K., and Sorbom, D. (1996). Lisrel 8: User’s Reference Guide. Scientific Software International.
Rosseel, Y. (2018). The Lavaan Tutorial. https://lavaan.ugent.be/tutorial/tutorial.pdf
On weeks on which there are readings, unless otherwise indicate, these readings will be made available to you in pdf form on Canvas at the beginning of the week.
Assignments
Each of the five assignments will be made available to you on Canvas as the semester unfolds. A given assignment will be due on the date and time specified on the schedule, below. Structural equation models will be fit using the Lavaan package of R. You should install R, and the Lavaan package, on your computer as soon as possible.
Workouts
There will be a number of “workouts”, relatively brief computational/theoretical exercises, each of which you will be undertaken either in class our on your own (which will be case will be decided as we move through the semester). Though it is highly recommended that you complete each and every workout (for they are designed to help you consolidate theoretical knowledge and master practical application), they are not for marks and will not be submitted. Rather, there will be made available to you, at the end of the week on which a workout is assigned, an answer key. You may compare your answers to the answer key at your leisure (and, of course, discuss during office hours).
Materials
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/coursematerials/mypersonalizedcoursematerials.
Registrar Notes:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s1001.html