Structural Equation Modeling, Spring 2015 (KU)

Upcoming Workshops:

Thank you for visiting my course notes. Here are some upcoming opportunities to learn from me and my colleagues in person:

Currently no workshops are planned

Course Information

Instructor:Dr. Jonathan Templin
Office Phone:785-864-5714
Office:614 Joseph R. Pearson Hall
Office Hours:To be announced
Course Meeting Time:Wednesdays from 1:30pm-4:20pm
Course Meeting Location:147 Joseph R. Pearson Hall

Brief Course Description

PRE 906, Structural Equation Modeling, is a course about the fundamentals of path analysis combined with latent variable modeling which, more broadly, falls under the heading of structural equation modeling. The course is designed to provide details of structural equation modeling, from the statistical underpinnings to how to run many various types of structural equation analyses, combining theoretical and practical perspectives.

The course will use the R statistical program with R Studio and the Lavaan package (Rosseel, 2012) for all computational and data analysis work involved in the course.

For all other specific information regarding general course policies, course evaluation rubrics, and grading systems, please see the course syllabus at the link below.

All readings will be made available via Dropbox shared folder. Please email me for an invitation to this folder.

Course Materials

Syllabus:PRE 906, Fall 2015 Syllabus
Project Information:PRE 906, Fall 2015 Project Information

Helpful R Links and Resources

Books: R for Data Science (Grolemund and Wickham, 2015)
R for SAS and SPSS Users by Muenchen (2007)
An introduction to R by Venables et al. (2013)
aRrgh: a newcomer's (angry) guide to R
DataCamp Introduction to R
Cookbook for R
Quick References:Reference Card #1 (various authors, noted on card)
Reference Card #2 (various authors, noted on card)

Tentative Schedule of  Course Topics

DateTopic & Content LinkRequired ReadingOptional ReadingsAssignments Due
1/21/2015Introduction to Structural Equation Modeling
Venables, Smith, and the R Core Team (2014)
Univariate and Multivariate DistributionsKline, Ch. 1; Gelman & Hill, Ch 2Johnson & Wichern, Chs. 2 and 4
Project #1
2/4/2015Maximum and Robust Maximum Likelihood Estimation; Missing Data with (R)MLMaximum likelihood and Robust ML
Enders, Ch. 3
Enders, Ch. 5
HW #1
2/11/2015Multivariate Models; Model Fit EvaluationKline, Ch. 5
Kaplan, Ch. 5; Pajares & Miller (1994)
HW #1 Rev
2/18/2015Path AnalysisKline, Ch. 6
HW #2
2/25/2015Introduction to Confirmatory Factor AnalysisBrown, Ch. 3
Hu & Bentler (1999); Bandalos & Finney (2010)
HW #2 Rev
3/4/2015Review WeekHW #3
3/11/2015No Class: Baby breakHW #3 Rev
3/18/2015No Class: Spring BreakNone
3/25/2015Scale Building with CFABrown, Ch. 4
Brown, Ch. 5
4/1/2015Multifactor CFA and Differing Factor Covariance ModelsBrown, Ch. 8Kaplan, Ch. 3HW #4
Project #2
4/8/2015Structural Equation Modeling: Path Analysis with Latent VariablesKline, Ch. 8
Kaplan, Ch. 4; Mueller & Hancock (2010)
HW #4 Rev
4/15/2015No Class: Conference Travel Day
4/22/2015On Test Scores (Part 1)HW #5
4/29/2015On Test Scores (Part 2): How to Properly Use Test Scores in Secondary Analyses
5/6/2015 Invariance/DIF Testing & Generalized ModelsBrown, Ch. 7
Kline, Ch. 11; Lomax (2010)
Project #3 (Optional)
5/15/2015 (Last Day of Finals Week)No Class-Finals WeekNoneProject #3

HW #5 Rev
Dropped from Lecture List:SEM when Outcomes Have Different DistributionsAtkins and Gallop (2007)Skrondal & Rabe-Hesketh (2004), Chs. 1 and 2
Dropped from Lecture List:Exploratory Analyses with CFA; EFA and PCABrown, Ch. 5
Johnson & Wichern, Chs. 8 and 9
Dropped from Lecture List:SEM: Path Analysis + CFA, Part 2: Mean Structures
Kline, Ch. 10McDonald & Ho (2002)
Dropped from Lecture List:SEM when Factors and Outcomes have Different DistributionsRupp, Templin, & Henson (2010), Ch. 3Skrondal & Rabe-Hesketh (2004), Ch. 4; Templin & Henson (2006)


Atkins, D. C., & Gallop, R. J. (2007). Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models. Journal of Family Psychology, 21, 726-735.

Bandalos, D. L., & Finney, S. J. (2010). Factor analysis: Exploratory and confirmatory. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 93-114). New York: Routledge.

Brown, T. A. (2006). Confirmatory factor analysis for applied research.
New York: Guilford.

Enders, C. K. (2010).Applied missing data analysis. New York, NY: Guilford.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.

Johnson, R. A. & Wichern, D. W. (2002). Applied multivariate statistical analysis (5th Ed.). Upper Saddle River, N.J.: Prentice-Hall.

Kaplan, D. (2009). Structural equation modeling: foundations and extensions (2nd Ed.). Thousand Oaks, CA: Sage.

Kline, R. B. (2002). Principles and practice of structural equation modeling (2nd Ed.). New York, NY: Guilford.

Lomax, R. G. (2010). Structural equation modeling: Multisample covariance and mean structures. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 385-395). New York: Routledge.

McDonald, R. P., & Ho, M.-H. R. (2002). Psychological Methods, 7, 64-82.

Mueller, R. O., & Hancock, G. R. (2010). Structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 371-383). New York: Routledge.

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.

Pajares, F., & Miller, M. D. (1994). Role of self-efficacy ad self-concept beliefs in mathematical problem solving: A path analysis.Journal of Educational Psychology, 86, 193-203.

Raykov, R. & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York: Routledge.

Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic measurement: Theory, methods, and applications. New York: Guilford Press.

Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. CRC Press.

Templin, J., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287-305.

Venables, W. N., Smith, D. M., & the R Core Team (2013). An introduction to R – Notes on R: A programming environment for data analysis and graphics (3.0.2 ed.). R Core Development team. Retrieved from