Upcoming Workshops:
Thank you for visiting my course notes. Here are some upcoming opportunities to learn from me and my colleagues in person:
Workshop  Dates  Location  Instructor 

Currently no workshops are planned 
Course Information
Instructor:  Dr. Jonathan Templin 
email:  jtemplin@ku.edu 
Office Phone:  7858645714 
Office:  614 Joseph R. Pearson Hall 
Office Hours:  To be announced 
Course Meeting Time:  Wednesdays from 1:30pm4:20pm 
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)  
Websites:  QuickR 
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
Date  Topic & Content Link  Required Reading  Optional Readings  Assignments Due 

1/21/2015  Introduction to Structural Equation Modeling  Venables, Smith, and the R Core Team (2014)  
1/28/2015  Univariate and Multivariate Distributions  Kline, Ch. 1; Gelman & Hill, Ch 2  Johnson & Wichern, Chs. 2 and 4  Project #1 
2/4/2015  Maximum and Robust Maximum Likelihood Estimation; Missing Data with (R)MLMaximum likelihood and Robust ML  Enders, Ch. 3  Enders, Ch. 5  HW #1 
2/11/2015  Multivariate Models; Model Fit Evaluation  Kline, Ch. 5  Kaplan, Ch. 5; Pajares & Miller (1994)  HW #1 Rev 
2/18/2015  Path Analysis  Kline, Ch. 6  HW #2  
2/25/2015  Introduction to Confirmatory Factor Analysis  Brown, Ch. 3  Hu & Bentler (1999); Bandalos & Finney (2010)  HW #2 Rev 
3/4/2015  Review Week  HW #3  
3/11/2015  No Class: Baby break  HW #3 Rev 

3/18/2015  No Class: Spring Break  None  
3/25/2015  Scale Building with CFA  Brown, Ch. 4  Brown, Ch. 5  
4/1/2015  Multifactor CFA and Differing Factor Covariance Models  Brown, Ch. 8  Kaplan, Ch. 3  HW #4 Project #2 
4/8/2015  Structural Equation Modeling: Path Analysis with Latent Variables  Kline, Ch. 8  Kaplan, Ch. 4; Mueller & Hancock (2010)  HW #4 Rev 
4/15/2015  No Class: Conference Travel Day  
4/22/2015  On Test Scores (Part 1)  HW #5  
4/29/2015  On Test Scores (Part 2): How to Properly Use Test Scores in Secondary Analyses  
5/6/2015  Invariance/DIF Testing & Generalized Models  Brown, Ch. 7  Kline, Ch. 11; Lomax (2010)  Project #3 (Optional) 
5/15/2015 (Last Day of Finals Week)  No ClassFinals Week  None  Project #3 HW #5 Rev 

Dropped from Lecture List:  SEM when Outcomes Have Different Distributions  Atkins and Gallop (2007)  Skrondal & RabeHesketh (2004), Chs. 1 and 2  
Dropped from Lecture List:  Exploratory Analyses with CFA; EFA and PCA  Brown, Ch. 5  Johnson & Wichern, Chs. 8 and 9  
Dropped from Lecture List:  SEM: Path Analysis + CFA, Part 2: Mean Structures  Kline, Ch. 10  McDonald & Ho (2002)  
Dropped from Lecture List:  SEM when Factors and Outcomes have Different Distributions  Rupp, Templin, & Henson (2010), Ch. 3  Skrondal & RabeHesketh (2004), Ch. 4; Templin & Henson (2006) 
References
Atkins, D. C., & Gallop, R. J. (2007). Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zeroinflated models. Journal of Family Psychology, 21, 726735.
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. 93114). 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, 155.
Johnson, R. A. & Wichern, D. W. (2002). Applied multivariate statistical analysis (5th Ed.). Upper Saddle River, N.J.: PrenticeHall.
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. 385395). New York: Routledge.
McDonald, R. P., & Ho, M.H. R. (2002). Psychological Methods, 7, 6482.
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. 371383). 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 selfefficacy ad selfconcept beliefs in mathematical problem solving: A path analysis.Journal of Educational Psychology, 86, 193203.
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., & RabeHesketh, 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, 287305.
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 http://cran.rproject.org/doc/manuals/Rintro.pdf.