Introduction to Structural Equation Modeling, Spring 2012 (UGA)


Upcoming Workshops:

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

WorkshopDatesLocationInstructor
Diagnostic MeasurementMay 22-25, 2017Omni Hilton Head Oceanfront Resort, Hilton Head Island, South CarolinaJonathan Templin
Introduction to Longitudinal Multilevel ModelsMay 30-June 2, 2017Omni Hilton Head Oceanfront Resort, Hilton Head Island, South CarolinaLesa Hoffman

Introduction to Structural Equation Modeling (ERSH 8370); Spring 2012, University of Georgia

Instructor: Dr. Jonathan Templin
Email: jtemplin@uga.edu Phone: 706-680-7148
Classroom: 119 Aderhold Hall Office: 570B Aderhold Hall
Time: 1:25-4:25 Wednesdays (3 credits) Office Hours: 11:00am-1:00pm Tuesdays (in Room 228 Aderhold) and by appointment
Downloadable Course Syllabus: Click here to download
Course Facebook Page: Introduction to Structural Equation Modeling (ERSH 8750) Spring 2012
Course Materials Repository: Dropbox

Tentative Schedule of Course Topics and Assignments:

Week Date Course Materials Readings
1 1/11 Course Introduction – Syllabus, Review of Regression, ANOVA, and Measurement; Introduction to Mplus

Lecture #1 Slides
Lecture #1 Mplus Example Files (Zipped Folder)

Lecture #1 Audio (Part 1)
Lecture #1 Audio (Part 2)
Lecture #1 Audio (Part 3)

Assignment #1: Due January 18 at 4pm
Data File for Assignment #1

#1, #2, #3
2 1/18 Introduction to Matrix Algebra/Multivariate Normal Distribution

Lecture #2 Slides
Lecture #2 Mplus Example Files (Zipped Folder)

Lecture #2 Audio (Part 1)
Lecture #2 Audio (Part 2)
Lecture #2 Audio (Part 3)

Assignment #2: Due January 25 at 1:25pm
Data File for Assignment #2

#4, #5
3 1/25 Introduction to Maximum Likelihood and Missing Data

Lecture #3 Slides
Lecture #3 Mplus Example Files (Zipped Folder)

Assignment #3: Due February 1 at 1:25pm
Data File for Assignment #3

#6,
#7,
#8,
#9
4a 2/1 Path Analysis Part 1 (through slide 68)

Lecture #4 Slides
Lecture #4 Mplus Example Files (Zipped Folder)

Lecture #4 Audio (Part 1)
Lecture #4 Audio (Part 2)
Lecture #4 Audio (Part 3)

#10,
#11
4b 2/8 Path Analysis Part 2 (slide 69 to end of Lecture 4a)

Lecture #4b Audio (Part 1)
Lecture #4b Audio (Part 2)
Lecture #4b Audio (Part 3)

Assignment #4: Due February 15 at 1:25pm
Data File for Assignment #4

#10a,
#11a
5 2/15 Confirmatory Factor Analysis #1: Concepts/Identification/Model Fit

Lecture #5 Slides
Lecture #5 Mplus Example Files (Zipped Folder)

#12,
#13,
#14
6 2/22 Building a Scale with Confirmatory Factor Analysis
Lecture #6 Slides
Lecture #6 Mplus Example Files (Zipped Folder)Lecture #6 Audio (Part 1)
Lecture #6 Audio (Part 2)
Lecture #6 Audio (Part 3)
#18,
#19,
#20
7 2/29 Comparing Classical Test Theory with CFA -and- How To Use Test Scores in Secondary Analyses

Lecture #7 Slides
Lecture #7 Mplus Example Files (Zipped Folder)

Lecture #7 Audio (Part 1)
Lecture #7 Audio (Part 2)
Lecture #7 Audio (Part 3)

Assignment #5: Due March 7 at 1:25pm

Data File for Assignment #5: On Dropbox

8 3/7 Multifactor Confirmatory Factor Analysis

Lecture #8 Slides
Lecture #8 Mplus Example Files (Zipped Folder)

Lecture #8 Audio (Part 1)
Lecture #8 Audio (Part 2)
Lecture #8 Audio (Part 3)

9 3/21 Principal Components Analysis and Exploratory Factor Analysis, with Comparisons to CFA

Lecture #9 Slides
Lecture #9 Mplus Example Files (Zipped Folder)

Assignment #6: Due March 28 at 1:25pm

Lecture #9 Audio (Part 1)
Lecture #9 Audio (Part 2)
Lecture #9 Audio (Part 3)

Data File for Assignment #6: On Dropbox

10 4/4 Robust Estimation and Generalized Models for Non-Normal Data

Lecture #10 Slides
Lecture #10 Mplus Example Files (Zipped Folder)

Lecture #10 Audio (Part 1)
Lecture #10 Audio (Part 2)
Lecture #10 Audio (Part 3)

11 4/18 Measurement Invariance and Multiple Group Analyses

Lecture #11 Slides
Lecture #11 Mplus Example Files (Zipped Folder)

12 4/25 Putting it Together: Structural Equation Models

Lecture #12 Slides

Course Readings:

Week Reference
1
  • Mplus introduction website: http://www.ats.ucla.edu/stat/mplus/seminars/IntroMplus/default.htm
  • Chapter 1: Introduction. Kline (2005). Principles and practice of structural equation modeling (2nd Ed.). New York: Guilford.
  • Chapter 1: Historical foundations of structural equation modeling for continuous and categorical latent variables. Kaplan, D. (2009). Structural equation modeling: foundations and extensions (2nd Ed.). Thousand Oaks, CA: Sage.
2
  • Chapter 2: Matrix algebra and random vectors. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall. (p. 84-100).
  • Chapter 4: The multivariate normal distribution. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall. (p. 149-177).
3
  • Chapter 1: An introduction to missing data. Enders, C. K. (2010). Applied Missing Data Analysis. New York: Guilford.
  • Chapter 2: Traditional methods for dealing with missing data. Enders, C. K. (2010). Applied Missing Data Analysis. New York: Guilford.
  • Chapter 3: An introduction to maximum likelihood estimation. Enders, C. K. (2010). Applied Missing Data Analysis. New York: Guilford.
  • Chapter 4: Maximum likelihood missing data handling. Enders, C. K. (2010). Applied Missing Data Analysis. New York: Guilford.
4
  • Chapter 5: Introduction to path analysis. Kline (2005). Principles and practice of structural equation modeling (2nd Ed.). New York: Guilford.
  • Chapter 2: Path Analysis. Kaplan (2009). Structural equation modeling: foundations and extensions (2nd Ed.). Thousand Oaks, CA: Sage.
  • Chapter 6: Details of path analysis. Kline (2005). Principles and practice of structural equation modeling (2nd Ed.). New York: Guilford.
  • Pajares, F., & Miller, M. D. (1994). Role of
    self-efficacy and self-concept beliefs in mathematical
    problem solving. Journal of Educational Psychology, 86, 193-203.
5
  • Chapter 7: Measurement models and confirmatory factor analysis. Kline (2005). Principles and practice of structural equation modeling (2nd Ed.). New York: Guilford
  • Chapter 3: Introduction to confirmatory factor analysis. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
  • Chapter 4: Confirmatory factor analysis. Raykov, T., & Marcoulides, G. A. (2006). A first course in structural equation modeling (2nd Ed.). New York: Taylor & Francis.
6
  • Chapter 4: Specification and interpretation of confirmatory factor models. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
  • Chapter 5: Confirmatory factor analysis model revision and comparison. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: 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.
7
  • Chapter 3: Factor analysis. Kaplan, D. (2009). Structural equation modeling: foundations and extensions (2nd Ed.). Thousand Oaks, CA: Sage.
  • Chapter 6: Reliability. Raykov, R. & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York: Routledge.
  • Chapter 7: Procedures for estimating reliability. Raykov, R. & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York: Routledge.
8
  • Chapter 11: Multi-Sample SEM. Kline (2005). Principles and practice of structural equation modeling (2nd Ed.). New York: Guilford.
  • Chapter 7: Confirmatory factor analysis with equality constraints, multiple groups, and mean structures. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
9
  • Chapter 4: Structural equation modeling in single and multiple groups. Kaplan, D. (2009). Structural equation modeling: foundations and extensions (2nd Ed.). Thousand Oaks, CA: Sage.
  • Chapter 8: Other types of confirmatory factor analysis models: higher-order factor analysis, scale reliability evaluation, and formative indicators. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
  • Chapter 8: Models with structural and measurement components. Kline (2005). Principles and practice of structural equation modeling (2nd Ed.). New York: Guilford.
  • Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7(3), 461-483.
10
  • Chapter 5: Structural regression models. Raykov, T., & Marcoulides, G. A. (2006). A first course in structural equation modeling (2nd Ed.). New York: Taylor & Francis.
  • DeShon, R. P. (1998). A cautionary note on measurement error corrections in structural equation models. Psychological Methods, 3, 412-423.
  • McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64-82.
11
  • MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.
  • MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test the significance of the mediated effect. Psychological Methods, 7, 83-104.
  • Edwards, J. R., & Lambert L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12, 1-22.
  • James, L. R.,Mulaik, S. A., & Brett, J. M. (2006). A tale of two methods. Organizational Research Methods, 9, 233-244.
12
  • Chapter 6: Latent change analysis. Raykov, T., & Marcoulides, G. A. (2006). A first course in structural equation modeling (2nd Ed.). New York: Taylor & Francis.
  • Chapter 10: Mean structure and latent growth models. Kline (2005). Principles and practice of structural equation modeling (2nd Ed.). New York: Guilford.
  • Hancock, G. R. (1997). Structural equation modeling methods of hypothesis testing of latent variable means. Measurement and Evaluation in Counseling and Development, 30, 91 – 105.
  • Hancock, G. R., Kuo, W-L., Lawrence, F. R. (2001). An illustration of second-order latent growth models. Structural Equation Modeling, 8, 470-489
13
  • Chapter 1: Introduction (p. 22-37). Raykov, T., & Marcoulides, G. A. (2006). A first course in structural equation modeling (2nd Ed.). New York: Taylor & Francis.
  • Chapter 5: Improving the accuracy of maximum likelihood analyses. Enders, C. K. (2010). Applied Missing Data Analysis. New York: Guilford.
14
  • Chapter 1: The omni-presence of latent variables. Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall.
  • Chapter 2: Modeling different response processes. Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall.
  • Chapter 9: Data issues in confirmatory factor analysis: missing, non-normal, and categorical data. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.