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., PrenticeHall. (p. 84100).
 Chapter 4: The multivariate normal distribution. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., PrenticeHall. (p. 149177).

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
selfefficacy and selfconcept beliefs in mathematical
problem solving. Journal of Educational Psychology, 86, 193203.

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, 155.

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: MultiSample 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: higherorder 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), 461483.

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, 412423.
 McDonald, R. P., & Ho, M.H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 6482.

11 
 MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593614.
 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, 83104.
 Edwards, J. R., & Lambert L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12, 122.
 James, L. R.,Mulaik, S. A., & Brett, J. M. (2006). A tale of two methods. Organizational Research Methods, 9, 233244.

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, WL., Lawrence, F. R. (2001). An illustration of secondorder latent growth models. Structural Equation Modeling, 8, 470489

13 
 Chapter 1: Introduction (p. 2237). 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 omnipresence of latent variables. Skrondal, A. & RabeHesketh, 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. & RabeHesketh, 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, nonnormal, and categorical data. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
