REQUIRED TEXTBOOKS Hoyle, Rick H. (ed.) Handbook of Structural Equation Modeling. NY: Guilford Press. (HSEM)

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1 SOSC 5760 Structural Equation Modeling Spring 2016 PREREQUISITES INSTRUCTOR Sound background in graduate level of statistics Raymond S. Wong Office: Room 2373 ( ) Office Hours: 10:00-12:00 pm Tuesday and by appointment only TA: Ziyan Chen TIME/PLACE 3:00-5:50 pm Tuesday, Room 2612A (Lift 31-32) SUBJECT MATTER This course focuses on sociological (and other social-scientific) applications of path analysis and structural equation models. Following a review of basic ideas about the structure, interpretation, estimation, and inference in recursive causal models, the course will work through problems of specification and identification in latent-variable models and non-recursive models, using published examples where possible. A wide variety of structural equation models will be reviewed. They include: factor models, MIMIC models, recursive and non-recursive models (with and without latent variables), multiple group models (with or without latent mean structures), models of repeated measurement, models with missing data, the specification of latent structural models for ordinal data, latent growth curve modeling, and multilevel structural equation models. COURSE REQUIREMENT (1) About 6-8 weekly exercises will be assigned (50%). Grades will be based on the completion of the exercises. Answer to all assignments should be lucid, orderly, self-contained, and brief. Printed output from the analysis should also be turned in, but just handing the output will not be good enough. All work must be complete and turned in on time. (2) A self-contained research paper that utilizes the types of models introduced in the seminar (50%), that is, it should contain literature review, data, findings, and discussion sections. The paper is due at the end of the semester and the normal length is about pages (content only, not counting cover page, abstract, tables, figures, bibliography, and appendices). REQUIRED TEXTBOOKS Hoyle, Rick H. (ed.) Handbook of Structural Equation Modeling. NY: Guilford Press. (HSEM) Kline, Rex Principles and Practice of Structural Equation Modeling. Fourth Edition. New York: Guilford. (PPSEM). Stata Corp Stata Structural Equation Modeling Reference Manual, Release 14. College Station, TX: Stata Press. (SEM) Recommended Reference: Acock, Alan C Discovering Structural Equation Modeling Using Stata. Revised edition. College Station, TX: Stata Press. You can also visit the Guilford Press to download all computer files (EQS, LISREL, MPLUS, and Stata) for the examples used in the Kline s book: OTHER USEFUL TEXTBOOKS Bollen, Kenneth A Structural Equations with Latent Variables. N.Y.: John Wiley. Duncan, Otis Dudley Introduction to Structural Equation Models. New York: Academic Press.

2 SOSC Hancock, Gregory R. and Ralph O. Mueller (Eds.) Structural Equation Modeling: A Second Course. Greenwich. CT: Information Age Publishing, Inc. Kaplan, David Structural Equation Modeling: Foundations and Extensions. Second Edition. Thousand Oaks, CA: Sage Publications. Kelloway. E. Kevin Using LISREL for Structural Equation Modeling: A Researcher's Guide. Thousand Oaks, CA: Sage Publications. Loehlin, John Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis. Fourth Edition. Mahwah, NJ: Lawrence Erlbaum Associates. Long, J. Scott Confirmatory Factor Analysis: A Preface to LISREL. Beverly Hills: Sage Publications. Long, J. Scott Covariance Structure Models: An Introduction to LISREL. Beverly Hills: Sage Publications. Marcoulides, George A. and Randall E. Schumacker (eds.) Advanced Structural Equation Modeling: Issues and Techniques. Mahwah, NJ: Lawrence Erlbaum Associates. Mulaik, Stanley A Linear Causal Modeling with Structural Equations. Boca Raton, FL: Chapman & Hall/CRC. Schumacker, Randall E. and Richard G. Lomax A Beginner s Guide to Structural Equation Modeling. Third Edition. London: Routledge Academic. Schumacker, Randall E. and George A. Marcoulides (eds.) Interaction and Nonlinear Effects in Structural Equation Modeling. Mahwah, NJ: Lawrence Erlbaum Associates. All required textbooks, with the exception of Stata SEM Reference Manual, have been reserved at the HKUST Library (2-hour loan) or they can be accessed electronically. Other required readings, that is, other than textbooks, can be found in the course website: COMPUTATION We will be using two Stata 14 commands, sem and gsem, as well as the student version of LISREL9.2 (can be downloaded from SSI website: for statistical estimation. The SOSC Computing Lab has both Stata 14 and LISREL8.8. Note that the student version of LISREL has a few limitations: (1) Basic statistical analyses and data manipulation are restricted to a maximum of 20 variables; (2) Structural equation modeling is restricted to a maximum of 16 observed variables; (3) Multilevel modeling is restricted a maximum of 15 variables; (4) It can only import ASCII, tab-delimited and comma-delimited and some (not all) SPSS for Windows data files by using the Import Data option on the File menu; and (5) The Export Data option on the File menu is restricted to ASCII, tab-delimited and comma-delimited data files. However, the student version should be more than sufficient for pedagogical purposes. Depending on individual circumstances, the above limitations may or may not pose serious problems for your final paper. It should be noted that it is possible to use vast amount of computing time without making significant progress unless great care is exercised in preparing maximum likelihood estimation of structural equation models. This is particularly true for sem and gsem in Stata despite its ease of use, though the same problem applies to LISREL in many occasions as well. Therefore you should exercise extreme care in planning and preparing the specifications and apply reasonable start values when submitting your SEM jobs. You may consult the Stata SEM manual (p ) for helpful tips to resolve the non-convergence problems.

3 SOSC TENTATIVE SCHEDULE AND READINGS Starred (*) items are required readings and unless indicated otherwise, they are available from my website ( Week 1 (2 Feb) Review: Matrix Algebra and Multiple Regression * 1. Kline, PPSEM, Ch * 2. Matseuda, Ross L Key Advances in the History of Structural Equation Modeling. Pp in Handbook of Structural Equation Modeling, edited by Rich H. Hoyle. NY: Guilford Press. (HSEM) * 3. Mulaik, Stanley Linear Causal Modeling with Structural Equations. Chapter 2. Boca Raton, FL: Chapman & Hall. (Other useful resources from the web include: and 4. Steiger, James H Driving Fast in Reverse: The Relationship between Software Development, Theory, and Education in Structural Equation Modeling. Journal of the American Statistical Association 96(453): Mulaik, Stanley A History of Path Analysis. Pp in Encyclopedia in Statistics in Behavioral Science, Volume 2, edited by Brian S. Everitt and David C. Howell. Chichester, UK: John Wiley & Sons. 6. Bielby, William T. and Robert M. Hauser Structural Equation Models. Annual Review of Sociology 3: Week 2 (9 Feb) No Class (Chinese New Year) Week 3 (16 Feb) Introduction to Structural Equation Models * 1. Kline, PPSEM, Ch * 2. Kline, Rex B Assumptions in Structural Equation Modeling. Pp in Handbook of Structural Equation Modeling, edited by Rich H. Hoyle. NY: Guilford Press. (HSEM) * 3. Ho, Moon-ho Ringo, Stephen Stark, and Olexander Chernshenko Graphical Representation of Structural Equation Models Using Path Diagrams. Pp in Handbook of Structural Equation Modeling, edited by Rich H. Hoyle. NY: Guilford Press. (HSEM) * 4. Bollen, Kenneth A. and Rich H. Hoyle Latent Variables in Structural Equation Modeling. Pp in Handbook of Structural Equation Modeling, edited by Rich H. Hoyle. NY: Guilford Press. (HSEM) * 5. Mulaik, Stanley A. and Lawrence R. James Objectivity and Reasoning in Science and Structural Equation Modeling. Pp in Structural Equation Modeling: Concepts, Issues, and Applications, edited by Rick H. Hoyle. Thousand Oaks, CA: Sage Publications. 6. MacCallum, Robert C. and James T. Austin Applications of Structural Equation Modeling in Psychological Research. Annual Review of Psychology 51: Week 4 (23 Feb) Recursive Models * 1. Kline, PPSEM, Ch. 7. * 2. Alwin, Duane F. and Hauser, Robert M The Decomposition of Effects in Path Analysis. American Sociological Review 40:37-47.

4 SOSC * 3. Sobel, Michael Direct and Indirect Effects in Linear Structural Equation Models. Sociological Methods & Research 16(1): * 4. Bollen, Kenneth A Total, Direct, and Indirect Effects in Structural Equation Models. Sociological Methodology 17: * 5. Cheong, JeeWon and David P. MacKinnon Mediation/Indirect Effects in Structural Equation Modeling. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 6. Stata Corp SEM. (sem command) * 7. Jöreskog, Karl and Dag Sörbom LISREL 8: User's Reference Guide. Chicago, IL: Scientific Software International. 8. Du Toit, Stephen, Mathilda du Toit, Gerhard Mels, and Yan Cheng LISREL for Windows: LISREL Syntax Files. Lincolnwood, IL: Scientific Software International, Inc. 9. Du Toit, Stephen, Mathilda du Toit, Gerhard Mels, and Yan Cheng LISREL for Windows: SIMPLIS Syntax Files. Lincolnwood, IL: Scientific Software International, Inc. Week 5 (1 Mar) Confirmatory Factor Analysis: Identification and Estimation * 1. Kline, PPSEM, Ch. 9 & 13. * 2. Brown, Timothy A. and Michael T. Moore. Confirmatory Factor Analysis. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 3. Hoyle, Rick H Confirmatory Factor Analysis. Pp in Handbook of Applied Multivariate Statistics and Mathematical Modeling, edited by Howard E. A. Tinsley and Steven D. Brown. San Diego, CA: Academic Press. * 4. Joreskog and Sorbom, LISREL 8, Ch. 3 and Long, J. Scott Confirmatory Factor Analysis: A Preface to LISREL. Beverly Hills, CA: Sage Publications. Week 6 (8 Mar) Models with Unobservable Variables and MIMIC Models * 1. Kline, PPSEM, Ch. 10. * 2. Joreskog, Karl G. and Arthur S. Goldberger Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable. Journal of the American Statistical Association 70: * 3. Joreskog and Sorbom, LISREL 8, Ch Alwin, Duane F. and David J. Jackson Measurement Models for Response Errors in Surveys: Issues and Applications. Pp in Karl F. Schuessler (ed.), Sociological Methodology San Francisco, CA: Jossey-Bass. 5. Bielby, William T., Robert M. Hauser, and David L. Featherman Response Errors of Blacks and Nonblack Males in Models of the Intergenerational Transmission of Socioeconomic Status. American Journal of Sociology 82:

5 SOSC Hauser, Robert M., Shu-Ling Tsai, and William H. Sewell A Model of the Stratification Process with Response Error in Social and Psychological Variables. Sociology of Education 556: Week 7 (15 Mar) Modeling and Testing Strategies * 1. Kline, PPSEM, Ch * 2. West, Stephen G., Aaron B. Taylor, and Wei Wu Model Fit and Model Selection in Structural Equation Modeling. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 3. Chou, Chih-Ping and Jimi Huh Model Modification in Structural Equation Modeling. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) 4. Satorra, Albert and Peter M. Bentler Corrections to Test Statistics and Standard Errors in Covariance Structure Analysis. Pp in Latent Variables Analysis: Applications for Development Research, edited by Alexander von Eye and Clifford C. Clogg. Thousand Oaks, CA: Sage Publications. 5. Williams, Larry J Equivalent Models: Concepts, Problems, Alternatives. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) 6. Marsh, Herbert W., Kit-Tai Hau, and Zhonglin Wen In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler s (1999) Findings. Structural Equation Modeling: A Multidisciplinary Journal 11(3): Week 8 (22 Mar) Non-Recursive Models * 1. Hauser, Robert M. and Raymond Sin-Kwok Wong Sibling Resemblance and Inter-Sibling Effects in Educational Attainment. Sociology of Education 62: * 2. Kohn, Melvin L. and Carmi Schooler Job Conditions and Personality: A Longitudinal Assessment of Their Reciprocal Effects. American Journal of Sociology 87: Week 9 (29 Mar) No Class (Mid-Term Break) Week 10 (5 Apr) Multiple-Group SEM Models * 1. Kline, PPSEM, 16. * 2. Sorbom, Dag and Karl G. Joreskog The Use of LISREL in Sociological Model Building. Pp in David J. Jackson and Edgar F. Borgotta (eds.), Factor Analysis and Measurement in Sociological Research: A Multidimensional Perspective. Beverly Hills, CA: Sage. 3. Wolfe, Lee M High School Seniors' Reports of Parental Socioeconomic Status: Black-White Differences. Pp in Peter Cuttance and Russell Ecob (eds.), Structural Modeling by Example. Cambridge: Cambridge University Press. 4. Kluegel, James R., Royce Singleton, Jr. and Charles E. Starnes Subjective Class Identification: A Multiple Indicator Approach. American Sociological Review 42: Week 11 (12 Apr) SEM with Means and Intercepts * 1. Kline, PPSEM, Ch. 15 (pp ).

6 SOSC * 2. Green, Samuel B. and Marilyn S. Thompson A Flexible Structural Equation Modeling Approach for Analyzing Means. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 3. Faulbaum, Frank Intergroup Comparisons of Latent Means Across Waves. Sociological Methods & Research 15(3): Week 12 (19 Apr) Models with Missing Data * 1. Graham, John W. and Donna L. Coffman Structural Equation Modeling with Missing Data. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 2. Rovine, Michael J Latent Variables and Missing Data Analysis. Pp in Latent Variables Analysis: Applications for Development Research, edited by Alexander von Eye and Clifford C. Clogg. Thousand Oaks, CA: Sage Publications. * 3. Allison, Paul Estimation of Linear Models with Incomplete Data. Pp in Sociological Methodology 1987,edited by Clifford C. Clogg. Washington, D.C.: American Sociological Association. * 4. Allison, Paul D. and Robert M. Hauser Reducing Bias in Estimates of Linear Model by Remeasurement of a Random Subsample. Sociological Methods & Research 19: Enders, Craig K A Primer on Maximum Likelihood Algorithms for Use with Missing Data. Structural Equation Modeling: A Multidisciplinary Journal 8(1): Davey, Adam, Jyoti Savla and Zupei Luo Issues in Evaluating Model Fit with Missing Data. Structural Equation Modeling: A Multidisciplinary Journal 12(4): Enders, Craig K Applying the Bollen-Stine Bootstrap for Goodness-of-Fit Measures to Structural Equation Models with Missing Data. Multivariate Behavioral Research 37(3): Week 13 (26 Apr) Models for Ordinal Data/Interaction * 1. West, Stephen, John F. Finch, and Patrick J. Curran Structural Equation Models with Nonnormal Variables: Problems and Remedies. Pp in Structural Equation Modeling: Concepts, Issues, and Applications, edited by Rick H. Hoyle. Thousand Oaks, CA: Sage Publications. * 2. Mislevy, Robert J Recent Developments in the Factor Analysis of Categorical Variables. Journal of Educational Statistics 11:3-31. * 3. Bovaird, James A. and Natalie A. Koziol Measurement Models for Ordered-Categorical Indicators. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 4. Marsh, Herbert W., Zhonglin Wen, Benjamin Nagengast, and Kit-Tai Hau Structural Equation Models of Latent Interaction. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 5. Du Toit, Stephen, Mathilda du Toit, Gerhard Mels, and Yan Cheng LISREL for Windows: PRELIS User s Guide. Lincolnwood, IL: Scientific Software International, Inc. * 6. Stata Corp SEM. (gsem command)

7 SOSC Muthen, Bengt O A General Structural Equation Model with Dichotomous, Ordered Categorical and Continuous Latent Variable Indicators. Psychometrika 49: Muthen, Bengt O Dichotomous Factor Analysis of Symptom Data. Sociological Methods and Research 18: Browne, M Asymptotically Distribution-Free Methods for the Analysis of Covariance Structures. British Journal of Mathematical and Statistical Psychology 37: Week 14 (3 May) Latent Growth Curve Modeling, Interaction Effects, and Multilevel SEM * 1. Kline, PPSEM, Ch. 15 (pp ) & 17. * 2. Biesanz, Jeremy C Autoregressive Longitudinal Models. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 3. McArdle, John J Latent Curve Modeling of Longitudinal Growth Data. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) * 4. Steele, Fiona Structural Equation Modeling: Multilevel. Pp in Encyclopedia of Statistics in Behavioral Science, Volume 4, edited by Brian S. Everitt and David C. Howell. Chichester, UK: John Wiley & Sons. * 5. Rabe-Hesketh, Sophia, Anders Skrondal, and Xiaohui Zheng Multilevel Structural Equation Modeling. Pp in Handbook of Structural Equation Modeling, edited by Rick H. Hoyle. NY: Guilford Press. (HSEM) 6. Willet, John B. and Magaret K. Keiley Using Covariance Structure Analysis to Model Change Over Time. Pp in Handbook of Applied Multivariate Statistics and Mathematical Modeling, edited by Howard E. A. Tinsley and Steven D. Brown. San Diego, CA: Academic Press. 7. Willet, John B. and Kristen L. Bub Structural Equation Modeling: Latent Growth Curve Analysis. Pp in Encyclopedia of Statistics in Behavioral Science, Volume 4, edited by Brian S. Everitt and David C. Howell. Chichester, UK: John Wiley & Sons. 8. Bollen, Kenneth A. and Patrick J. Curran Latent Curve Models: A Structural Equation Perspective. Hoboken, NJ: John Wiley & Sons. 9. Preacher, Kristopher J., Aaron L. Wichman, Robert C. MacCallum, and Nancy E. Briggs Latent Growth Curve Modeling. Thousand Oaks, CA: Sage Publications. 10. Hox, Joop and Reinoud D. Stoel Multilevel and SEM Approaches to Growth Curve Modeling. Pp in Encyclopedia of Statistics in Behavioral Science, volume 3, edited by Brian S. Everitt and David C. Howell. Chichester, UK: John Wiley & Sons. 11. Bauer, Daniel Estimating Multilevel Linear Models as Structural Equation Models. Journal of Behavioral Statistics 28(2):