Please note that this syllabus should be regarded as only a general guide to the course. The instructor may have changed specific course content and requirements subsequent to posting this syllabus. Last Modified: 15:26:44 01/07/2010 SC 705: Advanced Statistics Spring 2010 Monday 9:30 am-12:00 pm 531 McGuinn Hall Professor: Sara Moorman Office: 404 McGuinn Hall Office hours: Mondays 1:00-3:00 pm or by appointment E-mail: Sara.Moorman.1@bc.edu (please e-mail me from your BC account, and include SC705 in the subject line) Phone: 617-552-4209 About the Course This applied course is designed for students in sociology, education, nursing, organizational studies, political science, psychology, or social work with a prior background in statistics at the level of SC 703: Multivariate Statistics. It assumes a strong grounding in multivariate regression analysis. The major topics of the course will include hierarchical linear modeling and structural equation modeling. We will use HLM and LISREL to conduct the analyses. Required Readings Texts to purchase: Jöreskog, Karl and Dag Sörbom. 1996. LISREL 8: User s Reference Guide. Lincolnwood, IL: Scientific Software International. Jöreskog, Karl and Dag Sörbom. 2002. PRELIS 2: User s Reference Guide. Lincolnwood, IL: Scientific Software International. Kreft, Ita and Jan de Leeuw. 2006. Introducing Multilevel Modeling. Thousand Oaks, CA: Sage. Schumacker, Randall E. and Richard G. Lomax. 2004. A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. Snijders, Tom A. B. and Roel J. Bosker. 1999. Course reserves online: Access readings preceded by an asterisk (*) as.pdf files through the library website (http://www.bc.edu/libraries/) or through the link on the course Blackboard page (https://cms.bc.edu/webct/entrypageins.dowebct).
SC 705 Advanced Statistics page 2 of 7 Required Software This course requires the use of the programs LISREL and HLM. Both are available on the computers in the Sociology graduate student lounge. For use on your own Windows computer, student versions are freely downloadable from Scientific Software International: http://www.ssicentral.com/lisrel/student.html and http://www.ssicentral.com/hlm/student.html. Assessment Grading scale A 93 100% A- 90 92% B+ 87 89% B 83 86% B- 80 82% C 60 79% F 0 59% Task Due date Percentage of grade 10 homeworks February 1-May 15 50 (5 each) SEM mini-project March 15 25 HLM mini-project May 15 25 Weekly homework: At the end of class each week I will provide ~3 problems based on the lecture and readings for the day. The problems will be due, in hard copy or e-mailed to me in.pdf format, no later than the beginning of the next class session. I will not grade late work. Mini-projects: The two major topics in this course, SEM and HLM, are usually distinct in application. Therefore, at the end of each half of the course, you will complete a mini-project. These assignments will include ~6 related problems that will draw upon material from all weeks we spent on SEM or HLM. Academic Honesty Your work must be your words and ideas. When writing papers, use quotation marks around someone else s exact words and identify whose words they are. If you come across a good idea, by all means use it in your writing, but be sure to acknowledge whose idea it is. Do not allow another student to copy your work. Failure to comply will result in (a) automatic failure of the assignment, and (b) a report to the Dean and the Committee on Academic Integrity. For further information, please review the College s policies on academic integrity here: http://www.bc.edu/offices/stserv/academic/resources/policy.html#integrity
SC 705 Advanced Statistics page 3 of 7 Schedule January 25: Introduction to Structural Equation Modeling *Bentler, P. M., and Chih-Ping Chou. 1987. Practical Issues in Structural Equation Modeling. Sociological Methods & Research 16: 78 117. Schumacker, Randall E. and Richard G. Lomax. 2004. Introduction. Pp. 1-12 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. Schumacker, Randall E. and Richard G. Lomax. 2004. Path Models. Pp. 149-66 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. *Wolfle, Lee M. 2003. The Introduction of Path Analysis to the Social Sciences, and Some Emergent Themes: An Annotated Bibliography. Structural Equation Modeling 10: 1 34. February 1: Estimating Structural Equation Models in LISREL *Olsson, Ulf H., Tron Foss, Sigurd V. Troye, and Roy D. Howell. 2000. The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality. Structural Equation Modeling 7: 557 95. *Rigdon, Edward E. 1995. A Necessary and Sufficient Identification Rule for Structural Models Estimated in Practice. Multivariate Behavioral Research 30: 359 83. Schumacker, Randall E. and Richard G. Lomax. 2004. Matrix Approach to Structural Equation Modeling. Pp. 406-56 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. Schumacker, Randall E. and Richard G. Lomax. 2004. SEM Basics. Pp. 61-78 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. February 8: Factor Analysis and Multi-group Models *Rigdon, Edward E. 1994. Demonstrating the Effects of Unmodeled Random Measurement Error. Structural Equation Modeling 1: 375 80. Schumacker, Randall E. and Richard G. Lomax. 2004. Confirmatory Factor Models. Pp. 167-94 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. Schumacker, Randall E. and Richard G. Lomax. 2004. SEM Applications Part I. Pp. 323-53 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum.
SC 705 Advanced Statistics page 4 of 7 Schumacker, Randall E. and Richard G. Lomax. 2004. SEM Applications Part II. Pp. 354-405 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. February 15: Fit Statistics, Model Modification, and Alternative Models *Browne, Michael W. and Robert Cudeck. 1992. Alternative Ways of Assessing Model Fit. Sociological Methods & Research 21: 230 58. *MacCallum, Robert C., Duane T. Wegener, Bert N. Uchino, and Leandre R. Fabrigar. 1993. The Problem of Equivalent Models in Applications of Covariance Structure Analysis. Psychological Bulletin 114: 185 99. *MacCallum, Robert C., Mary Roznowski, and Lawrence B. Necowitz. 1992. Model Modifications in Covariance Structure Analysis: The Problem of Capitalization on Chance. Psychological Bulletin 111: 490 504. Schumacker, Randall E. and Richard G. Lomax. 2004. Model Fit. Pp. 79-122 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. February 22: Troubleshooting *Chen, Feinian, Kenneth A. Bollen, Pamela Paxton, Patrick J. Curran, and James B. Kirby. 2001. Improper Solutions in Structural Equation Models: Causes, Consequences, and Strategies. Sociological Methods & Research 29: 468 508. *Saris, Willem, Albert Satorra, and Dag Sörbom. 1987. The Detection and Correction of Specification Errors in Structural Equation Models. Sociological methodology 17: 105-29. *Rindskopf, David. 1984. Structural Equation Models: Empirical Identification, Heywood Cases and Related Problems. Sociological Methods & Research 13: 109 19. *Wothke, Werner. 1993. Nonpositive Definite Matrices in Structural Equation Modeling. Pp. 181-204 in K. A. Bollen & J. S. Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage. *** March 1: Spring vacation, no class***
SC 705 Advanced Statistics page 5 of 7 March 8: Criticisms of Structural Equation Modeling, or Troubleshooting Journal Reviewers * Boomsma, Anne. 2000. Reporting Analyses of Covariance Structures. Structural Equation Modeling 7: 461-83. * Hoyle, Rick H. and Abigail T. Panter. 1995. Writing about Structural Equation Models. Pp. 158-176 in Structural Equation Modeling: Concepts, Issues, and Applications, edited by R. H. Hoyle. Thousand Oaks, CA: Sage. * McDonald, Roderick P. and Moon-Ho Ringo Ho. 2002. Principles and Practice in Reporting Structural Equation Analyses. Psychological Methods 7: 64 82. Schumacker, Randall E. and Richard G. Lomax. 2004. Reporting SEM Research. Pp. 230-59 in A Beginner s Guide to Structural Equation Modeling. 2 nd ed. Mahwah, NJ: Lawrence Erlbaum. March 15: Introduction to Hierarchical Linear Modeling Kreft, Ita and Jan de Leeuw. 2006. Introducing Multilevel Modeling. Thousand Oaks, CA: Sage. March 22: Between- and Within-Group Variance *Bliese, Paul D., Ronald R. Halverson, and Chester A. Schriesheim. 2002. Benchmarking Multilevel Methods in Leadership: The Articles, the Model, and the Data Set. The Leadership Quarterly 13: 3-14. *Castro, Stephanie L. 2002. Data Analytic Methods for the Analysis of Multilevel Questions: A Comparison of Intraclass Correlation Coefficients, rwg(j), Hierarchical Linear modeling, Within- and Between-Analysis, and Random Group Resampling. The Leadership Quarterly 13: 69-93. *Markham, Steven E. and Ronald R. Halverson. 2002. Within- and Between-Entity Analyses in Multilevel Research: A Leadership Example Using Single Level Analyses and Boundary Conditions (MRA). The Leadership Quarterly 13: 35-52. Snijders, Tom A. B. and Roel J. Bosker. 1999. Statistical Treatment of Clustered Data. Pp. 13-37 in March 29: Centering; Random Intercept Models *Kreft, Ita G. G., Jan de Leeuw, and Leona S. Aiken. 1995. The Effect of Different Forms of Centering in Hierarchical Linear Models. Multivariate Behavioral Research 30: 1-21. *Paccagnella, Omar. 2006. Centering or Not Centering in Multilevel Models: The Role of the Group Mean and the Assessment of Group Effects. Evaluation Review 30:66-85. Park, Sunhee and Eileen T. Lake. 2005. Multilevel Modeling of a Clustered Continuous Outcome: Nurses Work Hours and Burnout. Nursing Research 54: 406-13.
SC 705 Advanced Statistics page 6 of 7 Snijders, Tom A. B. and Roel J. Bosker. 1999. The Random Intercept Model. Pp. 38-66 in *** April 5: Easter Monday, no class*** April 12: Random Slope and Intercept Models Snijders, Tom A. B. and Roel J. Bosker. 1999. Assumptions of the Hierarchical Linear Model. Pp. 120-39 in Snijders, Tom A. B. and Roel J. Bosker. 1999. The Hierarchical Linear Model. Pp. 67-85 in Snijders, Tom A. B. and Roel J. Bosker. 1999. How Much Does the Model Explain? Pp. 99-109 in Snijders, Tom A. B. and Roel J. Bosker. 1999. Testing and Model Specification. Pp. 86-98 in *** April 19: Patriots Day, no class*** April 26: Modeling Categorical Outcomes *Agresti, Alan, James G. Booth, James P. Hobert, and Brian Caffo. 2000. Random Effects Modeling of Categorical Response Data. Sociological Methodology 30: 27-80. *Guo, Guang and Hongxin Zhao. 2000. Multilevel Modeling for Binary Data. Annual Review of Sociology 26: 441-62. *Raudenbush, Stephen W. and Anthony S. Bryk. 2002. Hierarchical Generalized Linear Models. Pp. 291-335 in Hierarchical Linear Models: Applications and Data Analysis Methods. 2 nd ed. Thousand Oaks, CA: Sage. Snijders, Tom A. B. and Roel J. Bosker. 1999. Discrete Dependent Variables. Pp. 207-38 in
SC 705 Advanced Statistics page 7 of 7 May 3: Longitudinal Analysis *Bryne, Barbara M. and Gail Crombie. 2003. Modeling and Testing Change: An Introduction to the Latent Growth Curve Model. Understanding Statistics 2: 177-203. *Chapman, Robin S., Linda J. Hesketh, and Doris J. Kistler. 2002. Predicting Longitudinal Change in Language Production and Comprehension in Individuals With Down Syndrome: Hierarchical Linear Modeling. Journal of Speech, Language, and Hearing Research 45: 902-15. *Hertzog, Christopher, Ulman Lindenberger, Paolo Ghisletta, and Timo von Oertzen. 2006. On the Power of Multivariate Latent Growth Curve Models to Detect Correlated Change. Psychological Methods 11: 244-52. Snijders, Tom A. B. and Roel J. Bosker. 1999. Longitudinal Data. Pp. 166-99 in Multilevel Analysis. Thousand Oaks, CA: Sage. May 10: Three Level Models * Bryk, Anthony S. and Stephen W. Raudenbush. 1988. Toward a More Appropriate Conceptualization of Research on School Effects: A Three-Level Hierarchical Linear Model. American Journal of Education 97: 65-108. * Guo, Shenyang and David Hussey. 1999. Analyzing Longitudinal Rating Data: A Three- Level Hierarchical Linear Model. Social Work Research 23: 258-69. *Raudenbush, Stephen W. and Anthony S. Bryk. 2002. Three Level Models. Pp. 228-51 in Hierarchical Linear Models: Applications and Data Analysis Methods. 2 nd ed. Thousand Oaks, CA: Sage. *Roderick, Melissa, Brian A. Jacob, and Anthony S. Byrk. 2002. The Impact of High-Stakes Testing in Chicago on Student Achievement in Promotional Gate Grades. Educational Evaluation and Policy Analysis 24: 333-57.