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. 2nd ed. Mahwah, NJ: Lawrence Erlbaum.
Snijders, Tom A. B. and Roel J. Bosker. 1999. Multilevel Analysis. Thousand Oaks, CA: Sage.
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
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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%
B
83 – 86%
F
0 – 59%
Task
10 homeworks
SEM mini-project
HLM mini-project
AB-
90 – 92%
80 – 82%
Due date
February 1-May 15
March 15
May 15
B+
C
87 – 89%
60 – 79%
Percentage of grade
50 (5 each)
25
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
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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. 2nd 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. 2nd 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. 2nd 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. 2nd 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. 16794 in A Beginner’s Guide to Structural Equation Modeling. 2nd 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. 2nd ed. Mahwah, NJ: Lawrence
Erlbaum.
SC 705 Advanced Statistics
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Schumacker, Randall E. and Richard G. Lomax. 2004. “SEM Applications Part II.” Pp. 354-405
in A Beginner’s Guide to Structural Equation Modeling. 2nd 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. 2nd 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: 10529.
*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
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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. 2nd 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. 1337 in Multilevel Analysis. Thousand Oaks, CA: Sage.
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
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Snijders, Tom A. B. and Roel J. Bosker. 1999. “The Random Intercept Model.” Pp. 38-66 in
Multilevel Analysis. Thousand Oaks, CA: Sage.
*** 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 Multilevel Analysis. Thousand Oaks, CA: Sage.
Snijders, Tom A. B. and Roel J. Bosker. 1999. “The Hierarchical Linear Model.” Pp. 67-85 in
Multilevel Analysis. Thousand Oaks, CA: Sage.
Snijders, Tom A. B. and Roel J. Bosker. 1999. “How Much Does the Model Explain?” Pp. 99109 in Multilevel Analysis. Thousand Oaks, CA: Sage.
Snijders, Tom A. B. and Roel J. Bosker. 1999. “Testing and Model Specification.” Pp. 86-98 in
Multilevel Analysis. Thousand Oaks, CA: Sage.
*** 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. 2nd ed. Thousand Oaks, CA: Sage.
Snijders, Tom A. B. and Roel J. Bosker. 1999. “Discrete Dependent Variables.” Pp. 207-38 in
Multilevel Analysis. Thousand Oaks, CA: Sage.
SC 705 Advanced Statistics
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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 ThreeLevel 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. 2nd 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.