Y604 | 5986 | Dr. Ginette Delandshere

Prerequisites Students enrolled in the course should have completed Y502 or its equivalent [Y603 also recommended]. Students should also have prior knowledge of a computer system for accessing SAS or SPSS software or have experience with their WINDOWS applications. Course Content This course is based on the premise that the function of statistics is to formulate arguments for explaining comparative differences and relationships in data. This course focuses on the General Linear Model (GLM) and the various forms it takes in the multivariate context. The forms of the model will be discussed in relationship to the particular research questions for which they are appropriate. A range of multivariate statistical analysis procedures are considered to examine relationships between multiple variables (e.g., multiple dependent and/or independent variables) and comparisons will be made to their univariate equivalent. Principal component and factor analysis will also be covered as a way to reduce the number of measured variables to a smaller number of scores or underlying factors. Confirmatory factor analysis and the testing of simple structural models will also be introduced as well as structural equation modeling which allows for the examination of multiple relationships among variables and for taking into account measurement error (using measurement models for latent variables). The limitations (i.e., assumptions) and unresolved issues of each forms of the GLM will be examined. Students will learn to formulate research questions, to select appropriate analysis procedures, to conduct statistical analyses, and to report, interpret and write up narratives of the results in relation to the research questions and context. Objectives 1.To understand the nature and function of multivariate statistics and to use appropriate procedures to answer specific research questions 2.To carry out statistical analyses and to verify the underlying assumptions 3.To interpret computer printouts, report and write up results in relation to specific research contexts. Textbooks Two sets of textbooks have been ordered for this course and you can work from either set depending to your preference. Set 1 includes the Sage Publication series that cover the material for this course. Set 2 is a comprehensive textbook. Notes are also made available for each class session on SiteScape - http://ssf.indiana.edu/gdelands. Articles and chapters on reserve for this course can be found at http://129.79.35.24/coursepage.asp?cid=402 Set 1 Namboodiri, K. Matrix Algebra, An Introduction #38, Beverly Hills, CA: Sage Publications. Lewis-Beck, M.S. (1980). Applied Regression, An Introduction #22, Beverly Hills, CA: Sage Publications. Berry, W.D. & Feldman, S. (1985). Multiple Regression in Practice #50, Beverly Hills, CA: Sage Publications. Bray, J.H. & Maxwell, S.E. (1985). Multivariate Analysis of Variance #54, Beverly Hills, CA: Sage Publications. Klecka, W.R. (1980). Discriminant Analysis #19, Beverly Hills, CA: Sage Publications. Kim, J-O. & Mueller, C. W. (1978). Introduction to Factor Analysis #13, Beverly Hills, CA: Sage Publications, Inc. Kim, J-O. & Mueller, C. W. (1978). Factor Analysis: Statistical Methods and Practical Issues #14, Beverly Hills, CA: Sage Publications, Inc. Long, S. Confirmatory Factor Analysis, A Preface to LISREL #33. Beverly Hills, CA: Sage Publications, Inc. Long, S. Covariance Structure Models, An Introduction to LISREL #34. Beverly Hills, CA: Sage Publications, Inc. Set 2 Tabacknick, B. G. & Fidell, L. S. (2001). Using Multivariate Statistics, Fourth Edition. New York: Harper Collins Publishers, Inc. [T&F] Additional Reference Books Abelson, R. P. (1995). Statistics as Principled Argument. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Jöreskog K. G. (1993). Testing structural equation models. In K. A. Bollen, J. & Scott Long (Eds.) Testing structural equation models. Newburyk Park, CA: Sage Publications, Inc. Cooley, W. W. and Lohnes, P. R. (1985). Multivariate Data Analysis (2nd. ed.) New York, NY: John Wiley and Sons, Inc. Grimm, L. G. and Yarnold, P. R. (1995). Reading and Understanding Multivariate Statistics. DC: American Psychological Association. Hair, J. F., Anderson, R.E., Tatham, R.L. & Black, W.C. (1992). Multivariate Data Analysis, (3rd. ed.), Macmillan Publishing Company, New York, NY. Harris, R.J. (1985). A Primer of Multivariate Statistics, (2nd. ed.), Orlando, FL., Academic Press. Loehlin, J. C. (1992). Latent Variable Models: An Introduction to Factor, Path and Structural Analysis. (2nd. ed.), Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Morrison, D. F. (1989). Multivariate Statistical Methods, (2nd. ed.), New York, NY: McGraw-Hill Publishing Company Pedhazur, E.J. (1982). Multiple Regression in Behavioral Research: Explanation and Prediction, Second Edition. New York: Holt, Rinehart and Winston. Pedhazur, E. J. and Schmelkin, L. (1991). Measurement, Design, and Analysis: An Integrated Approach, Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Porter, T. M. (1995). Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press. Stevens, J. (1992). Applied Multivariate Statistics for the Social Sciences, (2nd. ed.), Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Tentative Course Outline and Schedule Week 1 Introduction and Overview 9/3-9/5 Students inventory of interests and experience with research, statistics and measurement o Taxonomy of Multivariate Techniques o Review of univariate analysis techniques o Assumptions underlying multivariate techniques Readings: [T&F: Chap. 1 & 2] Week 2-3 9/10 Making Claims with Statistics and Quantification o Function of Statistics - Meaning of Measurement [Abelson: Chap.1] - on reserve [Porter: Chap.4] - on reserve 9/12 Matrix Algebra 9/17-19 Operations, Order, Trace, Determinant o Sums of Square and Cross-Product (SSCP) o Inverse, Rank, Eigen Values, Eigen Vectors Readings: [T&F: Appendix A] Weeks 4-6 Multiple Regression 9/24-26 Basic model, assumptions 10/1-3 Analysis of the residuals 10/8-10 Outliers, Multicollinearity o Types of Multiple Regression o Part and Partial Correlation Readings:[T&F: Chap. 4 & 5] _REGRESSION ASSIGNMENT_ Weeks 7-8 Hotelling's T2 and Manova 10/15-17 10/22-24 Readings:[T&F: Chap. 9] Week 9-10 Discriminant Analysis 10/29-31 11/5-7 Readings:[T&F: Chap. 11] _EXAMINATION #1_ Weeks 11-12 Principal Component and Exploratory Factor Analysis 11/12-14 11/19-21 Readings:[T&F: Chap. 13] _EFA ASSIGNMENT_ Week 13-15 Structural Equation Modeling 11/26-28 Confirmatory Factor Analysis 12/3-5 Covariance Structure Models 12/10-12 Readings:[T&F: Chap. 14] [Jöreskog (1993)] Week 16 _EXAMINATION #2_ Course Assignments and Evaluation I expect all assigned readings to be done as specified in class for each session. Readings are assigned to complement in-class presentations and discussions, and to formalize understanding of the material. As an enrolled student, you will complete two written assignments, and two examinations. The assignments are designed to evaluate your conceptual understanding of the statistical analysis procedures, the computations involved, how the analyses relate to the research questions as well as the nature of the interpretations and inferences made based on the analysis of data. Short homework (e.g., matrix algebra exercises, reading research articles) will also be assigned for some of the topics and will have to be turned in on a timely basis. Each assignment will be evaluated according to a set of criteria that will be communicated as part of the assignment. The assignments, homework, and examinations will contribute to the final grade as follows: Regression assignment: 20% EFA assignment 20% Homework 10% Examination #1 25% Examination #2 25% Grading procedures are in accordance with the Bulletin for the Graduate Program of the School of Education. "Incomplete" will not be granted except for extremely unusual circumstances. Plagiarism will result in an automatic "F" for the course. Other Guidelines for the Course Students are encouraged to discuss the course material among themselves and to assist each other with data analysis. The written part of the assignments should, however, reflect individual student's work. Labs are designed to provide additional assistance to students and include: (1) assistance in setting up computer programs, (2) assistance with homework and assignments, and (3) review of concepts and procedures. Students should come to lab prepared to ask questions.