Y604 | 5839 | Dr. Joanne Peng

Objectives 1. To understanding the logic behind multivariate statistics (MS) and to apply selected MS procedures to well-defined social sciences and educational research questions. 2. To be able to objectively evaluate manuscripts in which selected MS analyses were employed. 3. To carry out MS analyses of data by SAS software in the Windows environment and to interpret the analysis results in written reports. 4. To understand selected articles which address unresolved theoretical issues in MS. These issues largely deal with statistical assumptions or the adequacy of applying certain models/procedures to real-world data. Textbooks Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon. Namboodiri, K. Matrix algebra. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-038. Thousand Oaks, CA: Sage. Lewis-Beck, M.S. (1980). Applied regression: An introduction. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-022. Thousand Oaks, CA: Sage. Berry, W.D. & Feldman, S. (1985). Multiple regression in practice. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-050. Thousand Oaks, CA: Sage. Thompson, B. (1984). Canonical correlation analysis: Uses and interpretation. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-047. Thousand Oaks, CA: Sage. Bray, J.H. & Maxwell, S.E. (1985). Multivariate analysis of variance. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-054. Thousand Oaks, CA: Sage. Klecka, W.R. (1980). Discriminant analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-019. Thousand Oaks, CA: Sage. Kim, J-O. & Mueller, C. W. (1978). Introduction to factor analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-013. Thousand Oaks, CA: Sage. Kim, J-O. & Mueller, C. W. (1978). Factor analysis: Statistical methods and practical Issues. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-014. Thousand Oaks, CA: Sage. Kruskal, J. B. & Wish, M. (1978). Multidimensional Scaling. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-011. Thousand Oaks, CA: Sage. Aldenderfer, M. S. & Blashfield, R. K. (1984). Cluster analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-044. Thousand Oaks, CA: Sage. Review Books/Website Kirk, R. E. (1999). Statistics-An Introduction (4th ed.,), Orlando, FL: Harcourt Brace & Company. Hays, W.L. (1988). Statistics for the Social Sciences (4th ed.,), New York, NY: Holt, Rinehart and Winston, Inc. http://psychology.wadsworth.com/workshops/workshops.html http://trochim.human.cornell.edu/kb/regrmean.htm Prerequisites The primary prerequisite to this course is an equivalent of a second course (e.g., Y502) in applied statistics which covered ordinary least squares regression models and analysis of variance in factorial designs. An aptitude for mathematical analysis and SAS programming is beneficial, though not required. Deficiencies in particular areas will be remedied in lab sessions. Tentative Course Schedule Week Topics Readings from Tabachnick & Fidell Readings from Sage 1 Course Orientation Introduction to Matrix Algebra Chapters 1 and 2 Appendix A #38 2 Matrix Algebra (continued) Appendix A #38 Assignment #1 on matrix algebra, due in one week. 3-5 Multiple Regression Chapters 5, 4 , and 17 #22 & #50 Assignment #2 on multiple regression, due in one week. 6 Canonical Correlation Chapter 6 #47 7-8 Hotelling's T2 and MANOVA Chapter 9 #54 Assignment #3-critique of an article using canonical correlation, or Hotelling's T2, or MANOVA, due in one week. 9-10 Discriminant Function Analysis Chapter 11 #19 11-12 Principal Components & Factor Analysis Chapter 13 #13 & #14 Assignment #4 on factor analysis, due in one week. 13 Multidimensional Scaling #11 14 Cluster Analysis #44 15 Wrap up and course evaluation Assignment #5-critique of an article using discriminant function analysis, multidimensional scaling, or cluster analysis, due in one week. Each student is required to complete five assignments. The specific instruction on assignments will be announced when the assignments are assigned in class. Labs and the AI Activities that typically take place in the labs include, but are not limited to, (a) clarification of lectures, (b) answering questions related to assignments, readings, or any administrative aspect of this course, and (c) instruction on basic SAS command language and execution. The attendance of labs is optional; but you alone are responsible for the consequences of missing the labs. You should have a valid student account on the university computing system. My e-mail address is PENG, the AI's e-mail address is WLIM (Woong Lim). Woong is located in ED 4008; his phone number is 856-8585 and office hours are 1-2 p.m. on Tuesdays and 3-4 p.m. on Thursdays. My office hours will be announced later in an e-mail. If you are unable to see us during any of our office hours, please see me as soon as possible. I want to make sure that we are both accessible to you when you need our help. Grading System Student's performance in this course is evaluated on the basis of five required assignments. Each assignment contributes 20% toward the final grade. The final course grade, expressed in letters, is determined for each student according to the following mastery levels: 85% mastery or above -- A 80% to 84% -- A- 75% to 79% -- B + 70% to 74% -- B 65% to 69% -- B- 60% to 64% -- C + 55% to 59% -- C 50% to 54% -- C- The letter grades should be interpreted according to the School of Education grading policy as follows: A Outstanding achievement. B- Fair achievement. A- Excellent achievement. C+ Not wholly satisfactory achievement. B+ Very good achievement. C Marginal achievement. B Good achievement C- Unsatisfactory achievement. Incomplete will be given only for a legitimate reason as outlined in the university's Academic Guide, and only after a conference between the instructor and the student. Throughout this course, you may contest every grade awarded to you within 48 hours of receiving such a grade. Once this "statute of limitation" has passed, it is assumed that you willingly accept the grade(s) assigned without further dispute. Academic Honesty and Intellectual Integrity According to P.72 of the Academic Handbook (June 1992 edition), each faculty member has "a responsibility to foster the intellectual honesty as well as the intellectual development of his/her students." In order to achieve these goals, each student enrolled in this course is prohibited from engaging in any form of "cheating" or "plagiarism." Cheating is defined as "dishonesty of any kind with respect to examination, course assignments, alteration of records, or illegal possession of examinations" (p. 72 of the Academic Handbook). "It is the responsibility of the student not only to abstain from cheating but, in addition, to avoid the appearance of cheating and to guard against making it possible for others to cheat. Any student who helps another student to cheat is as guilty of cheating as the student he or she assists. The student also should do everything possible to induce respect for the examining process and for honesty in the performance of assigned tasks in or out of class." (p. 72 of Academic Handbook). Plagiarism is defined as "offering the work of someone else as one's own" (p. 72 of Academic Handbook). "The language or ideas thus taken from another may range from isolated formulas, sentences, or paragraphs to entire articles copied from books, periodicals, speeches, or the writings of other students. The offering of materials assembled or collected by others in the form of projects or collections without acknowledgment also is considered plagiarism. Any student who fails to give credit for ideas or materials taken from another source is guilty of plagiarism." (p.72 of Academic Handbook). Evidence of student academic misconduct will result in (a) a lowered course grade, (b) transfer out of this course, (c) dismissal from student's academic unit, or (d) other disciplinary actions in accordance with the guidelines outlined on p.73 of Academic Handbook.