Education | Multivariate Analysis in Educational Research
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.