Education | Multivariate Analysis in Educational Research
Y604 | 6245 | Dr. Joanne Peng


Objectives

1.To understand 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 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.  Website at
http://www.abacon.com/tabachnick

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

2-3	
Matrix Algebra (continued)	
Appendix A	
#38
Assignment #1—matrix algebra

4-6	
Multiple Regression	
Chapters 5, 4 , and 17	
#22 & #50
Assignment #2—multiple regression

7	
Canonical Correlation	
Chapter 6	
#47

8-9	
Hotelling’s T2 and MANOVA	
Chapter 9	
#54
Assignment #3—critique of an article using canonical correlation, or
Hotelling’s T2, or MANOVA

10-11	
Discriminant Function Analysis	
Chapter 11	
#19

12-13	
Principal Components &
Factor Analysis	
Chapter 13	
#13 & #14
Assignment #4 —factor analysis

14-15	
Multidimensional Scaling or Cluster Analysis		
#11 or #44

15	
Wrap up and course evaluation		
Assignment #5—critique of an article using discriminant function
analysis, multidimensional scaling, or cluster analysis
Each student is required to complete five assignments.  The specific
instructions 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 SAS® command language and
execution.  The attendance of labs is optional; but you alone are
responsible for the consequences of missing the labs.  The first lab
is this Wed. in Room 2015 from 11 to 12 pm. It will be an
introduction to SAS® in the Windows environment.

The AI for this course is Stephen West and his e-mail address is
STWEST.  Steve is located in ED 4003, carrel #4015; his phone number
is 856-8313 ext. 36753  and office hours are  1-2 pm on Tuesdays, 10-
11 am on Fridays. My office hours are Mondays and Wednesdays 12-1 pm
and Fridays 1-2 pm. 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 to see us.

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.			.
A- Excellent achievement.				
B+ Very good achievement.			
B  Good achievement				
B- Fair achievement
C+ Not wholly satisfactory achievement.
C  Marginal 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.