多變量統計分析
Multivariate Statistical
Analysis
Spring 2004 Course Syllabus
Department of Psychology,
The Course
Semester:
Spring 2004 Course
Units: 3.0
Time:
Wednesdat
Course
Type: Lecture/Discussion/Lab Restriction:
Graduate students
The Instructor
Dr. Hawjeng Chiou, Ph.D. 邱皓政副教授 （http://hawjeng.idv.tw）
Office:
823, SF Building(聖言樓823室) Office
hours: appointment via phone or email
Email:
hawjeng@mails.fju.edu.tw Tel:
(O) 02-29031111 ext. 2129 or (H)02-29777344
Mailbox:
台北縣新莊市242輔仁大學心理學系 Fax:
(O)02-29038438
Objectives
本課程的目的在介紹各種常用於心理與教育領域的多變量統計分析方法與技術。作為一門研究所的計量課程，本課程將以基礎統計學的訓練為基礎，著重於各種多變量推論統計以及結構方程模式分析的基本概念與分析方法的介紹，例如區別分析、複變量變異數分析、因素分析、集群分析、邏輯迴歸、結構方程模式等等。課中除了原理性的討論，將配合統計軟體應用的介紹，例如SPSS、SAS、LISREL、EQS，使學生們不但能夠理解各種統計技術的原理與使用時機，並能夠具體的操作與分析，豐富學生們計量分析的知識素養與操作能力，協助學生們從事量化的研究工作。
This course introduces the multivariate
statistical techniques regularly used in educational research, such as MANOVA,
discriminant analysis, cluster analysis, canonical correlation, logistic
regression, structural equation modeling, etc. By integrating the application of computer software
such as SPSS or LISREL, this course expands beyond basic concepts of
statistical techniques to include more operational skills on doing research in
educational and relating issues.
The dual-objective of this course is to provide a sound conceptual
understanding on statistical issues, and to provide hands-on experience with
major statistical software packages.
Specific objectives this course seeks to accomplish are as follows:
1.
To
understand the basic principles of various multivariate statistical methods;
2.
To
acknowledge the advantages and limitations of statistical methods and
applications;
3.
To
apply multivariate statistical methods to practical research situations within
one’s own research area, and propose solutions to real-world research issues;
4.
To
analyze real data using software programs, SPSS or SEM software; and to propose
a complete paper on the basis of precise statistical language.
Course Format and Activities
This course combines
lecture and computer lab activities into one integrated format:
Lectures: The instructor covers each topic by
giving lectures in the class.
Lecture notes and reading material will be provided in advanced and
students are highly encouraged to be constructive and reflective preview of
assigned materials.
Computer
Labs: To enhance the concepts covered in the
class lectures, computer demos of related topics will be presented in class,
taking almost a half of the course hours.
Both real-life and simulated data will be used for these lab
activities. The computer labs take
one of two forms – demonstration of statistical software packages by instructor
or practice by students themselves.
Prerequisites
Required: The required prerequisite for this
course is introduction to statistics or measurement course. Students without this background are
strongly encouraged to consult the instructor, although no enforcement will be
made to block enrollment.
Recommended: The recommended prerequisite is computer
knowledge and skills. Experience
with statistical packages and computer programming is highly desirable for
successful completion of this course.
Students without any background in computer use are encouraged to team
up with someone who does.
Assignment
I. Class example:
Follow the class
schedule, students have to read the chapters in advance and introduce one of
the examples in text in order to leading the lecture of the class. A short memo is required and distributed
to the auditors in the class.
II. Final
project
One empirical study
has to be done by focusing at least on one multivariate statistical
technique. Real data is analyzed
and the writing of the paper has to follow the APA format. Oral presentation will be scheduled in
class. Paper due at the end of the
semester.
III. Lab
homework
This course
requires students to conduct separate exercises in correspondent to the class
materials for lab work. A simple
and short homework answer sheet has to turn in at the following week. The dataset will be provided by lecturer
during the class. **(Note.
Each homework has to be answered by two students and reported at the class.)
Textbooks
A. Using Multivariate statistics(4th Ed.) by
Tabachnick & Fidell (2001)
B.
C.
D.
A first course on SEM by Raykov, T., &
Marcoulides, G. A. (2000)
E.
邱皓政(2000)。量化研究與統計分析。台北: 五南。
F.
邱皓政(2003)。結構方程模式: LISREL的理論與應用。台北, 雙葉。
G.
林師模、陳苑欽(2003)。多變量分析：管理上的應用。台北, 雙葉。
H.
陳正昌、程炳林、陳新豐、劉子鍵(2003)。多變量分析方法：統計軟體應用。台北, 五南。
I.
陳順宇(2000)。多變量分析。台北，華泰書局。
J.
黃俊英(1996)。多變量分析。台北，華泰書局。
Class
Schedule
Revised
|
|
Week |
Topics & Activities |
Chapters & homework |
|
1 |
2/18 |
課程簡介（Course introduction） |
|
|
2 |
2/25 |
複變量分析概說（Basic concepts to
multivariate statistical analysis） |
|
|
3 |
3/03 |
類別變項統計: 從卡方到對數線性模式（Log-Linear modeling） |
A-Ch.1 |
|
4 |
3/l0 |
集群分析（Cluster analysis） |
C-Ch.5 |
|
5 |
3/17 |
多元尺度分析(Multidimensional Scaling） |
B-Ch.5 |
|
6 |
3/24 |
複變量變異數分析（Multivariate analysis of
variance） |
A-Ch.9 |
|
7 |
3/31 |
階層線性模式（Hierarchical Linear
Modeling） |
C-Ch.10 |
|
8 |
4/07 |
Profile
Analysis |
A-Ch.10 |
|
9 |
4/14 |
區別分析（Discriminant Function
analysis） |
A-Ch.11 |
|
10 |
4/21 |
邏輯迥歸（Logistic regression） |
A-Ch.12 |
|
11 |
4/28 |
典型相關（Canonical correlation
analysis） |
A-Ch.6 |
|
12 |
5/05 |
因素分析（Factor Analysis） |
A-Ch.13 |
|
13 |
5/12 |
結構方程模式路徑分析（Path Analysis on Structural
Equation Modeling） |
A-Ch.14 |
|
14 |
5/19 |
存活分析（Survival Analysis） |
A-Ch.15 |
|
15 |
5/26 |
時間序列分析（Time-series Analysis） |
A-Ch.16 |
|
16 |
6/02 |
期末學生報告 |
|
|
17 |
6/09 |
期末報告（或期末測驗）與課程總結 |
Final exam distribute |
Grading
Evaluation of student
performance is based on the following criteria:
1.
Class participation (attendance, class interaction,
after-class discussion)………………………………… 10%
2.
Homework or
computer comprehension take-home examination ………………………………………… 20%
3.
Class example introduction ……………………………………………………………………………………… 30%
4.
Final project………………….…………………………………………………………………………………… 40%
(This
syllabus is subject to change upon mutual consent from the students and the
instructor.)