多變量統計分析

Multivariate Statistical Analysis

 

Spring 2005 Course Syllabus

Department of Psychology, Fu-Jen Catholic University

 

 

The Course

Semester: Spring 2005                                                                       Course Units: 3.0                         

Time: Wednesdat 09:10 -12:00                                                          Lecture Room: SF846

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) 02-29052129

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. 

 

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. 

 

Textbooks

A.      Using Multivariate statistics(4th Ed.) by Tabachnick & Fidell (2001)。（雙葉代理）

B.      Reading and understanding multivariate statistics Ed. By Grimm, L. G., & Yarnold, P. R. (1995)

C.      Reading and understanding more multivariate statistics Ed. By Grimm, L. G., & Yarnold, P. R. (2000)

D.      Categorical data analysis (2nd ed.) by Alan Agrestui (2002)

E.       A first course on SEM by Raykov, T., & Marcoulides, G. A. (2000)

F.       邱皓政(2000)。量化研究與統計分析。台北: 五南。

G.       邱皓政(2003)。結構方程模式: LISREL的理論與應用。台北, 雙葉。

H.      王保進(2004)。多變量分析。台北, 高等教育出版社。

I.         林師模、陳苑欽(2003)。多變量分析：管理上的應用。台北, 雙葉。

J.        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。

K.      陳順宇(2000)。多變量分析。台北，華泰書局。

L.       黃俊英(1996)。多變量分析。台北，華泰書局。

 

Class Schedule

 Revised 1/9/2005

 

Week

Topics & Activities

Chapters & homework

1

2/23

課程簡介（Course introduction）

 

2

3/02

複變量分析概說（Basic concepts to multivariate statistical analysis）

 

3

3/09

集群分析（Cluster analysis）與多元尺度分析(Multidimensional Scaling）

C-Ch.5   B-Ch.5

4

3/l6

對數線性模式（Log-Linear modeling）

A-Ch.1

5

3/23

複變量變異數分析（Multivariate analysis of variance）

A-Ch.9

6

3/30

剖面分析（Profile Analysis）

A-Ch.10

7

4/06

[ 放假 ]

 

8

4/13

區別分析（Discriminant Function analysis）

A-Ch.11

9

4/20

邏輯迥歸（Logistic regression）

A-Ch.12

10

4/27

典型相關（Canonical correlation analysis）

A-Ch.6

11

5/04

因素分析（Factor Analysis）

A-Ch.13

12

5/11

結構方程模式（Structural Equation Modeling）

A-Ch.14

13

5/18

階層線性模式（Hierarchical Linear Modeling）

C-Ch.10

14

5/25

潛在類別分析（Latent Class Analysis）

*

15

6/01

存活分析（Survival Analysis）

A-Ch.15

16

6/08

時間序列分析（Time-series Analysis）

A-Ch.16

17

6/15

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.)