國立中山大學106學年度第1學期結構方程模式課程大綱

國立中山大學 106學年度第1學期課程教學大綱

National Sun Yat-sen University 106Academic year1st Semester Course syllabus

中文名稱
Course name(Chinese)

結構方程模式

課號
Course Code

HRM716

英文名稱
Course name(English)

STRUCTURAL EQUATION MODELING

課程類別
Type of the course

講授類

必選修
Required/Selected

選修

系所
Dept./faculty

人力資源管理研究所博士班

授課教師
Instructor

李澄賢

學分
Credit

因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，授課方式調整如下：

尚未建立傳染性肺炎(武漢肺炎)授課方式調整

因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，評分方式調整如下：

尚未建立傳染性肺炎(武漢肺炎)課程評分方式﹝評分標準及比例﹞

課程大綱 Course syllabus

本課程教學大綱已提供完整英文資訊（本選項僅供統計使用，未提供完整英文資訊者，得免勾記）【Provide information of course syllabus in English.(This is for statistical use only. For those who do not provide information of course syllabus in English, do not check this field.)】

This course will introduce students to Structural Equation Modeling in the Human Resource Management applications. Students will learn to examine the characteristics of observed variables (e.g., skewness and kurtosis), to use computer packages to perform parameter or model estimation, and to interpret and report the results of data analysis. General topics covered in class include: Path Analysis, Factor Analysis, Structural Regression Modeling, Latent Growth Curve Modeling, and some advanced topics in Structural Equation Modeling.

課程目標 Objectives

1. Be familiar with basic statistical concepts, matrix operations, and the relationship between manifest/observed variables and latent variables
2. Be familiar with factor analysis including both exploratory and confirmatory approaches
3. Be familiar with structural equation modeling and its extended applications
4. Be able to empirically examine the psychometric properties of a scale through factor analytic models
5. Be able to study relational phenomena in the field of Human Resource Management using structural equation modeling and its extensions
6. Be able to set up and execute the statistical software program Mplus to perform data analysis using different analytical techniques

授課方式 Teaching methods

Lecture & Discussion

評分方式﹝評分標準及比例﹞Evaluation (Criteria and ratio)等第制單科成績對照表 letter grading reference

1.Class Discussion：10%
2.Assignments：30%
3.Midterm Exam：20%
4.Term Paper & Final Presentation：40%

參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
〔請遵守智慧財產權觀念，不可非法影印。教師所提供之教材供學生本人自修學習使用,不得散播及做為商業用途〕
No copies for intellectual property rights. Textbooks provided by the instructor used only for self-study, can not broadcast or commercial use

1. Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (2nd edition). Mahwah, NJ: Lawrence Erlbaum Associates Inc.
2. 陳新豐 (2014)。結構方程模式：Mplus的應用。臺北：心理。

彈性暨自主學習規劃 Alternative learning periods

本門課程是否有規劃實施學生彈性或自主學習內容（每1學分2小時）

Is any alternative learning periods planned for this course (with each credit corresponding to two hours of activity)?

否：教師需於「每週課程內容及預計進度」填寫18週課程進度（每1學分18小時之正課內容）。
No：The instructor will include an 18-week course plan in the weekly scheduled progress (each credit corresponds to 18 hours of instruction)

是：教師需於「每週課程內容及預計進度」填寫16週課程內容（每1學分16小時之正課內容），並於下列欄位填寫每1學分2小時學生彈性或自主學習內容。
Yes：The instructor will include a 16-week course plan in the weekly scheduled progress (each credit corresponds to 16 hours of instruction);the details of the planned alternative learning periods are provided below (each credit corresponds to two hours of activity).

學生彈性或自主學習活動 Alternative learning periods	勾選或填寫規劃內容 Place a check in the appropriate box or provide details	時數 Number of hours
學生分組實作及討論 Group work and discussion
參與課程相關作業、作品、實驗 Participation in course-related assignments, work, or experiments
參與校內外活動（研習營、工作坊、參訪）或競賽 Participation in on- or off-campus activities (e.g., seminars, workshops, and visits) or competitions
課外閱讀 Extracurricular reading
線上數位教材學習 Learning with online digital learning materials
其他（請填寫規劃內容） Other (please provide details)

每週課程內容及預計進度 Weekly scheduled progress

週次	日期	授課內容及主題
1	2017/09/18~2017/09/24	Course Overview
2	2017/09/25~2017/10/01	Basic Statistical Concepts & Matrix Operations
3	2017/10/02~2017/10/08	Path Analysis & Introduction to Mplus
4	2017/10/09~2017/10/15	Factor Analysis I
5	2017/10/16~2017/10/22	Factor Analysis II
6	2017/10/23~2017/10/29	Applications of Factor Analysis
7	2017/10/30~2017/11/05	Structural Regression Modeling
8	2017/11/06~2017/11/12	Applications of Structural Regression Modeling I
9	2017/11/13~2017/11/19	Midterm
10	2017/11/20~2017/11/26	Applications of Structural Regression Modeling II
11	2017/11/27~2017/12/03	Practical Guidelines for Reporting SEM/CFA results
12	2017/12/04~2017/12/10	Latent Growth Curve Modeling
13	2017/12/11~2017/12/17	Applications of Latent Growth Curve Modeling
14	2017/12/18~2017/12/24	Advanced topics related to SEM I (e.g., multiple-group CFA, multiple-group SEM)
15	2017/12/25~2017/12/31	Advanced topics related to SEM II (e.g., categorical data)
16	2018/01/01~2018/01/07	Advanced topics related to SEM III (e.g., missing data)
17	2018/01/08~2018/01/14	Final Presentations I
18	2018/01/15~2018/01/21	Final Presentations II

課業討論時間 Office hours

時段1:
時間：星期二12:00-14:00
地點：管3036
時段2：
時間：星期三10:00-12:00
地點：管3036

系所學生專業能力/全校學生基本素養與核心能力 basic disciplines and core capabilitics of the dcpartment and the university

系所學生專業能力/全校學生基本素養與核心能力

課堂活動與評量方式

本課程欲培養之能力與素養 This course enables students to achieve...

紙筆考試或測驗 Test

課堂討論︵含個案討論︶ Group discussion (case analysis)

個人書面報告、作業、作品、實驗 Indivisual paper report/ assignment/ work or experiment

群組書面報告、作業、作品、實驗 Group paper report/ assignment/ work or experiment

個人口頭報告 Indivisual oral presentation

群組口頭報告 Group oral presentation

課程規劃之校外參訪及實習 Off-campus visit and intership

證照/檢定 License

參與課程規劃之校內外活動及競賽 Participate in off-campus/ on-campus activities and competitions

課外閱讀 Outside reading

※系所所學生專業能力

1.學生能注意學術倫理。

2.學生能具有效溝通及教學的能力。

3.學生能使用科學方法進行高品質的研究。

4.學生能具備國際觀並將之運用在研究中。

5.學生能了解專業的議題並運用專業知識來解決問題。

※全校學生基本素養與核心能力

1.表達與溝通能力。

2.探究與批判思考能力。

3.終身學習能力。

4.倫理與社會責任。

5.美感品味。

6.創造力。

7.全球視野。

8.合作與領導能力。

9.山海胸襟與自然情懷。

本課程與SDGs相關項目：The course relates to SDGs items:

尚未建立SDGS資料

本課程校外實習資訊: This course is relevant to internship:

本課程無註記包含校外實習