1.課堂參與 Class Participation：15% 2.小組作業 Assignments：45% 3.同儕評量 Peer Evaluation：10% 4.期末考 Final Exam：30%
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.)】
本課程的前半段主要介紹各種資料收集的應用。包括橫斷面資料、縱貫面資料、縱橫面資料與次級數據蒐集（網頁、檔案資料、資料庫）、內容分析、計量分析等等。除此之外，學生也會學習如何進行初級資料收集（問卷）與共同方法偏誤的處理。 The second section of this course will introduce students to various data collection methods in the applications of organizational behavior, human resource management, and the social sciences more broadly. Students will learn about cross-sectional data, longitudinal data, and panel data; secondary data and archive collection; along with content and econometric analysis. Also, we will briefly cover the concept of firsthand data collection (questionnaire survey) and common method bias. 本課程的後半段會先介紹如何設計相關量化研究，並應用進階的迴歸分析方法，同時透過實際操作教學，讓學生了解調節式中介效果 (moderated mediation) 與中介式調節效果 (mediated moderation) 的分析步驟，學習描述資料及解釋分析結果，並將這些方式應用於組織行為、人力資源管理及相關社會科學研究領域。除了前述量化研究方法外，本階段也會介紹基本的質性研究分析概念與方法，讓學生能了解不同的研究設計方式。 The first section of this course will introduce students to Quantitative Method and Advanced Regression Analysis. Students will learn mediated moderation and moderated mediation and apply these statistical techniques to the applications of Human Resource Management. Also, students will learn to use computer packages to perform data analysis, to interpret and report the analytical results. In addition, we will briefly touch the base of Qualitative Method. 本課程的主題包括：多元迴歸分析、探索性因素分析、驗證性因素分析、路徑分析、結構方程模式、質性研究簡介、資料結構、次級數據蒐集、內容與計量分析、初級數據蒐集（問卷）、共同方法偏誤。 General topics covered in this course include: Multiple Regression, Exploratory and Confirmatory Factor Analysis, Path Analysis, Structural Equation Modeling, Qualitative Method, Data Structure, Secondary Data and Archive Collection, Content and Econometric Analysis, and Firsthand Data Collection (Questionnaire Survey), and Common Method Bias.
課程目標 Objectives
1. 熟悉量化研究的研究設計方式。 2. 熟悉基礎統計知識與線性迴歸模式的資料分析方式與統計軟體。 3. 了解基本的質性研究分析概念與方法。 4. 熟悉因素分析(探索性與驗證性)、結構方程模式與其在人力資源管理上之應用。 5. 熟悉初級與次級數據資料蒐集相關應用 6. 熟悉資料結構與其分析方法（如：內容分析與計量分析） 7. 能夠以統計分析軟體SPSS與Mplus進行不同模型的資料分析。 1. Be familiar with research design in Quantitative Method 2. Be familiar with basic statistical concepts, linear regression analysis, and its associated software program package 3. Be familiar with basic concepts in Qualitative Method 4. Be familiar with factor analysis, structural equation modeling, and its applications in the Human Resource Management 5. Be familiar with secondary data and archive collection, and firsthand data collection as well 6. Be familiar with data structure and its associated data analysis (e.g., content and econometric analysis) 7. Be able to set up and execute the software programs SPSS and Mplus for statistical modeling
1.課堂參與 Class Participation：15% 2.小組作業 Assignments：45% 3.同儕評量 Peer Evaluation：10% 4.期末考 Final Exam：30%
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
使用教師自編與補充之課程講義教材，如有補充閱讀資料，會於上課前上傳給學生。 Supplemental materials will be given and students are expected to read them before class.
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).
本門課程規劃學生彈性或自主學習內容（每1學分2小時）：
Alternative learning periods planned for the course (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
Week
Date
Content and topic
1
2024/02/18~2024/02/24
課程簡介 Course Overview
2
2024/02/25~2024/03/02
橫斷面資料、縱貫面資料、縱橫面資料 Cross-sectional data, longitudinal data, and panel data
3
2024/03/03~2024/03/09
次級數據蒐集（網頁、檔案資料）、內容分析 Secondary data and archive collection, and content analysis
4
2024/03/10~2024/03/16
次級數據蒐集（資料庫）、計量分析 Secondary data collection, and econometric analysis
5
2024/03/17~2024/03/23
初級數據蒐集（問卷）、共同方法偏誤 Firsthand data collection (questionnaire survey), and common method bias
Internship: The required or elective courses should include credits and learning hours. Students should participate in the corporative company or institution to practice and learn the real skills. An internship certification must be handed in at the end of internship to get the credits or to fulfil the graduation requirements.