高等數量方法
Advanced Quantitative Methods
| 節 | 週一 |
|---|---|
2 09:00–09:50 | 高等數量方法 TA506 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
課程概述與目標: This course comprises three main sections: Human Resources Analytics, meta-analysis, and hierarchical linear models. In the Human Resources Analytics section, it introduces the concepts and practices of human resource analytics. Topics such as basic statistics and analytical methods used for analyzing human resource management decision-making are emphasized. The focus is on equipping future human resource professionals with the knowledge and skills required to apply business analytics to human resource decision-making. In the meta-analysis section, the aim is to introduce the concept and practices of meta-analysis following Hunter and Schmidt's approach. In the hierarchical linear models section, the course is application-oriented and introduces the basic concepts and practices of HLM using Mplus software. Additionally, this course introduces Monte Carlo simulation for experience sampling method (ESM).
Statistical Methods and data Analysis, Multivariate Analysis
1. Homework and Assignments: Individual Assignment (49%) There will be a total of 7 assignments. Late assignment will be resulting in a deduction of your assignment grade. I encourage you to work on the assignments with your classmates. But each student needs to write their assignments in his/her own words. Plagiarism will result in 0 points for your assignment. Final Project (31%) Students need to write a term paper that provides the analytic results (i.e., reliability, CFA, and HLM). A late project will result in a deduction of your project scores (10% each day). Please upload your assignment to Moodle system by the deadline. No plagiarism! You will share your assignment score with your classmates who have identical assignment answers to you. Impression & Participation (20%) The starting point of the class participation score is zero. In-class exercises or activities will be used as records of your participation. Attendance Policy Exams, group discussions, roll calls, or in-class activities will be used as your attendance records. Each student is exempt from 3 hours of absence with no questions asked. You will be assessed 1 point against your final grade for each absent hour. Sick leaves without diagnosis statements or medical receipts will be treated as an unexcused absence, depending on the circumstances. You will fail this class if you miss more than 12 class hours. 2. Exams and Quizzes: No. 3. Evaluation and Grading Policy: Individual Assignment (49%); Final Report (31%); Impression & Participation (20%). Pedagogy and other supplementary information (websites, TAs, handouts and/or databases): Course handouts will be provided.
- Artifact Corrections in Meta-Analysis
- Hierarchical Linear Models
- Big Data in Human Resource
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course instruction |
| 第 2 週 | Big Data in Human Resource Management Reading: 胡昌亞(2020; 2021)、蔡治(2018) (CH1) Introduction JASP |
| 第 3 週 | Case Analysis and Discussion Employee Attrition Data (Multiple Regression, Logistic Regression, and Decision Tree) |
| 第 4 週 | Case Analysis and Discussion (IA 1) 胡昌亞、葉冠義、張君卿(2022)。直播平台主播收益數據分析。 |
| 第 5 週 | Case Analysis and Discussion (IA2) 胡昌亞、李森斌、陳燕諭(2023)。據食以告:A餐飲集團之數據管理。 |
| 第 6 週 | Case Analysis and Discussion (IA 3) 胡昌亞、林秋炭、陳嘉純(2019)。大數據要我別聘你: 大數據分析在人力資源規劃之應用。 |
| 第 7 週 | Artifacts Issues and Hunter-Schmidt Model Readings: Schmidt & Hunter (2015) (CH1-CH4) |
| 第 8 週 | Hunter-Schmidt Model The SOP of a Meta-Analysis Research Readings: 李茂能(2016) (CH3) |
| 第 9 週 | Conducting Meta-Analysis with the Psychmeta Package in R Software (IA 4) |
| 第 10 週 | Mid-term Progress Report (IA 5) |
| 第 11 週 | Introduction to Multi-level Analysis (Concepts) Reading: González-Romá and Hernández (2017), Klein and Kozlowski (2000), Chan (1998), and Chen, Mathieu, and Bliese (2004). |
| 第 12 週 | Introduction to Multi-level Analysis (Methods) Reading: Hox (2010) (CH1, CH3, CH4) |
| 第 13 週 | Multilevel Regression Models and Path Models (IA6) Readings: Heck and Thomas (2020) (CH3-CH4) and Hox (2010) (CH2 and CH15) |
| 第 14 週 | Multilevel Structural Equation Models (IA7) Reading: Heck and Thomas (2020) (CH7) |
| 第 15 週 | Introduction to Monte Carlo Simulation for Experience Sampling Methods Data |
| 第 16 週 | Final Report |
蔡治(2018)。大數據時代的人力資源管理。崧燁文化。 Schmidt, F. L. & Hunter, J. E. (2015). Methods of meta-analysis: Correcting error and bias in research findings. Sage. Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: Guilford Press.
- 地點
- 教授研究室
- 時間
- 周五 10:00-12:00
- 聯絡方式
- chenyy@nycu.edu.tw