成長模型
Growth Modeling
| 節 | 週一 |
|---|---|
2 09:00–09:50 | 成長模型 TDA506 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
成長模型是一動態模型,可針對追蹤資料分析隨時間變化之趨勢,在管理的應用上日益普及。成長模型可利用階層線性模型 (HLM) 或結構方程模型 (SEM) 從事分析,本課程將具體說明 HLM 和 SEM的分析方法以及所對應之SAS PROC MIXED和CALIS的操作方式,也對HLM法與SEM法作優劣比較。本課程也會探討若干成長模型的進階議題以及在管理上的實證應用。本課程之目的在於進一步強化同學的研究能力。
統計方法與資料分析,多變量分析,線性結構模式
全程由教師講授,以板書為主,並提供SAS程式給同學參考使用。
1. 學期作業: 學期作業:學期作業一次。 2. 考試狀況: 期中考,期末報告。 3. 評量方法: 作業 20%,期中考 30%,期末報告 50%。
- Growth modeling
- Advanced topics and applications
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to growth modeling |
| 第 2 週 | Multi-level models |
| 第 3 週 | Unconditional growth models |
| 第 4 週 | Conditional growth models |
| 第 5 週 | Growth modeling by using the HLM approach |
| 第 6 週 | The use of PROC MIXED |
| 第 7 週 | Growth modeling by using the SEM approach |
| 第 8 週 | The use of PROC CALIS |
| 第 9 週 | Comparison between the HLM approach and the SEM approach |
| 第 10 週 | 期中考 |
| 第 11 週 | Identifying plausible level-1 error covariance structures |
| 第 12 週 | Quadratic growth models and applications |
| 第 13 週 | Piecewise growth models and applications |
| 第 14 週 | Growth modeling for latent constructs |
| 第 15 週 | Testing for dynamic relationships |
| 第 16 週 | 期末報告 |
教材以期刊論文為主。 References: [1] Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley. [2] Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1, 421-483. [3] Ding, C. G., Hung, W. C., Lee, M. C., & Wang, H. J. (2017). Exploring paper characteristics that facilitate the knowledge flow from science to technology. Journal of Informetrics, 11, 244-256. [4] Ding, C. G., & Jane, T. D. (2012). Using SAS PROC CALIS to fit level-1 error covariance structures of latent growth models. Behavior Research Methods, 44, 765-787. [5] Ding, C. G., Jane, T. D., Wu, C. H., Lin, H. R., & Shen, C. K. (2017). A systematic approach for identifying level-1 error covariance structures in latent growth modelling. International Journal of Behavioral Development, 41, 444-455. [6] Ding, C. G., Lin, H. R., Wu, C. H., & Jane, T. D. (2015). Using LGM analysis to identify hidden contributors to risk in the operation of a nuclear power plant. Safety Science, 75, 64-71. [7] Ding, C. G., Wu, C. H., and Chang, P. L. (2013). The influence of government intervention on the trajectory of bank performance during the global financial crisis: A comparative study among Asian economies. Journal of Financial Stability, 9, 556-564. [8] Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An Introduction to latent variable growth curve modeling: Concepts, issues, and applications (2nd ed.). Mahwah, NJ: Lawrence Erlbaum. [9] Flora, D.B. (2008). Specifying piecewise latent trajectory models for longitudinal data. Structural Equation Modeling, 15, 513-533. [10] Hancock, G. R., Kuo, W. L., & Lawrence, F. R. (2001). An illustration of second-order latent growth models. Structural Equation Modeling, 8, 470-489. [11] Hung, W. C., Ding, C. G., Wang, H. J., Lee, M. C., & Lin, C. P. (2015). Evaluating and comparing the university performance in knowledge utilization for patented inventions. Scientometrics, 102, 1269-1286. [12] Kim, M., Kwok, O. M., Yoon, M., Willson, V., & Lai, M. H. C. (2016). Specification search for identifying the correct mean trajectory in polynomial latent growth models. Journal of Experimental Education, 84, 307-329. [13] Kwok, O. M., West, S. G., & Green, S. B. (2007). The impact of misspecifying the within- subject covariance structure in multiwave longitudinal multilevel models: A Monte Carlo study. Multivaraite Behavioral Research, 42, 557-592. [14] Leite, W. L. (2007). A comparison of latent growth models for constructs measured by multiple items. Structural Equation Modeling, 14, 581-610. [15] Miller, J. W., Fugate, B. S., & Golicic, S. L. (2017). How organizations respond to information disclosure: Testing alternative longitudinal performance trajectories. Academy of Management Journal, 60, 1016-1042. [16] Murphy, D. L., & Pituch, K. A. (2009). The Performance of multilevel growth curve models under an autoregressive moving average process. Journal of Experimental Education, 77, 255-282. [17] Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models (2nd ed.), London: Sage. [18] Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 23, 323-355. [19] Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press. [20] Thoresen, C. J., Bradley, J. C., Bliese, P. D., & Thoresen, J. D. (2004). The big five personality traits and individual job performance growth trajectories in maintenance and transitional job stages. Journal of Applied Psychology, 89, 835-853. [21] Vandenberghe, C., Landry, G., Bentein, K., Anseel. F., Mignonac, K., & Roussel, P. (2019). A dynamic model of the effects of feedback-seeking behavior and organizational commitment on newcomer turnover. Journal of Management, published online. [22] Wu, C. H., Ding, C. G., Jane, T. D., Lin, H. R., & Wu, C. Y. (2015). Lessons from the global financial crisis for the semiconductor industry. Technological Forecasting and Social Change, 99, 47-53. [23] Wu, C. H., Ding, C. G., & Wu, C. Y. (2018). On the assessment of the performance in earnings management for the banking industry: The case of China’s banks, Applied Economics Letters, 25, 1463-1465.
- 地點
- 教授研究室
- 時間
- 週一 14:00 ~ 16:00 (可以e-mail另約其他時間)
- 聯絡方式
- cding@nycu.edu.tw