成長模型

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受到重視。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。本課程也提供成長模型在管理上的實證研究範例並進行討論。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。 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] 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.

1. 學期作業: 學期作業:學期作業一次,另須繳交期末報告。 2. 考試狀況: 期中考。 3. 評量方法: 作業 20%,期中考 30%,期末報告 50%。

• Growth modeling
• Applications of growth modeling

教材以期刊論文為主。

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@mail.nctu.edu.tw