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題名：成長曲線模式之樣本單位數決定研究：蒙地卡羅模擬
作者：巫博瀚 引用關係
作者(外文)：Po-HanWu
校院名稱：國立成功大學
系所名稱：教育研究所
指導教授：陸偉明
學位類別：博士
出版日期：2012
主題關鍵詞：多層次模式；成長曲線模式；樣本數決定；蒙地卡羅模擬研究；multilevel modeling；growth curve modeling；determining sample sizes；Monte Carlo simulation study
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近年來多層次模式已成為縱貫或重複測量資料的主流分析方式。然而，在進行成長曲線模式分析時，不同層次的模式需要多大的樣本方足以獲得不偏的參數估計結果，迄今尚無模擬研究予以探討。因此，各層所需的樣本數如何配置，向為進行縱貫性調查研究的重大挑戰，其重要性日益殷切。
為釐清前述研究議題，本研究以縱貫研究常見的樣本配置組合為模擬設計依據，期透過蒙地卡羅模擬研究，探討不同樣本規模與二層配置對於無條件成長模式與條件成長模式參數估計值（迴歸係數）與隨機效果變異數正確性的影響，並依據模擬研究結果提出最有效率的樣本數配置組合。本研究操弄的影響因子為「每一個人的時點數」與「人數」，並使用Mplus5.21版統計軟體進行內部蒙地卡羅模擬研究，模擬資料的產生與分析均透過Mplus的MONTECARLO指令進行分析，估計方法採ML估計法。
研究結果發現，無論是無條件成長模式或條件成長模式，當第二層分析單位數達100或100人以上時，無論第一層的分析單位數（波數）為三波、四波、五波或六波，對於迴歸係數的估計都是不偏的。此外，當第二層的樣本規模（人數）較小時，則隨機效果的估計會有嚴重的偏誤，而當提高第二層分析單位的樣本規模時，則隨機效果參數的估計將會愈正確。總括來說，如果研究者只關心模式中的固定效果時，則使用小規模樣本（100人）即能獲得良好的估計，然而當研究者亦關注模式中的隨機效果時，則無可避免地必須使用較大規模的樣本，方能滿足參數估計正確性的要求。
Multilevel modeling has become the main methodology for analyzing longitudinal or repeated measures data recently. Moreover, determining the required sample size at different levels is the important issue for longitudinal studies. However, few simulation study discusses the required sample size to gain unbiased estimated parameters on growth curve modeling at different levels. This work designed Monte Carlo simulation to investigate the impacts of an unconditional growth model and those of a conditional growth model on the accuracies of estimated parameters (regression coefficients) and random effect variances, respectively. The manipulated factors were ‘the number of time points per person’ and ‘the number of people.’ All the analyses were conducted by using Mplus 5.21; simulated data were generated from the command of MONTECARLO, and then the data were estimated by ML estimation.
The results indicated that the estimated regression coefficients were unbiased in both unconditional and conditional growth models when the second level contained more than 100 units, no matter the first level unit. In addition, when the second level has a small sample (i.e., small number of people), the estimations for random effect were seriously biased. However, a larger sample had its estimations more accurately. In sum, if the researchers only discuss fixed effects in the model, a small sample (e.g., 100) in the level 2 can gain the good estimations. If the researchers focus on the random effects as well, a large sample is needed to have accurately estimated parameters
巫博瀚、陸偉明（2010）。延宕交叉相關與二階層線性成長模式在台灣青少年自尊的發現。測驗學刊，57(4)，541-565。new window
巫博瀚、陸偉明（2012）。台灣青少年憂鬱情緒成長發展之縱貫研究：多層次分析。
林定香、李梓生（2008）。探討階層線性模式中影響二元反應變數參數估計之因子。測驗學刊，55(3)，635-658。new window
Afshartous, D. (1995). Determination of sample size for multilevel model design. Paper presented at the annual meeting of the American Educational Research Association. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.4318new window
Bickel, R. (2007). Multilevel analysis for applied research: It's just regression! New York, NY: Guilford.
Bradley, J. V. (1978). Robustness? British Journal of Mathematical and Statistical and Psychology, 31, 144-152.
Browne, W. J., ＆ Draper, D. (2000). Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models. Computational Statistics, 15, 391-420.
Cochran, W. (1977). Sampling techniques (3rd ed.). New York: Wiley.
Cohen, J. (1998). Determining sample sizes for surveys with data analyzed by hierarchical linear models. Journal of Official Statistics, 14, 267-275.
Cohen, M. P. (2005). Sample size considerations for multilevel surveys. International Statistical Review, 73, 279-287.
Dattalo, P. (2008). Determining sample size: Balancing power, precision, and practicality. New York, NY: Oxford University Press.
Everitt, B. S. (2005). Longitudinal data analysis. In B. S. Everitt ＆ D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (Vol. 3, pp. 1098-1101). Hoboken, NJ: Wiley.
Goldstein, H. (2003). Multilevel statistical models (3rd ed.). New York, NY: Oxford University Press.
Goldstein, H. (2011). Multilevel statistical models (4th ed.). West Sussex, UK: Wiley.
Heck, R. H., ＆ Thomas, S. L. (2009). An Introduction to multilevel modeling techniques (2nd ed.). New York: Routledge.
Henry, G. T. (1990). Practical sampling. Newbury Park, CA: Sage.
Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York, NY: Routledge.
Hox, J. J. (2011). Panel modeling: Random coefficients and covariance structures. In J. J. Hox ＆ J. K. Roberts (Eds.), Handbook of advanced multilevel analysis (pp. 85-96). New York: NY: Routledge.
Hox, J. J., ＆ Roberts, J. K. (2010). Multilevel analysis: Where we were and where we are. In J. J. Hox ＆ J. K. Roberts (Eds.), Handbook of Advanced Multilevel Analysis New York, NY: Routledge.
Hox, J. J., ＆ Roberts, J. K. (2011). Multilevel analysis: Where we were and where we are. In J. J. Hox ＆ J. K. Roberts (Eds.), Handbook of advanced multilevel analysis (pp. 3-11). New York: NY: Routledge.
Kim, J. S. (2009). Multilevel analysis: An overview and some contemporary issues. In R. E. Millsap ＆ A. Maydeu-Olivares (Eds.), The SAGE handbook of quantitative methods in psychology (pp. 336-361). Thousand Oaks, CA: SAGE.
Kish, L. (1965). Survey sampling. New York, NY: Wiley.
Kreft, I., ＆ Leeuw, J. D. (1998). Introducing multilevel modeling. Thousand Oakes, CA: Sage.
Leeuw, J. D., ＆ Kreft, I. (1986). Random coefficient models for multilevel analysis. Journal of Educational Statistics, 11, 57-85.
Leeuw, J. D., ＆ Meijer, E. (2008). Introduction to multilevel analysis. In J. D. Leeuw ＆ E. Meijer (Eds.), Handbook of Multilevel Analysis (pp. 1-76). New York, NY: Springer.
Longford, N. T. (2008). Missing data. In J. D. Leeuw ＆ E. Meijer (Eds.), Handbook of Multilevel Analysis (pp. 377-399). New York, NY: Springer.
Luke, D. A. (2004). Multilevel modeling. Thousand Oaks, CA: Sage publication, Inc.
Luke, D. A. (2008). Multilevel growth curve analysis for quantitative outcomes. In S. Menard (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 545-564). San Diego, CA: Elsevier Academic Press.
Maas, C. J. M., ＆ Hox, J. J. (2004). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58, 127–137.
Maas, C. J. M., ＆ Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 1, 86-92.new window
Moerbeek, M., Breukelen, G. J. P. V., ＆ Berger, M. P. F. (2008). Optimal designs for multilevel studies. In J. D. Leeuw ＆ E. Meijer (Eds.), Handbook of Multilevel Analysis (pp. 177-205). New York, NY: Springer.
Mok, M. (1995). Sample size requirements for 2-level designs in educational research. Multilevel Modelling Newsletter, 7, 11-15.
Raudenbush, S. W. (1997). Statistical analysis and optimal design for cluster randomized trials. Psychological Methods, 2, 173-185.
Raudenbush, S. W., ＆ Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: SAGE.
Raudenbush, S. W., ＆ Liu, X. (2000). Statistical power and optimal design for multisite randomized trials. Psychological Methods, 5, 199-213.
Reise, S. P., ＆ Duan, N. (2003). Design issues in multilevel studies. In S. P. Reise ＆ N. Duan (Eds.), Multilevel modeling: Methodological advances, issues, and applications (pp. 285-298). Mahwah, NJ: Lawrence Erlbaum Associates.
Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 24(4), 323-355.
Singer, J. D., ＆ Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.
Singer, J. D., ＆ Willett, J. B. (2005). Growth curve modeling. In B. S. Everitt ＆ D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (Vol. 2, pp. 772-779). Hoboken, NJ: Wiley.
Skrondal, A., ＆ Rabe-Hesketh, S. (2008). Multilevel and related models for longitudinal data. In J. D. Leeuw ＆ E. Meijer (Eds.), Handbook of Multilevel Analysis (pp. 275-299). New York, NY: Springer.
Snijders, T. A. (2005). Power and sample size in multilevel linear models. In B. S. Everitt ＆ D. C. Howell (Eds.), Encyclopedia of statistics in behavioral sciences (Vol. 3, pp. 1570–1573). Chicester: UK: Wiley.
Snijders, T. A., ＆ Bosker, R. J. (1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237-259.
Snijders, T. A., ＆ Bosker, R. J. (1994). Modeled variance in two-level models. Sociological Methods ＆ Research, 22, 342-363.
Snijders, T. A. B., ＆ Bosker, R. J. (1999). Multilevel analysis: An Introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage.
Stoel, R. D., ＆ Garre, F. G. (2011). Growth curve analysis using multilevel regression and structural equation modeling. In J. J. Hox ＆ J. K. Roberts (Eds.), Handbook of advanced multilevel analysis (pp. 97-111). New York: NY: Routledge.
Weiss, R. E. (2005). Modeling longitudinal data. New York, NY: Springer.
 
 
 
 
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