:::

詳目顯示

回上一頁
題名:測量誤差對成長混合模型異質性分析的影響:蒙地卡羅模擬研究與實徵資料分析
作者:曾明基
作者(外文):Ming-Chi Tseng
校院名稱:國立東華大學
系所名稱:課程設計與潛能開發學系
指導教授:張德勝
邱皓政
學位類別:博士
出版日期:2013
主題關鍵詞:成長混合模型潛在成長混合模型Monte Carlo模擬研究Growth Mixture ModelLatent Growth Mixture ModelMonte Carlo Simulation
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(1) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:0
成長混合模型適合應用在縱貫性且具樣本異質性結構的資料,可以解決因樣本異質性而導致的縱貫性分析參數估計偏誤的問題。然而,在每一測量時間點的重複變項測量上,成長混合模型僅一單一題項加總計分的方式來進行成長模型的建構以及後續異質性的探討,這樣的測量方式並沒有考慮到測量誤差以及測量誤差對成長模型建構與樣本異質性的影響。
基於此,本研究在每一測量時間點透過多個題項的重複測量而成為包含測量誤差的潛在成長混合模型,在考慮潛在構念的測量誤差並在控制測量誤差的前提下,才進行成長模型建構與樣本異質性的探討。在估計測量誤差的前提下,則成長混合模型以及潛在成長混合模型的適配度以及分群的精確性是否產生改變,則是本研究企圖釐清之處。
本研究首先藉由Monte Carlo模擬研究,比較測量誤差的大小對成長混合模型以及潛在成長混合模型的影響。經由模擬研究發現,測量誤差會影響成長混合模型以及潛在成長混合模型的適配度以及分群精確性,當測量誤差越小時,適配度以及分群精確性越佳。此外,在同一母群參數架構下比較成長混合模型以及潛在成長混合模型發現,潛在成長混合模型因自由度較大而在AIC、BIC、ABIC的適配度膨脹快速,但在LMR、BLRT的適配指標表現較成長混合模型佳,而分群精確性指標Entropy的結果並不一致,當樣本數較小時潛在成長混合模型表現較佳,當樣本數較大時成長混合模型表現較佳。
進一步透過青少年憂鬱及敵意的實證建構,比較成長混合模型以及潛在成長混合模型在模式適配度以及分群精確性的差異,實證研究結果與模擬研究相似。此外,由於潛在成長混合模型可事先控制題項測量誤差,因此在不同時間點的解釋變異量表現均較成長混合模型佳。
Growth mixture model is suitable for application in the heterogeneity of the structure of the longitudinal samples, longitudinal analysis of parameter estimation due to sample heterogeneity bias problem can be solved. However, the duplicate variable measurement for each measurement point in time, the growth mixture model is only a single item plus total points to carry out the construction of growth models, as well as follow-up to the heterogeneity of this measurement method and did not taking into account the measurement error.
Based on this, the present point in each measuring time by the repeated measurements of the plurality of items become latent growth mixture model, in considering the potential constructs and in the control of the premise of the measurement error. In the premise of the estimated measurement error, this study attempted to clarify at the goodness-of-fit of the model as well as the accuracy of clustering between growth mixture model and latent growth mixture model.
The study by the Monte Carlo simulation study to compare the measurement error affect growth mixture model and latent growth mixture model. Measurement error will affect the goodness-of-fit of the model as well as the clustering accuracy in growth mixture model and latent growth mixture model. When the measurement error is smaller, the goodness-of-fit and the accuracy of clustering the better. In addition, in the framework of the same parameter compare growth mixture model and latent growth mixture model found that latent growth mixture model due to greater freedom in the AIC, BIC, ABIC fit expansion quickly, but appropriate in the LMR, BLRT with indicators of performance than growth mixture model. While Entropy results are not consistent, latent growth mixture model performed better when the sample size is small, growth mixture model performed better when the sample size is large.
Through empirical construct of adolescent depression and hostility, the difference between growth mixture model and latent growth mixture model are similar to the simulation study. In addition, due to the latent growth mixture model can control measuring error, explained variance at different time points are better than growth mixture model.
王文中、吳齊殷(2003)。縱貫性研究中度量化的一些議題:以症狀檢核表SCL-90-R為例。中華心理衛生學刊,16(3),1-30。
王郁琮(出版中)。台灣青少年憂鬱發展軌跡、性別差異及違常行為之成長混合模型分析。教育與心理研究。
Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332.
Asparouhov, T., & Muthen, B. O. (2012). Auxiliary variables in mixture modeling: A 3-step approach using Mplus. Mplus Webnote 15.
Bakk, Z., Tekle, F., & Vermunt, J. K. (in press). Estimating the association between latent class membership and external variables using bias adjusted three-step approaches. Sociological Methodology.
Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods and Research, 16, 78-117.
Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: John Wiley.
Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation approach. Hoboken, NJ: Wiley.
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: The Guildford Press.
Burk, W. J., & Laursen, B. (2005). Adolescent perceptions of friendship and their associations with individual adjustment. International Journal of Behavioral Development, 29(2), 156-164.
Byrne, B. M. (2006). Structural equation modelling with EQS: Basic Concepts, Applications and Programming (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195-212.
Cho, S. J., Cohen, A. S., Kim, S. H., & Bottge, B. (2010). Latent transition analysis with a mixture item response theory measurement model. Applied Psychological Measurement, 34(7), 483-504.
Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral and health sciences. Hoboken, NJ: John Wiley & Sons.
Dekker, M., Ferdinand, R., van Lang, N., Bongers, I., van der Ende, J., & Verhulst, F. (2007). Developmental trajectories of depressive symptoms form early childhood to later adolescence: Gender differences and adult outcomes. Journal of Child Psychology and Psychiatry, 48, 657-666.
Derogatis(1983). SCL-90-R administration, scoring, and procedure manual-II. Towson, MD: Clinical Psychometric Research.
deRoon-Cassini, T. A., Mancini, A. D., Rusch, M. D., & Bonnano, G. A. (2010). Psychopathology and resilience following traumatic injury: A latent growth mixture model analysis. Rehabilitation Psychology, 55(1), 1-11.
Diggle, P. J., Liang, K. Y., & Zeger, S. L. (1998). Analysis of longitudinal data. New York, NY: Oxford University Press Inc.
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 Associates.
Frühwirth-Schnatter, S. (2006). Finite mixture and markov switching models. New York, NY: Springer.
Grimm, K. J., & Ram, N. (2009). A second-order growth mixture model for developmental research. Research in Human Development, 6, 121-143.
Gueorguieva, R., Mallinckrodt, C., & Krystal, J. H. (2011). Trajectories of depression severity in clinical trials of duloxetine: Insights into antidepressant and placebo responses. Archives of General Psychiatry, 68(12), 1227-1237.
Hancock, G. R., Kuo, W. L., & Lawrence, F. R. (2001). An illustration of second-order latent growth models. Structural Equation Modeling, 8, 470-489.
Hertzog, C., Lindenberger, U., Ghisletta, P., & von Oertzen, T. (2006). On the power of multivariate latent growth curve models to detect correlated change. Psychological Methods, 11, 244-252.
Hsieh, C., von Eye, A. A., Maier, K. S., Hsieh, H., & Chen, S. (in press). A unified latent growth curve model. Structural Equation Modeling.
Huang, D., Brecht, M., Hara, M., & Hser, Y. (2010). Influences of a covariate on growth mixture modeling. J Drug Issues, 40(1), 173-194.
Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16, 39-59.
Kaplan, D. (2008). An overview of Markov chain methods for the study of stage-sequential developmental processes. Developmental Psychology, 44, 457-467.
Lazarsfeld, P., & Henry, N. (1968). Latent Structure Analysis. New York, NY: Houghton Mifflin.
Lewinsohn, P. M., Rohde, P., Klein, D. N., & Seeley, J. R. (1999). The natural course of adolescent major depressive disorder: I. continuity into young adulthood. Journal of the American Academy of Child & Adolescent Psychiatry, 38(1), 56-63.
Lin, T. H., & Dayton, C. M. (1997). Model selection information criteria for non-nested latent class models. Journal of Educational and Behavioral Statistics, 22, 249-264.
Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrics, 88, 767-778.
Lu, I. R. R., Thomas, D. R., & Zumbo, B. D. (2005). Embedding IRT in Structural Equation Models: A comparison with regression based on IRT scores. Structural Equation Modeling, 12(2), 263-277.
Mash, E. J., & Wolfe, D. A. (2010). Abnormal Child Psychology. Wadsworth CENGAFE Learning.
McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 561-614). New York, NY: Plenum.
McArdle, J. J., & Epstain D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110-133.
MacCallum, R. (2003). Working with Imperfect Models. Multivariate Behavioral Research, 38(1), 113-139.
McLachlan, G., & Peel, D. (2000). Finite mixture models. New York, NY: John Wiley & Sons.
Meredith, W., & Tisak J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.
Muthén, B. (2001a). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New Developments and Techniques in Structural Equation Modeling (pp. 1-33). Lawrence Erlbaum Associates.
Muthén, B. (2001b). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/ latent growth modeling. In L. M. Collins & A. Sayer (Eds.), New Methods for the Analysis of Change (pp. 291-322). Washington, D. C.: APA.
Muthén, B. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29, 81-117.
Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling. Psychological Methods, 58, 525-543.
Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.
Muthén, B. (2006). Should substance use disorders be considered as categorical or dimensional? Addiction, 101(1), 6-16.
Muthén, B. (2008). Latent variable hybrids: Overview of old and new models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 1-24). Charlotte, NC: Information Age Publishing, Inc.
Muthén, B., & Asparouhov, T. (2009). Growth mixture modeling: Analysis with non-Gaussian random effects. In G. Fitzmaurice, M. Davidian, G. Verbeke, & G. Molenberghs (Eds.), Longitudinal data analysis (pp. 144-165). Boca Raton, FL: Chapman & Hall.
Muthén, B., Brown, C. H., Hunter, A., Cook, I. A., & Leuchter, A. F. (2011). General approaches to analysis of course: Applying growth mixture modeling to randomized trials of depression medication. In P. E. Shrout (Ed.), Causality and psychopathology: Finding the determinants of disorders and their cures (pp. 159-178). New York, NY: Oxford University Press.
Muthén, B., Brown, C. H., Masyn, K., Jo, B., Khoo, S. T., Yang, C. C., Wang, C. P., Kellam, S., Carlin, J., & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475.
Muthén, L. K., & Muthén, B. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), 599-620.
Muthén, L. K., & Muthén, B. (2006). Mplus user’s guide (4th ed.). Los Angeles, CA: Muthén & Muthén.
Muthén, B., & Shedden, K. (1999). Finite mixture modelling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
Nagin, D. S. (1999). Analyzing developmental trajectories: A semiparametric group-based approach. Psychological Methods, 4(2), 139-157.
Nylund, K. L., Asparouhov, T., & Muthén, B. (2006). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535-569.
Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J., & Chen, F. (2001). Monte Carlo experiments: Design and implementation. Structural Equation Modeling, 8, 287-312.
Piko, B. F., & Fitzpatrick, K. M. (2003). Depressive symptomatology among Hungarian youth: A risk and protective factors approach. American Journal of Orthopsychiatry, 73(1), 44-54.
Power, R. A., Muthén, B., Henigsberg, N., Mors, O., Placentino, A., Mendlewicz, J., Maier, W., McGuffin, P., Lewis, C. M., Uher, R. (2012). Non-random dropout and the relative efficacy of escitalopram and nortriptyline in treating major depressive disorder. Manuscript submitted for publication.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Rudolph, K. D., & Hammen, C. (1999). Age and gender as determinants of stress exposure, generation, and reactions in youngsters: A transactional perspective. Child Development, 57, 316-331.
Schafer, J. L., & Graham, J. M. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.
Sheng, Y., & Wikle, C. K. (2008). Bayesian multidimensional IRT models with a hierarchical structure. Educational and Psychological Measurement, 68, 413-430.
Tofighi, D., & Enders, C. K. (2007). Identifying the correct number of classes in a growth mixture model. In G. R. Hancock (Ed.), Mixture models in latent variable research (pp. 317-341). Greenwich, CT: Information Age.
Uher, R., Muthén, B., Souery, D., Mors, O., Jaracz, J., Placentino, A., Petrovic, A., Zobel, A., Henigsberg, N., Rietschel, M., Aitchison, K.J., Farmer, A., McGuffin, P. (2010). Trajectories of change in depression severity during treatment with antidepressants. Psychological Medicine, 40(8), 1367-1377.
Van Horn, M. L., Jaki, T., Masyn, K., Ramey, S. L., Smith, J. A., & Antaramian, S. (2009). Assessing differential effects: Applying regression mixture models to identify variations in the influence of family resources on academic achievement. Developmental Psychology, 45(5), 1298-1313.
Vermunt, J. K. (2004). Latent profile model. In M. S. Lewis-Beck, A. Bryman, & T. F. Liao (Eds.), The sage encyclopedia of social sciences research methods (pp. 554-555). Thousand Oakes, CA: Sage Publications.
Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450-469.
Yang, C. C. (1999). Finite mixture model selection with psychometric applications. (Doctoral dissertation, University of California, Los Angeles, 1999). Dissertation Abstracts International, 59(9-A), 3421.
Yang, C. C. (2006). Evaluating latent class analyses in qualitative phenotype identification. Computational Statistics & Data Analysis, 50(4), 1090-1104.
Yi, C., Wu, C., Chang, Y., & Chang, M. (2009). The psychological well-being of Taiwanese youth: School versus family context over the life course. International Sociology, 24(3), 397-429.

 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE