|
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.
|