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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/110778

    Title: 潛在移轉分析法與中位數法在長期追蹤資料分組的差異比較
    On classification of longitudinal data ─ comparison between Latent Transition Analysis and the method using Median as a cutpoint
    Authors: 李坤瑋
    Lee, Kun Wei
    Contributors: 江振東
    Chiang, Jeng Tung
    Lee, Kun Wei
    Keywords: 長期類別型追蹤資料
    Longitudinal categorical data
    Latent transition analysis
    Generalized estimating equation
    Logistic regression
    Date: 2017
    Issue Date: 2017-07-11 11:24:58 (UTC+8)
    Abstract: 當資料屬於類別型的長期追蹤資料(Longitudinal categorical data)時,除了可以透過廣義估計方程式(General estimate equation, GEE)來求解模型參數估計值外,潛在移轉分析(Latent transition analysis, LTA)法也是一種可行的資料分析方法。若資料的期數不多,也可以選擇將資料適度分群後使用羅吉斯迴歸分析(Logistic regression)法。當探討的反應變數為二元(Binary)型態,且觀察對象於每一期提供多個測量變數值的情況之下,廣義估計方程式與羅吉斯迴歸分析法的使用,文獻上常見先將所有的測量變數值加總後,以「中位數」作為分類的切割點。不同於以上兩種方法,潛在移轉分析法則是直接使用原始資料來取得觀察對象的潛在狀態相關訊息,因此與前二者的作法不同,可能導致後續的各項分析結果有所差異存在。
    Several methods can be used to analyze longitudinal categorical data, as among them Latent Transition Analysis (LTA), and Generalized Linear Models estimated by Generalized Estimating Equations (GEE) probably the most popular. In addition, if the number of periods is two, then with certain grouping of data, the Logistic Regression can also be applied to perform the analyses.
    When there are more than one manifest response variable for each study subject, LTA is able to classify the subjects in terms of the original manifest response variables and proceeds with necessary analyses. On the other hand, GEE method and Logistic Regression lack the flexibility, and require certain transformation to transform the manifest response variables into a categorical response variable first. One common way to form a binary response is to sum all manifest variables, and then taking median as a cut-point.
    In this study, we explore the differences of the classification resulted from LTA directly and using median as a cut-point through simulations. An empirical study is also provided to illustrate the classification differences, and the differences on the subsequent analyses using LTA, GEE method, and Logistic Regression approach.
    Reference: 1.Chang, F.C., Chiu, C.H., Lee, C.M., Chen, P.H., and Miao, N.F. (2014). Predictors of the initiation and persistence of Internet addiction among adolescents in Taiwan. Addictive Behaviors, 39, 1434–1440
    2.Chung, H., Park, Y.S., and Lanza, S.T. (2005). Latent transition analysis with covariates: pubertal timing and substance use behaviours in adolescent females. Statistics in Medicine, 24, 2895-2910
    3.Collins, L.M., and Wugalter, S.E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27, 131-157.
    4.Diggle, P.J., Heagerty , P., Liang, K.Y., and Zeger, S.L. (2002). Analysis of longitudinal data (Second Edition). Oxford University Press.
    5.Kleinbaum, D.G., and Klein, M. (2010). Logistic regression: a self-learning text (Third Edition). Springer Dordrecht Heidelberg London New York.
    6.Lanza, S.T., Dziak, J.J., Huang, L., Wagner, A.T., and Collins, L.M. (2015). Proc LCA & Proc LTA users' guide (Version 1.3.2). University Park: The Methodology Center, Penn State. Available from methodology.psu.edu.
    7.Lazarsfeld, P.F., and Henry, N.W. (1968). Latent structure analysis. Boston: Houghton Mifflin.
    8.Liang, K.Y., and Zeger, S.L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73, 13-22
    9.SAS Institute Inc. (2016). SAS/STAT® 14.2 User’s Guide. Cary, NC: SAS Institute Inc.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104354003
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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