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題名：混合因素分析對群體異質性之探索：以國中生學業困擾二元資料為例
書刊名：教育與心理研究
作者：王郁琮 引用關係／溫福星 引用關係
作者(外文)：Wang, Lawrence Yu-chung／Wen, Fur-hsing
出版日期：2011
卷期：34:3
頁次：頁37-63
主題關鍵詞：混合因素分析；群體異質性；學業困擾；Factor mixture model；Population heterogeneity；Learning disturbance
原始連結：連回原系統網址new window
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  • 被引用次數被引用次數:期刊(5) 博士論文(0) 專書(0) 專書論文(0)
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  • 點閱點閱:151
本研究示範如何應用混合因素模式處理群體異質性。文中除了展示混合因素分析模式之特性，並比較該模式與類別因素分析及潛在類別分析之間的差異。本研究樣本為1,703位國中一至三年級學生，樣本資料為有關於學業困擾的議題。研究結果發現，二因素二類別之混合模式為解釋國中生五種代表性學習困擾行為之最佳模式：學習困擾可以分為「外在焦慮困擾」與「內在動機困擾」等兩種連續因素，而其困擾潛在類別可以分為「高困擾」與「低困擾」兩種。混合因素模式為近年來新興之統計技術，本研究成功地示範如何利用混合因素模式在實徵二元資料分析上，並對未來研究方向提出若干建議。
One essential assumption of traditional multivariate analysis is population homogeneity and model parameters estimated will be inaccurate if the assumption is violated. The purpose of this study was to demonstrate using factor mixture model (FMM) to explore population heterogeneity. FMM is a combination of factor analysis and latent class analysis, therefore, this study also described characteristics of FMM in comparison to categorical factor analysis (CaCFA) and latent class analysis (LCA). One thousand seven hundred and three middle school students of all grades were surveyed regarding their learning behaviors and binary data were collected and analyzed with CaCFA, LCA and FMM models. Results show that the five typical learning behaviors investigated can be explained best by a two-factor and two-class FMM model. The two factors are “external learning anxiety” and “internal lack of motivation” whereas the two latent classes are “high” and “low” disturbance groups. Factor mixture model was a newly developed statistical model and most recent studies investigating characteristics of FMM have used simulated data. This study successfully utilizes FMM to a real life data set from an empirical study of learning behaviors. From a statistical aspect, FMM produces factor scores similar to those of CaCFA. However, the results of latent classifications from FMM are somewhat different from those of LCA. Suggestions and recommendations are given for future studies.
期刊論文
1.Rost, J.(1990)。Rasch Models in Latent Classes: An Integration of Two Approaches to Item Analysis。Applied Psychological Measurement，14(3)，271-282。  new window
2.Bauer, D. J.、Curran, P. J.(2003)。Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes。Psychological Methods，8(3)，338-363。  new window
3.Muthen, B.、Shedden, K.(1999)。Finite mixture modeling with mixture outcomes using the EM algorithm。Biometrics，55(2)，463-469。  new window
4.Kim, Youngkoung、Muthén, Bengt O.(2009)。Two-part factor mixture modeling: Application to an aggressive behavior measurement instrument。Structural Equation Modeling，16(4)，602-624。  new window
5.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。  new window
6.Lubke, G. H.、Muthén, B. O.(2005)。Investigating population heterogeneity with factor mixture models。Psychological Methods，10，21-39。  new window
7.Lubke, G.、Muthén, B. O.(2007)。Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters。Structural Equation Modeling: A Multidisciplinary Journal，14(1)，26-47。  new window
8.Lubke, G.、Neale, M. C.(2006)。Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood?。Multivariate Behavioral Research，41(4)，499-532。  new window
9.Muthen, B.(2006)。Should substance use disorders be considered as categorical or dimensional。Addiction，101(1)，6-16。  new window
10.Yung, Y.(1997)。Finite mixtures in confirmatory factor analysis models。Psychometrika，62(3)，297-330。  new window
11.Flora, D. B.、Curran, P. J.(2004)。An empirical evaluation of alternative methods of estimation for comfirmatory factor analysis with ordinal Data。Psychological Methods，9(4)，466-491。  new window
12.Hicks, B. M.、Krueger, R. F.、Iacono, W. G.、McGue, M.、Patrick, C. J.(2004)。Family transmission and heritability of externalizing disorders: A twin-family study。Archives of General Psychiatry，61，922-928。  new window
13.Lubke, G.、Muthen, B.、Moilanen, I.、McGough, J.、Loo, S.、Swanson, J.、Yang, M. et al.(2007)。Subtypes versus severity differences in the attention-deficit/hyperactivity disorder in the northern finnish birth cohort。Journal of the American Academy of Child and Adolescent Psychiatry，46，1584-1593。  new window
學位論文
1.Clark, S. L.(2010)。Mixture modeling with behavioral data(博士論文)。University of California。  new window
圖書
1.Langenheine, R.、Rost, J.(1988)。Latent trait and latent class models。New York：Plenum Press。  new window
2.Bartholomew, D. J.、Steele, F.、Moustaki, I.、Galbraith, J. I.(2002)。The Analysis and Interpretation of Multivariate Data for Social Scientists。Boca Raton, FL：Chapman & Hall/CRC。  new window
3.Peel, David、McLachlan, Geoffrey J.(2000)。Finite Mixture Models。New York, NY：John Wiley & Sons Inc.。  new window
4.Muthen, L. K.、Muthen, B. O.(1998)。Mplus user's guide。Los Angeles, CA：Muthen, L. K., Muthen, B. O.。  new window
5.胡秉正、何福田(1997)。國民中學行為困擾調查表。臺北市：中國行為科學社。  延伸查詢new window
6.Bartholomew, D. J.、Knott, M.(1999)。Latent variables models and factor analysis。Arnold。  new window
7.Heinen, T.(1996)。Latent class and discrete latent trait models: Similarites and differences。Thousand Oaks, CA。  new window
8.Molenaar, P. C. M.、van Eye, A.(1994)。On the arbitrary nature of latent variables。Latent variable analysis: Applications for developmental research。Thousand Oaks, CA。  new window
9.Rost, J.(1989)。Rasch models and latent class models for measuring change with ordinal variables。Multiway data analysis。Amsterdam。  new window
10.Rost, J.、von Davier, M.(1995)。Mixture distributed Rasch models。Rasch models: Foundations, recent developments, and applications。New York。  new window
11.Meredith, W.、Horn, J.(2001)。The role of factorial invariance in modeling growth and change。New methods for the analysis of change。Washington, DC。  new window
 
 
 
 
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