國立高雄師範大學 特殊教育研究所 104學年度教學綱要
科目名稱:結構方程模式 □必修 R選修 教師:吳裕益
任課班級:博士班 助教:
每學期開課學分數:上學期 學分 下學期 3 學分
總學分數: 3 學分 每週上課時數: 3 小時
連繫電話: 2314 上課地點:
Office hours:星期三10:00-12:00 (研究室)
E-Mail: t1850@nknucc.nknu.edu.tw
一、課程目標
※※請遵守智慧財產權觀念、不得非法影印※※
本課程目標
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核心能力指標(博士班)
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1. 具備特殊教育通論領域之進階專業知能
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★★★★★
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2. 具備特殊教育特定領域之進階專業知能
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★★★
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3. 具備研究批判與發表之能力
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★★
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4. 具有學術倫理
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註:★表示比重10%(總計100%)
二、教學目標
1.瞭解結構方程模式之原理及在教育研究之應用。(核心能力[博]2~4)
2.實際應用結構方程模式軟體來分析研究資料及撰寫研究報告。(核心能力[博]2~4)
三、評量方式
課堂討論(核心能力[博]2~3)、作業(核心能力[博]2~3)、撰寫報告(核心能力[博]2~4)
四、主要讀本及參考書目
1.主要讀本
吳裕益(未出版):結構方程模式的理論與應用。高師大特教系。
李茂能(2006):結構方程模式軟體Amos之簡介及其在測驗編製之應用。台北:心理。
李茂能(2009):圖解AMOS在學術研究之應用。台北:五南。
余民寧(2006):潛在變項模式:SIMPLIS的應用。台北:高等教育。
Arbuckle,J. L. (2007). AMOS 7.0 user’s guide. Chicago: SmallWaters.
2.其他參考書目
邱浩政(2003):結構方程模式:LISREL的理論、技術與應用。台北:雙葉。
黃芳銘(2002):結構方程模式理論與應用。台北:五南。
Bollen, K. A. (1989). Structural equations with latent variables. NY: Wiley.
Byrne, B. M. (2001). Structural equation modeling with AMOS. NJ: LEA.
Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., and Alpert, A. (1999). An introduction to latent variable growth curve modeling: Concepts, Issues, and applications. NJ: LEA.
Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concept, issues, and applications . CA: Sage.
Kaplan, D. K. (2000). Structural equation modeling: Foundations and extensions. CA: Sage.
Kline, R.B. (1998). Principle and practice of structural equation modeling. NY: Guilford.
Little, T.D., Schnabel, K.U., and Baumert, J. (Eds.). (2000). Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples. NJ: LEA.
Marcoulides, G. A., and Schumacker, R. E. (Eds.). (1996). Advanced structural equation modeling: issues and techniques. NJ: LEA.
Marcoulides, G. A., and Schumacker, R. E. (Eds.). (2001). New developments and techniques in structural equation modeling. NJ: LEA.
Schumacker, R.E., and Marcoulides, G. A. (Eds.). (1998). Interaction and nonlinear effects in structural equation modeling. NJ: LEA.
五、教學進度
週別
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內 容
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結構方程模式基本概念
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2
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結構方程模式基本概念
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3
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結構方程模式適合度之評鑑
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4
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結構方程模式適合度之評鑑
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AMOS軟體簡介
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AMOS軟體在回歸及徑路分析之應用
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AMOS軟體在驗證性因素分析之應用
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AMOS在多群體分析之應用
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AMOS在多群體分析之應用
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AMOS在共變數分析之應用
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AMOS在多模式分析之應用
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AMOS在縱貫資料分析之應用
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AMOS在縱貫資料分析之應用
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AMOS的貝氏SEM
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LISREL軟體應用
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LISREL軟體應用
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PLS軟體應用
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PLS軟體應用
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結構方程模式其他閱讀資料(Readings about SEM)
通論性SEM(General SEM)
MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201-226.
Hershberger, S. (2003). The growth of structural equation modeling: 1994-2001. Structural Equation
Modeling, 10, 35-46.
Williams, L. J., Edwards, J. R., & Vandenberg, R. J. (2003). Recent advances in causal modeling methods for organizational and management research. Journal of Management, 29(6), 903-936.
Andrew J. Tomarken and Niels G.Waller (2005). Structural equation modeling: Strengths, limitations, andmisconceptions, Annual Review of Clinical Psychology, 1: 31–65.
SEM分析程序(Procedure for SEM)
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and
recommended two-step approach. Psychological Bulletin, 103, 411–423.
Hayduk, L. A., & Glaser, D. N. (2000). Jiving the Four-Step, Waltzing Around Factor Analysis, and Other Serious Fun. Structural Equation Modeling, 7(1), 1–35.
Mulaik, S. A., &Millsap, R. E. (2000). Doing the Four-Step Right. Structural Equation Modeling, 7, 36-73.
Bollen, K. A. (2000). Modeling Strategies: In Search of the Holy Grail. Structural Equation Modeling, 7, 74-81.
Bentler, P. M. (2000). Rites,Wrongs, and Gold in Model Testing. Structural Equation Modeling, 7, 82-91.
Herting, J. R., & Costner, H. L. (2000). Another Perspective on "The Proper Number of Factors" and the Appropriate Number of Steps. Structural Equation Modeling, 7, 92-110.
Hayduk, L. A., & Glaser, D. N. (2000). Doing the Four-Step, Right-2-3, Wrong-2-3: A Brief Reply to
Mulaik and Millsap; Bollen; Bentler; and Herting and Costner. Structural Equation Modeling, 7, 111-123.
SEM發現報告撰寫(Reporting SEM findings)
McDonald, R. P., & Ho, M. R. (2002). Principles and practice in reporting structural equation analysis.
Psychological Methods,7, 64-82.
Jackson, D. L., Gillaspy, Jr., J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory
factor analysis: An overview and some recommendations. Psychological Methods,14(1), 6–23.
Schreiber, J. B. (2008). Core reporting practices in structural equation modeling. Administrative Pharmacy, 4, 83-97.
模式適合度(Model fit)
Hu, Li-tze, & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:
conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual
Differences, 42, 815-824.
樣本大小與檢定力議題(Sample size and Power issue)
Kim, K. H. (2005). The relation among fit indexes, power, and sample size in structural equation modeling. Structural Equation Modeling, 12(3), 368-90.
MacCallum, R. C, & Browne, M. W., & Cai, L. (2006). Testing differences between nested covariance
structure models: power analysis and null hypotheses. Psychological Methods, 11(1), 19-35.
一階及高階驗證性因素分析(CFA & HCFA)
Jackson, D. L. (2007). The effect of the number of observations per parameter in misspecified confirmatory factor analytic models. Structural Equation Modeling, 14(1), 48-76.
DiStefano, C. (2002). The impact of categorization with confirmatory factor analysis. Structural Equation Modeling, 9(3), 327-346.
Koufteros, X., Babbar, S., & Kaighobadi, M. (2009). A paradigm for examining second-order factor
models employing structural equation modeling. International Journal of Production Economics, 120, 633-652.
Silvia, P. J. (2008). Another look at creativity and intelligence: Exploring higher-order models and probable 3 confounds. Personality and Individual Differences, 44(4), 1012-1021.
測量不變性與題目分群(Measurement Invariance and parceling)
Schmitt, N., & Kuljanin, G. (2008). Measurement invariance: Review of practice and implications. Human Resource Management Review, 18(4), 210-222.
Cheung. G. W. (2008). Testing Equivalence in the structure, means, and variances of higher-order
constructs with structural equation modeling. Organizational Research Methods, 9(3), 369-403.
Williams, L. J., & O'Boyle Jr, E. H. (2008).Measurement models for linking latent variables and indicators: A review of human resource management research using parcels. Human Resource Management Review, 18, 233-242.
Meade, A. W., & Kroustalis, C. M. (2006). Problems with item parceling for confirmatory factor analytic: Tests of measurement invariance. Organizational Research Methods 9(3), 369-403.
SEM交互作用與中介效果分析(Interaction and Medication in SEM)
Marsh, H. W., Wen, Z., & Hau, K. T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9, 275–300.
Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and
product terms: Implications for modeling interactions among latent variables. Structural Equation
Modeling, 13, 497–519.
Marsh, H. W., Wen, Z., & Hau, K. T., Little, T. D., Bovaird, J. A., & Widaman, K. F. (2007).
Unconstrained Structural Equation Models of Latent Interactions: Contrasting Residual- and
Mean-Centered Approaches. Structural Equation Modeling , 14(4), 570–580.
Chan, W. (2007). Comparing indirect effects in SEM: A sequential model fitting method using
covariance-equivalent sepcifications. Structural Equation Modeling , 14(2), 326-346.
多層次SEM(Multilevel Structural Equation Modeling)
Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008). The
Multilevel Latent Covariate Model: A New, More Reliable Approach to Group-Level Effects in
Contextual Studies. Psychological Methods, 13(3), 203–229.
Zyphur, M. J., Kaplan, S. A., & Christian, M. S. (2008). Assumptions of Cross-Level Measurement and
Structural Invariance in the Analysis of Multilevel Data: Problems and Solutions. Group Dynamics: Theory, Research, and Practice, 12(2), 127-140.
混合模式(Mixture Modeling)
Steinley. D., & McDonald, R. P. (2007). Examining factor score distributions to determine the nature of latent spaces. Multivariate Behavioral Research, 42(1), 133-156.
Lubke, G. H. & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models.
Psychological Methods, 10, 21-39.
Bauer, D. J., & Curran, P. J. (2004). The integration of continuous and discrete latent variable models:
Potential problems and promising opportun