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第一天大綱
1. The basic of Structural Equation Modeling
1. The growth of SEM
2. SEM terms and symbols
2. Amos的操作環境與模型建立
1. Amos introduction and operation
2. 路徑分析 (path analysis)
3. 迴歸分析 (regression analysis)
4. 結構模型分析 (SEM analysis)
5. 圖形調整及輸出
6. SEM model report
7. Goodness of fit (GOFs)
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第一天大綱
3. SEM identification
1. How many sample size are need?
2. Rules for determining SEM model
parameters.
3. Model identification
4. The estimated principle of SEM
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第二天大綱
4. 驗證式因素分析
(Confirmatory Factor Analysis, CFA)
1. 一階驗證性因素分析(first order)
2. 組成信度 (CR)與平均變異數萃取量 (AVE)
3. 收斂及區別效度
4. Second order SEM model
5. Using covariance matrix analyze SEM
1. Making covariance matrix
2. Analyze SEM
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The growth of SEM
(Herhberger, 2003)
• SEM has become the preeminent
multivariate method of data analysis.
• SEM has become the preeminent place in
which to publish developments in SEM.
• As long as SEM continues to respond
to the needs of scientific workers, it
will continue to flourish, from its
present, healthy young adulthood
into, perhaps, an immortality that
bypasses old age.
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Publishing Research in Marketing
Journals Using SEM
1. Papers not reporting an SEM model have a
greater likelihood of getting rejected.
2. Papers reporting an SEM model are rated more
favorably by reviewers.
3. The use of SEM influences reviewer
recommendations and paper quality
4. AMOS applications in marketing have a
greater chance of being rejected.
5. Goodness (badness) of fi t indices are
positively (negatively) related to paper quality
and reviewer recommendations.
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Model Fit categories
• Absolutely Model Fit
– 可解釋為樣本共變異數矩陣被模型共變異數矩陣解釋
的比例,類似於R2。
– X2, X2/DF, GFI, AGFI, SRMR, RMSEA
• Incremental Model Fit
– 研究模型的配適度與統計基本模型比較改善的程度,
基本模型指的是獨立(虛無)模型。
– NFI, RFI, IFI, TLI (NNFI), CFI
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Model Fit categories
• Parsimonies Model Fit
– 決定研究模型是否太過複雜,同一筆樣本資料但相似
的模型以精簡指標愈大者愈好。
– PGFI, PNFI, PCFI
– 使用率極低
• Competitive Model Fit
– 主要用於非巢狀模型比較,配適指標愈小愈好,沒有
標準值。
– ECVI、AIC及BIC (常用)
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Why P-value is sig.?
• In practice, chi-square is not considered
to be a very useful fit index by most
researchers.
• It is difficult to get a nonsignificant chi-
square (indicative of good fit) when
samples sizes are much over 200 or so.
Maruyama, G. (1998). Basics of Structural Equation Modeling.
Thousand Oaks CA: Sage.
Tanaka, J.S. (1993). Multifaceted conceptions of fit in structural
equation models. In K.A. Bollen, & J.S. Long (eds.), Testing
structural equation models. Newbury Park, CA: Sage.
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How many sample size are need?
• rule of 10: 10 observations per indicator
(Barclay, et al. 1995; Chin 1998; Chin, and Newsted
1999; Kahai and Cooper 2003)
• Bentler and Chou (1987) suggest sample
size to free parameter are 5:1 (資料符合常
態,無遺漏值及例外值情形下),否則要10倍的樣
本數
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How many sample size are need ?
Hair, Joseph F., William C. Black , Barry J. Babin , Rolph E. Anderson. (2009)
Multivariate Data Analysis (7th Edition). Englewood Cliffs, N.J. Prentice Hall.
小樣本容易導致收斂失敗、不適當的解(違犯估
計) 、低估參數值及錯誤的標準誤
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Rule 1: All variances of independent variables are
model parameters.
Rule 2 : All covariances between independent
variables are model parameters
Rule 3. All factor loadings connecting the latent
variables with their indicators are model
parameters.
Rule 4. All regression coefficients between
observed or latent variables are model parameters
Rule 5. The variances of, and covariances between,
dependent variables as well as the covariances
between dependent and independent variables are
never model parameters.
Rule 6. For each latent variable included in a model,
the metric of its latent scale needs to be set.
SEM參數設定原則
(Raycov & Marcoulides, 2006)
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The variances of, and covariances
between, dependent variables as well
as the covariances between dependent
and independent variables are never
model parameters