Mark Twin once said There are three kinds
- Slides: 141
Mark Twin once said, “There are three kinds of lies: Lies, Damned lies, and Statistics. ”
結構方程模式 Structural Equation Modeling 楊志強 博士 國立台北教育大學國教系教授 師資培育中心主任 cyang@tea. ntue. edu. tw http: //tea. ntue. edu. tw/~cyang
Question 3 構面 題數 Cronbach’s α 自信 5 0. 87 焦慮 5 0. 90 滿意 5 0. 92 整體構面 15 0. 65
Question 7 層面名稱 題數 人數 平均數 標準差 整體組織績效 23 1062 105. 95 16. 51 目標達成 7 1062 32. 52 4. 93 行政效率 6 1062 27. 29 4. 72 作滿意 5 1062 23. 50 4. 28 組織適應 5 1062 22. 86 4. 64
Question 10 Levene's Test for Equality of Variances Equal variances assumed Equal variances not assumed F Sig. t df 0. 555 68 0. 35 4 Sig. (2 tailed) 0. 581 0. 554 0. 557 67. 68 0. 579 多數決? ?
R=-0. 22
Delete it and give it to qualitative researcher
R=0. 989
• 結構方程模式(Structural Equation Modeling, SEM) • 潛在變數模式(Latent Variable Models, LVM) • 線性結構關係模式(Linear Structural Relationship Model, LISREL) • 共變數結構分析(Covariance Structure Analysis) • 驗證性因素分析(Confirmatory Factor Analysis, CFA) 也是如來神掌最後一式?
r X 1 X 2 相關分析(Correlational Analysis)
x 1 e β 1 y y = b x + e 1 簡單線性迴歸(Simple Linear Regression)
e β 1 x 1 y β 2 x 2 多元線性迴歸(Multiple Linear Regression)
e x u 邏輯氏迴歸(Logistic Regression)
e x 1 u x 2 邏輯氏迴歸(Logistic Regression)
e 2 e 1 x 1 y 2 路徑分析(Path Analysis)
e 2 e 1 x 1 y 2 x 2 路徑分析(Path Analysis)
e x 1 y e x 1 within y within between 階層線性模式(Hierarchical Linear Model, HLM)
e 1 y 1 e 2 y 2 e 3 y 3 e 4 y 4 e 5 y 5 e 6 y 6 f 1 驗證性因素分析(Confirmatory Factor Analysis) f 2
e 1 y 1 e 2 y 2 e 3 y 3 e 4 y 4 e 5 y 5 e 6 y 6 r 1 f 1 r 2 f f 2 二階驗證性因素分析(2 nd order Confirmatory Factor Analysis)
d 1 x 1 d 2 x 2 d 3 x 3 f 1 f 2 zeta y 1 e 1 y 2 e 2 y 3 e 3 結構方程模式(Structural Equation Model, SEM)的general case 潛在變項路徑分析(Path Analysis with Latent Variables, PA-LV)
y 1 e 1 y 2 e 2 y 3 e 3 x 1 f x 2 zeta Multiple indicators multiple causes, MIMIC Model
e 1 y 1 e 2 y 2 e 3 y 3 C 潛在類別分析(Latent Class Analysis, LCA) Latent Profile Analysis, LPA Latent Trait Analysis, LTA
e 1 u 1 e 2 u 2 e 3 u 3 f 試題反應理論(Item Response Theory, IRT)
e 1 y 1 e 2 y 2 e 3 y 3 intercept slope 潛在成長曲線模式(Latent Growth Curve Model, LGC)
e 1 y 1 e 2 y 2 e 3 y 3 intercept C slope 混和潛在成長曲線模式(Mixture Latent Growth Curve Model, MLGC)
d 1 x 1 d 2 x 2 d 3 x 3 f 1 f 2 y 1 e 1 y 2 e 2 y 3 e 3 zeta group為干擾/情境變項(moderating variable) 多群組分析(Multiple Group Analysis)
d 1 x 1 d 2 x 2 d 3 x 3 f 1 within f 2 zeta y 1 e 1 y 2 e 2 y 3 e 3 within between 多階層結構方程模式(Multilevel Structural Equation Model, Multilevel SEM)
基本概念 再談探索性因素分析 Exploratory Factor Analysis (EFA)
因素旋轉 • 正交旋轉(Orthogonal rotation methods) – Quartimax – Varimax – Equimax • 斜交選轉(Oblique rotation methods ) – Oblimin – Promax – Orthoblique
因素負荷量的選取 樣本大小 0. 30 350 0. 35 250 0. 40 200 0. 45 150 0. 50 120 0. 55 100 0. 60 85 0. 65 70 0. 70 60 0. 75 50
結構方程模式 驗證性因素分析 � Confirmatory Factor Analysis (CFA)
δ 1 x 1 δ 2 δ 3 x 2 x 3 λ 11 λ 21 ξ 1 λ 31 x 1= λ 11 ξ 1+ δ 1 δ[delta] ; λ[lambda]; ξ[xi] x 2= λ 21 ξ 1+ δ 2 希臘字母讀音網站 x 3= λ 31 ξ 1+ δ 3 http: //wuyy. idv. tw/research/Greek. Letters/greekletter. htm
ε 1 x 1 ε 2 x 2 ε 3 ξ 1 x 3 Φ 12/ Φ 21 ε 4 x 4 ε 5 x 5 ε 6 x 6 Φ[phi] ξ 2
指標變項的討論:形成性指標 (formative indicators) [zeta] ζ y 1 y 2 η y 3 vpls下載 http: //www 2. kuas. edu. tw/prof/fred/vpls/about. PLSPC. htm
樣本大小的討論 • 樣本大小至少超過150個。 Rigdon, E. (2005). SEM FAQ. from http: //www. gsu. edu/~mkteer/html • 至少要為x觀察變項數目的10倍量或 15倍量。 Thompson, B. (2000). Ten commandments of structural equation modeling. In L. G. Grimm & P. R. Yarnold (eds. ), Reading and understanding more multivariate statistics (pp. 261 -283). Washington, DC: APA. 樣本大小亦取決於潛在變項的數目
常見電腦軟體 • • • LISREL SIMPLIS AMOS EQS Mplus • • • Mx Statistica SAS PROC CALIS COSAN LVPLS …
t=13 v 1 v 2 v 3 v 4 1/2(3+3)(3+3+1)=21 1 ε 1 x 1 1 ε 2 1 ε 3 v 7 ξ 1 w 2 x 3 c 1 1 ε 4 x 4 ε 6 1 w 3 x 6 v 8 ξ 2 x 5 ε 5 1 v 6 w 1 x 2 1 v 5 1 w 4 df=21 -13=8
整體模式適配度指標:外在品質評估 增值適配度指數 標準 NFI > 0. 9 RFI IFI > 0. 9 TLI/NNFI CFI > 0. 9 Diamantopoulos, A. & Siguaw, J. A. (2000). Introducing LISREL: A guide for the uninitiated. Thousand Oaks, CA: Sage.
Other Stuff
符號介紹 外衍觀察變項 內衍觀察變項 exogenous observed variable endogenous observed variable γ 11 x 1 y 1 Φ 21 x 2 Φ 31 Φ 32 x 3 ζ 1 γ 22 β 21 γ 23 觀察變項路徑分析 y 2 ζ 2 γ[gamma]; β[beta] Path analysis with observed variables (PA-OV)
外衍潛在變項 內衍潛在變項 exogenous latent variable endogenous latent variable δ 1 δ 2 δ 3 x 1 x 2 γ 11 ξ 1 η 1 x 3 潛在變項路徑分析 y 1 β 21 y 2 ζ 1 γ 21 Path analysis with latent variables (PA-LV) y 3 y 4 η 2 y 5 ζ 2 y 6 ε 1 ε 2 ε 3 ε 4 ε 5 ε 6
觀察變項路徑分析 Path analysis with observed variables (PA-OV)
潛在變項路徑分析 Path analysis with latent variables (PA-LV)
MIMIC模式 y 1 x 1 η 1 x 2 y 2 ζ 1 y 3 ε 1 ε 2 ε 3
More Stuff
• 潛在平均數結構分析(latent mean structure analysis)/多群組結構共變數與平均數模式( multigroup covariance and mean structure modeling) • 潛在成長曲線模式(Latent Growth Curve Model, LGC)
Feedback and Comment cyang@tea. ntue. edu. tw
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