an overview of structural equation models with latent … · 2007-03-05 · an overview of...

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An Overview of STRUCTURAL EQUATION MODELS

WITH LATENT VARIABLES

Kenneth A. Bollen Odum Institute for Research in Social Science

Department of Sociology University of North Carolina

at Chapel Hill

Presented at the Miami University Symposium on Computational Research - March 1-2, 2007, Miami University, Oxford, OH.

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I.What are Structural Equation Models? (SEM) II. Fundamental Hypothesis [E = E(2)] III. Illustrations IV. Three Common Types of SEM V. Overview of Modeling Steps VI. SEM with Means and Intercepts VII. Software VIII. Empirical Example

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I. What are SEM? General Statistical Model Special Cases: ANOVA & ANCOVA Multiple Regression Econometric Models Path Analysis Factor Analysis Etc.

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SEM Allow: 1. Latent & Observed Variables 2. Random & Nonrandom Errors 3. Errors-in-Variables Regressions 4. Multiple Indicators 5. Restrictions on Parameters 6. Test of Model Fit 7. Nonnormal Variables

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II. Fundamental Hypothesis Ho: E = E(2) E = Population Covariance Matrix 2 = Vector of Parameters E(2) = Model Implied Covariance Matrix

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III. Illustrations A. Simple Regression as a SEM

y x

x y

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III.A.

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III.A.

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III.B. Simple Factor Analysis

y1 = η + ε1 y2 = η + ε2

•

y1 y2

•1 •2

1 1

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III.B.

E=E(2)

⎥⎥⎦

⎤

⎢⎢⎣

⎡

+

+=

⎥⎥⎦

⎤

⎢⎢⎣

⎡)()()(

)()(

)(),()(

2

1

221

1

εηη

εη

VARVARVAR

VARVAR

yVARyyCOVyVAR

e.g., COV(y1,y2)=COV(η + ε1, η + ε2) =COV(η,η)

=VAR(η)

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Confirmatory Factor Analysis: Air Quality Example

y1 = η1 + ε1 y2 = λ21η1 + ε2 y3 = λ31η1 + ε3 y4 = λ41η1 + ε4

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Latent Variables

Variables of Interest But Not Directly Measured

Common in Sciences: Intelligence, Worker Productivity, Diseases, Happiness, Value of House, Carrying Capacity, “Free” Market, Disturbance Variables

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III.C. Simple General Model

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III.C.

[ ] E = VAR (y) COV (x1,y) VAR (x1)

COV(x2, y) COV(x2, x1) VAR(x2)

E(2) =

[ ](2VAR(>) + VAR(.) (VAR(>) VAR(>) + VAR(*1) (VAR(>) VAR(>) VAR(>) + VAR(*2)

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IV. Three Types of SEM A. Classical Econometric 1. Multiequation System y=βy + Γx + ζ 2. No Measurement Error y=η x=ξ

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Felson and Bohrnstedt (1979) Model of Perceived Attractiveness and Perceived Academic Performance (N = 209) x1 = Grade Point Average x2 = Deviation of height from mean by grade and sex x3 = Weight adjusted for height x4 = Physical attractiveness rated by children outside class y1 = Perceived academic ability, based on class-mates’ ratings y2 = Perceived attractiveness, classmates’ ratings y1 = $12y2 + (11x1 + .1 y2 = $21y1 + (22x2 + (23x3 + (24x4 + .2 E(.i) = 0 COV(.1, .2) … 0 COV(.i,xj) = 0 for i=1,2; j=1,2,3,4

x1

x2

x3

x4

y2

y1 •1

•2

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IV.B. Confirmatory Factor Analysis 1. Latent Variables 2. Measurement Errors y = Λy η + ε y = vector of observed indicators η = vector of latent variables (or “factors”) ε = vector of “measurement errors” E(ε) = 0 COV(η ,ε) = 0

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IV.B. Confirmatory Factor Analysis 1. Latent Variable Model η= Βη + Γξ + ζ η= latent endogenous variables ξ = latent exogenous variables ζ = disturbance vector Β = coefficient matrix for η or η effects Γ = coefficient matrix for ξ or η effects E(ζ) = 0, COV(ξ,ζ)=0

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IV.C. General SEM 2. Measurement Model

y = Λy η + ε E(ε)=0 x = Λx ξ+ δ E(δ)=0 y = indicators of η Λy=factor loadings of η or y ε = errors of measurement for y x= indicators of ξ Λx= factor loadings of ξ or x δ = errors of measurement for x δ, ε, ζ,ξ are uncorrelated

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IV.C. General SEM

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IV.C.

ξ1 = Industrialization 1960 xi = Indicators of industrialization 01 = Democratic political structure 1960 02 = Democratic political structure 1965 yi = Indicators of political democracy

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V. Overview of Steps in Modeling A. Specification B. Implied Covariance Matrix C. Identification D. Estimation E. Testing and Diagnostics F. Respecification

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V.A. Specification 1. What latent variables? 2. Relation between latent variables?

3. What measures? 4. Relation between measures and latent variables?

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V.B. Implied Covariance Matrix Ho: G = G(2) Each Model => G(2)

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e.g.,

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V.C. Identification Unique values for parameters? If E(21) =E(22), then 21 = 22 Identification VAR(y) = 21 + 22

21 22 VAR(y) ____________________________________ 5 5 10 7 3 10 9 1 10

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Establishing Identification 1. Algebraic Means E =E(2) solve for 2 2. Identification Rules 3. Empirical Tests

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e.g.,

Identified? Yes, Three Indicator Rule

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V.D. Estimation Ho: E =E(2)

S sample estimator of E E(θ̂ ) sample estimator of E(2) Choose θ̂ so E(θ̂ ) close to S

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e.g., y1 = x1 + .1

E =E(2)

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡ +=

⎥⎥⎦

⎤

⎢⎢⎣

⎡1111

1111

221

1

)()()(

φφ

ψφ

xVARyxCOVyVAR

Goal to find 2

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⎥⎥⎦

⎤

⎢⎢⎣

⎡=

46

610S

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡ +

=Σ1111

111111

ˆˆ

ˆˆˆ

)ˆ( φφ

φψφ

θ

Find 11φ̂ and 11ψ̂

Make E(θ̂ ) close to S

Say 11φ̂ = 7 , 11ψ̂ = 3

⎥⎥⎦

⎤

⎢⎢⎣

⎡=Σ

77

710)ˆ(θ

⎥⎥⎦

⎤

⎢⎢⎣

⎡

−−

−=Σ−

31

10)ˆ(θS

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ESTIMATORS A. Full Information 1. Maximum Likelihood (ML) 2. Generalized Least Squares (GLS) 3. Unweighted Least Squares (ULS) 4. Weighted Least Squares (WLS) [Arbitrary Distribution Function (ADF)] B. Limited Information 1. Two-Stage Least Squares (2SLS)

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V.E. Testing and Diagnostics Ho: E =E(2)

P2 Test Tm = (N-1)* Fit Function Min. df = ½ (p+q) (p+q+1) -# of parameters

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2. Overall Model Fit

BIC= Tm – df ln(N)

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V.E. Testing and Diagnostics

3. Residuals (S-E(θ̂ ))

4. Component Fit

5. Statistical Power

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e.g.,

P2 = 16.0 df = 2 p = .0003

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VI.E. Respecification

1. Substantive-Based Revisions 2. Lagrangian Multiplier 3. WALD Ê 4. Residuals (S - G(2))

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e.g.,

P2 = 4.2 df = 1 p = .04

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Steps in Modeling

1. Specification

2. Implied Covariance Matrix

3. Identification

4. Estimation

5. Testing

6. Respecification

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VI. SEM with Means and Intercepts

A. Specification

1. Latent Variable Model

η= "0 + Βη + Γξ + ζ

intercept

e.g, η1= "01 + Β12η2 + (11ξ1 + ζ1

η2= "02 + Β23η3 + (22ξ2 + ζ2

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VI. SEM with Means and Intercepts

A. Specification

2. Measurement Model

y = "y + Λy η + ε x = "x + Λx ξ+ δ

intercept

e.g., y1 = "y1 + λy11 η1 + ε1 y2 = "y2 + λy21 η1 + ε2 x1 = "x1 + λx11 ξ1+ δ1

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VI. SEM with Means and Intercepts

B. Implied Moments

1. Implied Covariance Matrix E =E(2)

2. Implied Mean Vextor μ = μ(θ) Mean of observed variables= Model implied means

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VI. C. Identification Still have Σ, covariance matrix of observed variables. New parameters: intercepts ( αη,αy,αx) & means of latent ξ (k) New observed variable information: Means of observed variables (μy μx) Can we use Σ, μy, and μx to find unique values of all model parameters? Yes identified

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VI. D. Estimation Same as before (e.g., ML, WLS, 2SLS) E. Testing Same as before (e.g., chi-square, IFI, etc.) F. Respecification Same as before

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VII. Software LISREL [ PRELIS, SIMPLIS] AMOS EQS Mplus Proc Calis in SAS

Mx EzPath AUFIT LINCS

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VIII. Empirical Example Robins, P. K. and R. W. West. 1977. Measurement Errors in the Estimation of Home Value. Journal of the American Statistical Association 72, 290-94.

x1

y1

x3

ζ1

η1

ε3

Figure Robins and West (1977, JASA) Value of Home (η1) with Causal Indicators of lot size (x1), square footage (x2), number of rooms (x3), etc. (xq), and Effect Indicators of Appraised Value (y1), Owner Estimate (y2), & Assessed Value (y3), and Disturbances (ζ1, ε1 to ε3 )

y2

ε1

x2

xq

y3

ε2

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VIII. Empirical Example Bollen, K.A. 1993. Liberal Democracy: Validity and Method Factors in

Cross-National Measures. American Journal of Political Science, 37:1207-1230.

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