1 general structural equation (lisrel) models week #2 class #2
TRANSCRIPT
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General Structural Equation (LISREL) Models
Week #2 Class #2
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Today’s classToday’s class
Latent variable structural equations Latent variable structural equations in matrix form (from yesterday)in matrix form (from yesterday)
Fit measuresFit measures SEM assumptionsSEM assumptions What to write up What to write up LISREL matricesLISREL matrices
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From yesterday’s lab:
Ach1
REDUCE e1
1
1
NEVHAPP e21
NEW_GOAL e31
IMPROVE e41
ACHIEVE e51
CONTENT e61
Reference indicator:
REDUCE
Regression Weights: Estimate S.E. C.R. Label ------------------- -------- ------- ------- -------
REDUCE <---------- Ach1 1.000 NEVHAPP <--------- Ach1 2.142 0.374 5.721 NEW_GOAL <-------- Ach1 -2.759 0.460 -5.995 IMPROVE <--------- Ach1 -4.226 0.703 -6.009 ACHIEVE <--------- Ach1 -2.642 0.450 -5.874 CONTENT <--------- Ach1 2.657 0.460 5.779
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From yesterday’s lab:
Ach1
REDUCE e1
1
1
NEVHAPP e21
NEW_GOAL e31
IMPROVE e41
ACHIEVE e51
CONTENT e61
Reference indicator:
REDUCE
Standardized Regression Weights: Estimate-------------------------------- --------
REDUCE <---------- Ach1 0.138 NEVHAPP <--------- Ach1 0.332 NEW_GOAL <-------- Ach1 -0.541 IMPROVE <--------- Ach1 -0.682 ACHIEVE <--------- Ach1 -0.410 CONTENT <--------- Ach1 0.357
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From yesterday’s lab:
Reference indicator:
REDUCE
Ach1
REDUCE e1
1
1
NEVHAPP e21
NEW_GOAL e31
IMPROVE e41
ACHIEVE e51
CONTENT e61
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Regression Weights: Estimate S.E. C.R. Label
------------------- -------- ------- ------- -------
REDUCE <---------- Ach1 1.000
NEVHAPP <--------- Ach1 -113.975 1441.597 -0.079
NEW_GOAL <-------- Ach1 215.393 2717.178 0.079
IMPROVE <--------- Ach1 373.497 4711.675 0.079
ACHIEVE <--------- Ach1 211.419 2667.067 0.079
CONTENT <--------- Ach1 -155.262 1961.974 -0.079
Standardized Regression Weights: Estimate-------------------------------- --------
REDUCE <---------- Ach1 0.002 NEVHAPP <--------- Ach1 -0.223 NEW_GOAL <-------- Ach1 0.534 IMPROVE <--------- Ach1 0.762 ACHIEVE <--------- Ach1 0.415 CONTENT <--------- Ach1 -0.264
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Solution:
• Use a different reference indicator
• (Note: REDUCE can be used as a reference indicator in a 2-factor model, though other reference indicators might be better because REDUCE is factorally complex)
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REDUCE
NEVHAPP e21
NEW_GOAL e31
IMPROVE e41
ACHIEVE e51
CONTENT e61
e11
Content
Achieve
1
1
When to add, when not to add parameters
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Modification Indices
Covariances: M.I.Par Change
e1 <--> Ach1 63.668 0.032e1 <--> Cont1 6.692 0.016e6 <--> Ach1 32.540 -0.023e5 <--> Cont1 4.370 0.012e5 <--> e6 13.033 -0.028e4 <--> e1 28.242 0.036e4 <--> e6 24.104 -0.034e3 <--> e1 4.500 0.012e2 <--> e1 5.440 0.016e2 <--> e6 5.290 -0.016e2 <--> e5 14.681 0.025e2 <--> e3 12.410 -0.017
REDUCE
NEVHAPP e21
NEW_GOAL e31
IMPROVE e41
ACHIEVE e51
CONTENT e61
e11
Content
Achieve
1
1
Discrepancy 125.260 0.000Degrees of freedom 8P 0.000 0.000
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REDUCE
NEVHAPP e21
NEW_GOAL e31
IMPROVE e41
ACHIEVE e51
CONTENT e61
e11
Content
Achieve
1
1
Regression Weights: M.I. Par Change
REDUCE <-- Ach1 52.853 0.406REDUCE <-- ACHIEVE 16.291 0.076REDUCE <-- IMPROVE 50.413 0.140REDUCE <-- NEW_GOAL 23.780 0.117CONTENT <-- Ach1 27.051 -0.293CONTENT <-- ACHIEVE 24.336 -0.094CONTENT <-- IMPROVE 31.694 -0.112ACHIEVE <-- REDUCE 4.791 0.033ACHIEVE <-- NEVHAPP 11.086 0.056IMPROVE <-- REDUCE 18.169 0.058IMPROVE <-- CONTENT 16.219 -0.053NEW_GOAL <-- NEVHAPP 6.137 -0.032NEVHAPP <-- REDUCE 4.031 0.029NEVHAPP <-- ACHIEVE 9.687 0.050NEVHAPP <-- NEW_GOAL 9.452 -0.063
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REDUCE
NEVHAPP
.26
e21
NEW_GOAL
.19
e31
IMPROVE
.17
e41
ACHIEVE
.35
e51
CONTENT
.40
e61
.37
e11
.17
Cont1
.07
Ach1
-.05
.99
1.08
1.00
1.03
1.76
1.00
.66
Choice to add or not to add parameter from Ach1 REDUCE
a matter of theoretical judgement. (Note changes in other parameters)
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Goodness of Fit Measures in Structural Equation Models
A Good Reference: Bollen and Long, TESTING STRUCTURAL EQUATION MODELS, Sage, 1993.
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Goodness of Fit Measures in Structural Equation Models
A fit measure expresses the difference between Σ(θ) and S. Using whatever metric it employs, it should register “perfect” whenever Σ(θ) = S exactly.
This occurs trivially when df=0
0 to 1 usually thought of as best metric (see Tanaka in Bollen & Long, 1993)
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Goodness of Fit Measures in Structural Equation Models
Early fit measures: Model Χ2 :
Asks the question, is there a statistically significant difference between S and Σ ?
If the answer to this question is “no”, we should definitely NOT try to add parameters to the model (capitalizing on change)
If the answer to this question is “yes”, we can cautiously add parameters Contemporary thinking is that we need some other
measure that is not sample-size dependent
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Goodness of Fit Measures in Structural Equation Models
Model Χ2 : X2 = (N-1) * Fml
Contemporary thinking is that we need some other measure that is not sample-size dependent
An issue in fit measures: “sample size dependency” (not considered a good thing)
Chi-square is very much sample size dependent (a direct function of N)
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Goodness of Fit Measures in Structural Equation Models
Model Χ2 : X2 = (N-1) * Fml
Contemporary thinking is that we need some other measure that is not sample-size dependent
An issue in fit measures: “sample size dependency” (not considered a good thing)
Chi-square is very much sample size dependent (a direct function of N)
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Goodness of Fit Measures in Structural Equation Models
Problem with Χ2 itself as a measure (aside from the fact that it is a direct function of N):
Logic of trying to “embrace” the null hypothesis. Even if chi-square not used, it IS important
as a “cut off” (never add parameters to a model when chi-square is non-signif.
Many measures are based on Χ2
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Goodness of Fit Measures in Structural Equation Models
The “first generation” fit measures: Jöreskog and Sörbom’s Goodness of Fit
Index (GFI) [LISREL] Bentler’s Normed Fit Index (NFI) [EQS]
These have now been supplemented in most software packages with a wide variety of fit measures
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Fit Measures
GFI = 1 – tr[Σ-1S – I]2
tr (Σ-1S)2
Takes on value from 0 to 1Conventional wisdom: .90 cutoffGFI tends to yield higher values than other
coefficientsGFI is affected by sample size, since in small
samples, we would expect larger differences between Σ and S even if the model is correct (sampling variation is larger)
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Fit Measures
GFI is an “absolute” fit measure
There are “incremental” fit measures that compare the model against some baseline.
- one such baseline is the “Independence Model
- Independence Model: models only the variances of manifest variables (no covariances) [=assumpt. all MVs independent] “Independence Model chi-square” (usually very large)
- Σ will have 0’s in the off-diagonals
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Fit Measures
NFI = (Χ2b-Χ2
m)/ Χ2b Normed Fit Index (Bentler)
(subscript b = baseline m=model)
Both NFI and GFI will increase as the number of model parameters increases and are affected by N (though not as a simple *N or *N-1 function).
GFI = widely used in earlier literature since it was the only measure (along with AGFI) available in LISREL
NFI (along with NNFI) only measure available in early versions of EQs
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Fit MeasuresThinking about fit indices:
Desirable properties:
1. Normed (esp. to 0 1)Some measures only approx: TLI
Arbitrary metric: AIC (Tanaka: AIC could be normed)
2. Not affected by sample size (GFI, NFI are)
3. “Penalty function” for extra parameters (no inherent advantage to complex models) – “Parsimony” indices deal with this
4. Consistent across estimation techniques (ML, GLS, other methods)
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Fit MeasuresBollens delta-2
(Χ2b – Χ2
m )/ Χ2b – dfm
RMR – root mean residual (only works with standardized residuals)
SRMR - standardized RMRParsimony GFI 2df/p * (p+1) * GFIAGFI = 1 – [1(q+1) / 2df ] [1 – GFI]RNI (Relative Noncentrality Index)
= [(Χ2b – dfb) – (X2
m- dfm)] / (Χ2b – dfb)
CFI = 1 – max[(X2m- dfm),0] / max[(X2
m- dfm), (X2 b- dfb),0]
RMSEA = sqrt (MAX[(X2m- dfm),(n-1),0) / dfm
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Fit Measures
Some debate on conventional .90 criterion for most of these measures
Hu & Bentler, SEM 6(1), 1999 suggest: • Use at least 2 measures• Use criterion of >.95 for 0-1 measure, <.06 for
RMSEA or SRMR
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SEM Assumptions
Fml estimator:
1. No Kurtosis
2. Covariance matrix analysed *
3. Large sample
4. H0: S = Σ(θ) holds exactly
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SEM Assumptions
Fml estimator:
1. Consistent
2. Asymptotically efficient
3. Scale invariant
4. Distribution approximately normal as N increases
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SEM Assumptions
Fml estimator:
Small Samples
1980s simulations:
- Not accurate N<50
- 100 + highly recommended
- “large sample” usually 200+
- in small samples, chi-square tends ot be too large
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Writing up results from Writing up results from Structural Equation ModelsStructural Equation Models
What to Report, What to OmitWhat to Report, What to Omit
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Writing up results from Structural Equation Writing up results from Structural Equation ModelsModels
Reference: Hoyle and Panter Reference: Hoyle and Panter chapter in Hoyle.chapter in Hoyle.
Important to note that there is a Important to note that there is a wide variety of reporting styles (no wide variety of reporting styles (no one “standard”).one “standard”).
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Writing up results from Structural Equation Writing up results from Structural Equation ModelsModels
A DiagramA Diagram Construct Equation ModelConstruct Equation Model Measurement Equation modelMeasurement Equation model
Some simplification may be required.Some simplification may be required.
Adding parameter estimates may clutter Adding parameter estimates may clutter (but for simple models helps with (but for simple models helps with reporting).reporting).
Alternatives exist (present matrices).Alternatives exist (present matrices).
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Reporting Structural Equation ModelsReporting Structural Equation Models
““Written explanation justifying each Written explanation justifying each path and each absence of a path” path and each absence of a path” (Hoyle and Panter)(Hoyle and Panter)
(just how much journal space is (just how much journal space is available here? )available here? )
It might make more sense to try to It might make more sense to try to identify potential controversies identify potential controversies (with respect to inclusion, (with respect to inclusion, exclusion).exclusion).
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Controversial paths?Controversial paths?
LV1
y11
1
y2
1
y3
1
LV21
111
e1
e2
LV320
y4
1
y5
1
y6
1
LV411
1
1
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What to report and what not to report…..What to report and what not to report…..
Present the details of the statistical Present the details of the statistical modelmodel
Clear indication of all free parametersClear indication of all free parameters Clear indication of all fixed parametersClear indication of all fixed parameters It should be possible for the It should be possible for the
reader to reproduce the model reader to reproduce the model
4.4. Describe the dataDescribe the data1.1. Correlations and standard errors (or Correlations and standard errors (or
covariances) for all variables ??covariances) for all variables ??
Round to 3-4 digits and not just 2 if you do Round to 3-4 digits and not just 2 if you do thisthis
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What to report and what not to report…What to report and what not to report…
4. Describing the data (continued)4. Describing the data (continued) Distributions of the dataDistributions of the data
Any variable highly skewed?Any variable highly skewed? Any variable only nominally continuous Any variable only nominally continuous
(i.e., 5-6 discrete values or less)?(i.e., 5-6 discrete values or less)? Report Mardia’s Kurtosis coefficient Report Mardia’s Kurtosis coefficient
(multivariate statistic)(multivariate statistic) Dummy exogenous variables, if anyDummy exogenous variables, if any
5. Estimation Method5. Estimation MethodIf the estimation method is not ML, report If the estimation method is not ML, report
ML results.ML results.
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What to report and what not to report…What to report and what not to report…
6. Treatment of Missing Data6. Treatment of Missing Data How big is the problem?How big is the problem? Treatment method used?Treatment method used?
Pretend there are no missing dataPretend there are no missing data Listwise deletionListwise deletion Pairwise deletionPairwise deletion FIML estimation (AMOS, LISREL >=8.5)FIML estimation (AMOS, LISREL >=8.5) Nearest neighbor imputation (LISREL Nearest neighbor imputation (LISREL
>=8.1)>=8.1) EM algorithm (covariance matrix EM algorithm (covariance matrix
imputation ) (LISREL >=8.5)imputation ) (LISREL >=8.5)
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What to report and what not to report…What to report and what not to report…
7. Fit criterion7. Fit criterion Hoyle and Panter suggest “.90; justify Hoyle and Panter suggest “.90; justify
if lower”.if lower”. Choice of indices also an issue.Choice of indices also an issue.
There appears to be “little consensus There appears to be “little consensus on the best index” (H & P recommend on the best index” (H & P recommend using multiple indices in presentations)using multiple indices in presentations)
Standards:Standards:
Bollen’s delta 2 (IFI)Bollen’s delta 2 (IFI)
Comparative Fit IndexComparative Fit Index
RMSEARMSEA
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Fit indicesFit indices
Older measures:Older measures: GFI (Joreskog & Sorbom)GFI (Joreskog & Sorbom) Bentler’s Normed Fit indexBentler’s Normed Fit index Model Chi-SquareModel Chi-Square
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What to report & what not to report….What to report & what not to report….
8. Alternative Models used for Nested Comparisons 8. Alternative Models used for Nested Comparisons (if (if appropriate)appropriate)
11
1
1
US West
U.S. Midwest
U.S. Rust Belt
US South
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9. Plausible explanation for correlated errors9. Plausible explanation for correlated errors[“these things were just too darned big to ignore”][“these things were just too darned big to ignore”]
Generally assumed when working with panel model with Generally assumed when working with panel model with equivalent indicators across time:equivalent indicators across time:
1
1 1 1
1
1 1 1
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What to reportWhat to report
10. Interpretation of regression-based 10. Interpretation of regression-based modelmodel
Present standardized and Present standardized and unstandardized coefficients (usually)unstandardized coefficients (usually)
Standard errors? (* significance test Standard errors? (* significance test indicators?)indicators?)
R-square for equationsR-square for equations Measurement model too?Measurement model too? (expect higher R-squares) (expect higher R-squares)
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What to report.What to report.
Problems and issuesProblems and issues Negative error variances or other Negative error variances or other
reasons for non-singular parameter reasons for non-singular parameter covariance matricescovariance matrices
How dealt with? Does the final model How dealt with? Does the final model entail any “improper estimates”?entail any “improper estimates”?
Convergence difficulties, if anyConvergence difficulties, if any LISREL: can look at FLISREL: can look at Fmlml across values of across values of
given parameter, holding other parameters given parameter, holding other parameters constantconstant
Collinearity among exogenous Collinearity among exogenous variablesvariables
Factorially complex itemsFactorially complex items
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What to report & what not to report….What to report & what not to report….
General Model Limitations, Future General Model Limitations, Future Research issues:Research issues:
Where the number of available Where the number of available indicators compromised the modelindicators compromised the model
2-indicator variables? (any constraints 2-indicator variables? (any constraints required?)required?)
Single-indicator variables? (what Single-indicator variables? (what assumptions made about error variances?)assumptions made about error variances?)
Indicators not broadly representative of the Indicators not broadly representative of the construct being measured?construct being measured?
Where the distribution of data Where the distribution of data presented problems presented problems
Larger sample sizes can helpLarger sample sizes can help
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What to report & what not to report….What to report & what not to report….
General Model Limitations, Future General Model Limitations, Future Research issues:Research issues:
Missing data (extent of, etc.)Missing data (extent of, etc.) Cause-effect issues, if any (what Cause-effect issues, if any (what
constraints went into non-recursive constraints went into non-recursive model? How reasonable are these?)model? How reasonable are these?)
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Matrix form: LISREL MEASUREMENT MODEL MATRICES
Manifest variables: X’s
Measurement errors: DELTA ( δ)
Coefficients in measurement equations: LAMBDA ( λ )
Sample equation:
X1 = λ1 ξ1+ δ1
MATRICES:
LAMBDA-x THETA-DELTA PHI
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Matrix form: LISREL MEASUREMENT MODEL MATRICES
A slightly more complex example:
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Matrix form: LISREL MEASUREMENT MODEL MATRICES
Labeling shown here applies ONLY if this matrix is specified as “diagonal”
Otherwise, the elements would be: Theta-delta 1, 2, 5, 9, 15.
OR, using double-subscript notation:
Theta-delta 1,1
Theta-delta 2,2
Theta-delta 3,3
Etc.
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Matrix form: LISREL MEASUREMENT MODEL MATRICES
While this numbering is common in some journal articles, the LISREL program itself does not use it. Two subscript notations possible:
Single subscript Double subscript
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Matrix form: LISREL MEASUREMENT MODEL MATRICES
Models with correlated measurement errors:
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Matrix form: LISREL MEASUREMENT MODEL MATRICES
Measurement models for endogenous latent variables (ETA) are similar:
Manifest variables are Ys
Measurement error terms: EPSILON ( ε )
Coefficients in measurement equations: LAMBDA (λ)
• same as KSI/X side
•to differentiate, will sometimes refer to LAMBDAs as Lambda-Y (vs. Lambda-X)
Equations
Y1 = λ1 η 1+ ε1
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Matrix form: LISREL MEASUREMENT MODEL MATRICES
Measurement models for endogenous latent variables (ETA) are similar:
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LISREL MATRIX FORM
An Example:
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LISREL MATRIX FORM
An Example:
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LISREL MATRIX FORM
An Example:
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LISREL MATRIX FORM
An Example:
+ theta-epsilon, 8 x 8 matrix with parameters in diagonal and 0s in off diagonals (a “diagonal” matrix)
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Class Exercise
1
1
1
1
1
#1
Provide labels for each of the variables
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#2
1
1
1
1
1
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#1
delta
epsilon
ksieta
zeta
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#2
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Lisrel Matrices for examples.
No Beta Matrix in this model
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Lisrel Matrices for examples.
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Lisrel Matrices for examples (example #2)
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Lisrel Matrices for examples (example #2)