Introduction to Lisrel 8.5

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Introduction to Lisrel 8.5. Presented in DHPR, NHRI 2004.6. Overview – Lisrel 8.5. Lisrel 8.5 Lisrel Lisrel: for advanced user Simplis: for beginner Only need to assign variable association Prelis. Options to run Lisrel. Syntax only Simplis, Lisrel

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Introduction to Lisrel 8.5

Presented in DHPR, NHRI

2004.6

Overview – Lisrel 8.5
• Lisrel 8.5
• Lisrel
• Simplis: for beginner
• Only need to assign variable association
• Prelis
Options to run Lisrel
• Syntax only
• Simplis, Lisrel

* Notice:Function will not be activated until the original file was saved as a new one.

• Simplis project
• Lisrel project
• Path Diagram
• Path analysis for latent variables
• Recursive models for longitudinal studies, 1967 and 1971
• Notes:
• The error terms of ANOMIAand POWERL are specified to be correlated over time.
• The four one-way arrows to the right of the diagram represent the measurement errors in ANOMIA67, POWERL67, ANOMIA71 and POWERL71 respectively.
• The two-way arrows on the right indicate that some of the measurement errors are correlated. The covariance between the two error terms for each variable can be interpreted as a specific error variance. For other models for the same data.

Observed Variables

ANOMIA67 POWERL67 ANOMIA71 POWERL71 EDUC SEI

Covariance Matrix from file 'C:\Data\SP\SP.COV'

Sample Size = 932

Latent Variables ALIEN67 ALIEN71 SES

Relationships

ANOMIA67 POWERL67 = ALIEN67

ANOMIA71 POWERL71 = ALIEN71

EDUC SEI = SES

ALIEN67 = SES

ALIEN71 = ALIEN67 SES

Set Error Covariance of ANOMIA67 and ANOMIA71 Free

Set Error Covariance of POWERL67 and POWERL71 Free

Path Diagram

Number of Decimals = 4

Iterations = 250

Method of Estimation: Maximum Likelihood

End of Problem

• second-order factor analysis model.
• The following equation
• is in the form of a factor analysis model for with first order factors and measurement errors Now suppose that the variables in turn can be accounted for by a set of factors so called second-order factors, so that
• where is a matrix of second-order factor loadings and is a vector of unique components for
• To illustrate the model, we use data on some cognitive ability tests. The standard deviations and correlations of two forms of each of five tests are given in the table below. The sample size (N) is 267.
• The model specification is:
• Here * denotes parameters to be estimated and 0 and 1 are fixed parameters.

The number of Y-variables = 10

Second order factor analysis

DA NI=10 NO=267 NG=1 MA=CM

LA 'GESCOM A' 'GESCOM B' 'CONWOR A' 'CONWOR B' 'HIDPAT A' 'HIDPAT B' THIROUND THIBLUE 'VOCABU A' 'VOCABU B'

CM FI='C:\Lisrel Data\LS\sofa3' SY

SE

1 2 3 4 5 6 7 8 9 10 /

MO NY=10 NK=2 NE=5 LY=FU,FI BE=FU,FI GA=FU,FI PH=ST PS=DI,FR TE=DI,FR

LE

Gescom Conwor Hidpat Things Vovabu

LK

Speedclo Vocabul

VA 1.00 LY(1,1) LY(2,1) LY(3,2) LY(4,2) LY(5,3) LY(6,3) LY(7,4) LY(9,5) LY(10,5)

FR LY(8,4) GA(1,1) GA(2,1) GA(3,1) GA(4,2) GA(5,2)

PD

OU ME=ML PC RS SS XM IT=250 LY=.lys GA=.gas TV=.tvs

Example 3 : Directly Create Syntax and Path Diagram by drawing Path Diagram -1
• Six Psychological Variables-A Confirmatory Factor Analysis
Example 3 : Directly Create Syntax and Path Diagram by drawing Path Diagram - 2

Six Psychological Variables- A Confirmatory Factor Analysis

SYSTEM FILE from file 'C:\Lisrel Data\PD\EX5.DSF'

Sample Size = 145

Latent Variables VISUAL VERBAL

Relationships

VISPERC = VISUAL

CUBES = VISUAL

LOZENGES = VISUAL

'PAR COM' = VISUAL

'SEN COM' = VERBAL

WORDMEAN = VERBAL

Set the Variance of VISUAL to 1.00

Set the Covariances of VERBAL and VISUAL to 0.53

Set the Variance of VERBAL to 1.00

Path Diagram

Iterations = 250

Method of Estimation: Maximum Likelihood

End of Problem

How to use Prelis - 1
• Run Syntax
• EXAMPLE : ATTITUDES OF MORALITY AND EQUALITY

DA NI=8 NO=200 MI=0 TR=PA

LA HUMRGHTS EQUALCON RACEPROB EQUALVAL EUTHANAS

CRIMEPUN CONSCOBJ GUILT

RA FI=DATA.EX2

OU MA=PM

• Open *.PSF
• Most of the statistical procedures can be performed when a PRELIS data file (*.psf) is opened.
How to use Prelis - 2
• Importing data into Prelis
• From *.sas7bdat or * .txt
• Assigning labels to variable
• Define missing values
• Save and output covariance or correlation matrixes