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SEM Analysis SPSS/AMOS. Ski Satisfaction. Download, from BlackBoard , these files SkiSat-VarCov.txt SkiSat.amw SEM-Ski-Amos-TextOutput.docx Boot up AMOS File, Open, SkiSat.amw See my document for how to draw the path diagram. Identify Data File. - PowerPoint PPT Presentation
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SEM AnalysisSPSS/AMOS
Ski Satisfaction• Download, from BlackBoard, these files
– SkiSat-VarCov.txt– SkiSat.amw– SEM-Ski-Amos-TextOutput.docx
• Boot up AMOS• File, Open, SkiSat.amw• See my document for how to draw the
path diagram.
Identify Data File• File, Data Files, File Name. Select SkiSat-
VarCov.txt. Open.
View Data File• View Data.
Love-Ski Properties• Right-Click on Love-Ski• Select Object Properties• Notice that I have fixed the variance to 1.
Path Properties• Right-click on the arrow leading from
SkiSat to snowsat. Select Properties.• Notice that I have fixed the coefficient to 1.
Set Analysis Properties• Minimization History• Standardized Estimates• Squared Multiple Correlations• Residual Moments• Modification Indices• Indirect, Direct, and Total Effects
Calculate Estimates
• Proceed With The Analysis
View Text (Output)• Result (Default model)• Minimum was achieved• Chi-square = 8.814• Degrees of freedom = 4• Probability level = .066 No significant,
but uncomfortably close• Null is that the model fits the data perfectly
Standardized Weights
EstimateSkiSat <--- senseek .399SkiSat <--- LoveSki .411foodsat <--- SkiSat .601numyrs <--- LoveSki .975dayski <--- LoveSki .275snowsat <--- SkiSat .760
R2
• The last four are estimated reliabilities.
EstimateSkiSat .328dayski .076foodsat .362snowsat .578numyrs .950
Standardized Residual Covariances
• Looks like we need to allow senseek to covary with dayski and numyrs.
senseek dayski foodsat snowsat numyrssenseek .000 dayski 2.252 .000 foodsat .606 .754 .193 snowsat .660 .567 .313 .308 numyrs 2.337 .000 .488 .707 .000
Standardized Total Effects
LoveSki senseek SkiSatSkiSat .411 .399 .000dayski .275 .000 .000foodsat .247 .240 .601snowsat .312 .303 .760numyrs .975 .000 .000
Standardized Direct Effects
LoveSki senseek SkiSatSkiSat .411 .399 .000dayski .275 .000 .000foodsat .000 .000 .601snowsat .000 .000 .760numyrs .975 .000 .000
Standardized Indirect Effects
LoveSki senseek SkiSatSkiSat .000 .000 .000dayski .000 .000 .000foodsat .247 .240 .000snowsat .312 .303 .000numyrs .000 .000 .000
Modification Indices: Covariances
• This is the Lagrange Modifier Test. It is a significant Chi-Square on one degree of freedom. The fit of the model would be improved by allowing senseek and LoveSki to covary.
M.I. Par Change
senseek <--> LoveSki 5.574 1.258
Fit• Comparative Fit Index = .919. • CFI is said to be good with small samples.
Fit is good if > .95.• Root Mean Square Error of Approximation
= .110• < .06 indicates good fit, > .10 indicates
poor fit
Modified Model• Added a path from SenSeek to LoveSki
– LoveSki is now a latent dependent variable• Fixed the regression coefficient from
LoveSki to NumYrs at 1, giving LoveSki the same variance as NumYrs.– I had noticed earlier that LoveSki and NumYrs
were very well correlated.• Added a disturbance for LoveSki, as it is
now a latent dependent variable
• Minimum was achieved• 2(3) = 2.053• Previously 2(4) = 8.814• 2 has dropped 6.761 points on one
degree of freedom.• Probability level = .562
– Null is that the model fits the data perfectly
Standardized Residual Covariances
• No large standardized residuals.
senseek dayski foodsat snowsat numyrssenseek .000 dayski .891 .000 foodsat .024 -.075 .000 snowsat -.013 -.440 .000 .000 numyrs -.255 .000 -.005 .138 .000
Fit• CFI = 1.000• RMSEA = 0.000