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
• File, Data Files, File Name. Select SkiSatVarCov.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
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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)
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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
SkiSat
SkiSat
foodsat
numyrs
dayski
snowsat
<--<--<--<--<--<---
senseek
LoveSki
SkiSat
LoveSki
LoveSki
SkiSat
Estimate
.399
.411
.601
.975
.275
.760
R2
SkiSat
dayski
foodsat
snowsat
numyrs
Estimate
.328
.076
.362
.578
.950
• The last four are estimated reliabilities.
Standardized Residual
Covariances
senseek
senseek
.000
dayski
2.252
foodsat
.606
snowsat
.660
numyrs
2.337
dayski
.000
.754
.567
.000
foodsat snowsat
.193
.313
.488
.308
.707
numyrs
.000
• Looks like we need to allow senseek to
covary with dayski and numyrs.
Standardized Total Effects
SkiSat
dayski
foodsat
snowsat
numyrs
LoveSki
.411
.275
.247
.312
.975
senseek
.399
.000
.240
.303
.000
SkiSat
.000
.000
.601
.760
.000
Standardized Direct Effects
SkiSat
dayski
foodsat
snowsat
numyrs
LoveSki
.411
.275
.000
.000
.975
senseek
.399
.000
.000
.000
.000
SkiSat
.000
.000
.601
.760
.000
Standardized Indirect Effects
SkiSat
dayski
foodsat
snowsat
numyrs
LoveSki
.000
.000
.247
.312
.000
senseek
.000
.000
.240
.303
.000
SkiSat
.000
.000
.000
.000
.000
Modification Indices:
Covariances
senseek <-->
LoveSki
Par
M.I.
Change
5.574 1.258
• 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.
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
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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
senseek
dayski
foodsat
snowsat
numyrs
senseek
.000
.891
.024
-.013
-.255
dayski
.000
-.075
-.440
.000
foodsat snowsat
.000
.000
-.005
.000
.138
• No large standardized residuals. 
numyrs
.000
Fit
• CFI = 1.000
• RMSEA = 0.000
