SEM Analysis SPSS/AMOS

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