SEM Analysis SPSS/AMOS

Ski Satisfaction Download, from BlackBoard, these files Boot up AMOS 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 Estimate SkiSat <--- senseek .399 LoveSki .411 foodsat .601 numyrs .975 dayski .275 snowsat .760

R2 The last four are estimated reliabilities. Estimate SkiSat .328   Estimate SkiSat .328 dayski .076 foodsat .362 snowsat .578 numyrs .950

Standardized Residual Covariances Looks like we need to allow senseek to covary with dayski and numyrs.   senseek dayski foodsat snowsat numyrs .000 2.252 .606 .754 .193 .660 .567 .313 .308 2.337 .488 .707

Standardized Total Effects   LoveSki senseek SkiSat .411 .399 .000 dayski .275 foodsat .247 .240 .601 snowsat .312 .303 .760 numyrs .975

Standardized Direct Effects   LoveSki senseek SkiSat .411 .399 .000 dayski .275 foodsat .601 snowsat .760 numyrs .975

Standardized Indirect Effects   LoveSki senseek SkiSat .000 dayski foodsat .247 .240 snowsat .312 .303 numyrs

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

2 has dropped 6.761 points on one degree of freedom. 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 numyrs .000 .891 .024 -.075 -.013 -.440 -.255 -.005 .138

Fit CFI = 1.000 RMSEA = 0.000 