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
R2 • The last four are estimated reliabilities.
Standardized Residual Covariances • Looks like we need to allow senseek to covary with dayski and 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.
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.
Fit • CFI = 1.000 • RMSEA = 0.000