comparative value priorities of chinese and new zealand

Structural Equations Model Goodness of Fit Tests for the SVS
The AMOS SEM tests results below indicate a marginally acceptable to good fit of the
dimensions for the Guangzhou sample data to the SVS57 individual values model. The
fit of the New Zealand sample data is good by the accepted standards.
Guangzhou SVS Sample SEM Test Results
The SEM analysis for raw scores found Chi Square = 2615.8, df = 945, and, good
parsimony-adjusted measures; however the RMSEA is marginally good at about 0.085.
The Guangzhou sample data is a marginally good fit to the SVS57 model, indicating a
need for future research with larger sample sizes, and experimentation with rephrasing
and retranslating survey items. See the results in Figures 5.1 and 5.2.
Figure 5.1. SVS Parsimony-Adjusted Measures for the GZ Sample Using Raw
Scores
Model for SVS57, GZ Raw Scores PRATIO PNFI PCFI
Default model .874 .554 .633
Saturated model .000 .000 .000
Independence model 1.000 .000 .000
Figure 5.2. SVS RMSEA for the GZ Sample Using Raw Scores
Model for SVS57, GZ Raw Scores RMSEA LO 90 HI 90
Default model .085 .081 .088
Independence model .151 .147 .154
New Zealand SVS Sample SEM Test Results
The SEM analysis for NZ raw scores found Chi Square = 1751.2, df = 945, and good
parsimony-adjusted measures and RMSEA. The SVS sample data are good fits to the
model, according to SEM. See Figures 5.3 and 5.4.
Figure 5.3. SVS Parsimony-Adjusted Measures for the NZ SVS57 Scores
Model for SVS57, NZ Raw Scores PRATIO PNFI PCFI
Default model .874 .513 .651
Saturated model .000 .000 .000
Independence model 1.000 .000 .000
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