comparative value priorities of chinese and new zealand

Figure 5.4. SVS RMSEA for the NZ SVS57 Scores
Model for SVS57, NZ Raw Scores RMSEA LO 90 HI 90
Default model .062 .058 .067
Independence model .115 .111 .119
Structural Equations Models for the LBDQXII
SEM analyses were run for the two samples for responses on the LBDQXII using raw
scores and Centred scores.
� GZ Raw 10715.5, df = 4784
� NZ Raw Chi-square = 8581.642, Degrees of freedom = 4784
As seen in Figures 5.5 through 5.8, SEM analysis indicates a good fit for the LBDQXII
in both the Guangzhou and New Zealand Samples.
The SEM analyses indicate a marginally good (SVS57 in Guangzhou), to good fit
between the SVS57 and LBDQXII operationalisations of the latent value and preferred
leader behaviour dimensions.
Figure 5.5. Parsimony-Adjusted Measures for the GZ LBDQXII Scores
Model GZ LBDQXII Raw Scores PRATIO PNFI PCFI
Default model .947 .372 .502
Saturated model .000 .000 .000
Independence model 1.000 .000 .000
Figure 5.6. RMSEA for the GZ LBDQXII Scores
Model GZ LBDQXII Raw Scores RMSEA LO 90 HI 90
Default model .071 .069 .073
Independence model .101 .099 .102
Figure 5.7. Parsimony-Adjusted Measures for the NZ LBDQXII Scores
Model NZ LBDQXII Raw Scores PRATIO PNFI PCFI
Default model .947 .296 .463
Saturated model .000 .000 .000
Independence model .947 .296 .463
Figure 5.8. RMSEA for the NZ LBDQXII Scores
Model NZ LBDQXII Raw Scores RMSEA LO 90 HI 90
Default model .060 .058 .062
Independence model .082 .080 .083
To further investigate fit of my data to the models I will now analyse the
Multidimensional Scaling Smallest Space Analysis (SSA) results from the data.
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