Table 2.
CFA results.
| Society | N | RMSEA | CFI | TLI | Items with non-significant loadingsa |
Empirically under-identified itemsa |
Median Factor Loadings (Range) |
Median Factor Correlations (Range) |
|---|---|---|---|---|---|---|---|---|
| Albania | 750 | .029 | .939 | .938 | 76 | .73 (.01–.95) | .78(.48–.96) | |
| Algeria | 300 | .026 | .795 | .789 | 11, 19, 40, 52 | .53(.11–.85) | .61(.15–.94) | |
| Argentina | 679 | .027 | .861 | .857 | .65(.28–.96) | .55(.20–.79) | ||
| Brazil | 679 | .026 | .909 | .906 | .69(.34–.91) | .65(.16–.81) | ||
| China | 515 | .016 | .926 | .924 | 18, 82 | .67(.07–.88) | .74(.52–.93) | |
| Flanders | 737 | .024 | .899 | .896 | .68(.16–.90) | .59(.28–.82) | ||
| France | 395 | .026 | .876 | .872 | 56d, 94 | .68(.11–.93) | .58(.18–.77) | |
| Hong Kong | 330 | .022 | .917 | .914 | .72(.39–.99) | .74(.48–.90) | ||
| Iceland | 299 | .016 | .955 | .954 | 57 | 56c | .75(.35–1.001) | .70(.31–.88) |
| Japan | 1000 | .023 | .927 | .925 | 104 | .79(.50–1.04) | .75(.57–.88) | |
| Korea | 299 | .022 | .937 | .936 | .74(.39–.91) | .67(.30–.88) | ||
| Lithuania | 573 | .029 | .903 | .900 | 6, 17 | .67(.11–.95) | .70(.37–.85) | |
| Poland | 282 | .026 | .906 | .903 | .73(.28–.91) | .67(.17–.86) | ||
| Portugal | 397 | .027 | .775 | .768 | 7, 91, 92 | .61(.01–.93) | .64(.11–.78) | |
| Russia | 436 | .028 | .871 | .868 | .66(.31–.95) | .64(.13–.82) | ||
| Serbia | 312 | .021 | .932 | .930 | 91 | .73(.32–1.06) | .70(.36–.87) | |
| Taiwan | 300 | .019 | .893 | .890 | 70, 92 | .70(.14–.99) | .60(.29–.84) | |
| United Kingdom | 299 | .021 | .912 | .909 | 40 | .74(.26–1.07) | .61(.15–.77) |
Note. RMSEA = Root Mean Square Error of Approximation, CFI = Comparative Fit Index, TLI = Tucker-Lewis Index.
a
The number is the item’s number on the ABCL.