Table 4.
Comparison of factor analysis models applied to the SMAQ.
| Model | χ2 (df) | P-value | RMSEA (90% CI) | BIC | CFI | SRMR | ΔBIC | ΔCFI |
|---|---|---|---|---|---|---|---|---|
| Sex* | 0.000 | |||||||
| Configural | 613.69 (18) | 0.000 | 0.109 (0.101, 0.116) | 98433 | 0.942 | 0.036 | ||
| Metric | 627.65 (23) | 0.000 | 0.097 (0.090, 0.103) | 98404 | 0.942 | 0.039 | 29 | 0.000 |
| Scalar | 660.90 (28) | 0.000 | 0.096 (0.084, 0.096) | 98394 | 0.939 | 0.040 | 10 | 0.003 |
| Race** | ||||||||
| Configural | 676.37 (54) | 0.000 | 0.112 (0.104, 0.119) | 96524 | 0.939 | 0.039 | ||
| Metric | 760.82 (79) | 0.000 | 0.097 (0.091, 0.103) | 96393 | 0.933 | 0.048 | 131 | 0.006 |
| Scalar | 832.95 (104) | 0.000 | 0.087 (0.082, 0.093) | 96250 | 0.928 | 0.050 | 143 | 0.005 |
| Income*** | ||||||||
| Configural | 588.57 (27) | 0.000 | 0.111 (0.103, 0.118) | 88460 | 0.940 | 0.038 | ||
| Metric | 607.81 (37) | 0.000 | 0.095 (0.089, 0.102) | 88394 | 0.939 | 0.041 | 66 | 0.001 |
| Scalar | 662.75 (47( | 0.000 | 0.088 (0.082, 0.094) | 88363 | 0.934 | 0.043 | 31 | 0.005 |
*Sex (female, male)
**Race (white, black, AIAN, Asian, mixed, other)
***Income (<$50,000, $50,000-$100,000, >$100,000