Table 6. Maximum Likelihood Estimates of the Final Model.
After the model-generating procedure SEM, the estimates for the final model were calculated as seen in Figure 4.
| Path | Estimate | Std. Estimate | S.E. | C.R. | P |
|---|---|---|---|---|---|
| LMI ← Intensity | 36.228 | 0.562 | 19.921 | 1.819 | 0.069 |
| LTHvl ← LMI | -0.419 | -0.192 | 0.234 | -1.792 | 0.073 |
| LTHvl ← SMA | 1.336 | 0.416 | 0.333 | 4.013 | *** |
| LTHvl ← RCer | 1.079 | 0.536 | 0.210 | 5.151 | *** |
| RTHvl ← LTHvl | 0.766 | 0.921 | 0.044 | 17.564 | *** |
| RSII ← LMI | 0.550 | 0.500 | 0.108 | 5.112 | *** |
| RSII ← LTHvl | 0.177 | 0.351 | 0.056 | 3.167 | 0.002 |
| RSII ← RCer | 0.439 | 0.432 | 0.115 | 3.831 | *** |
| LPPC ← RCer | 0.426 | 0.481 | 0.114 | 3.727 | *** |
| LPPC ← LTHvl | -0.302 | -0.687 | 0.057 | -5.340 | *** |
| LPMv ← RTHvl | 0.355 | 1.000 | 0.084 | 4.227 | *** |
| LPMv ← LTHvl | -0.215 | -0.738 | 0.075 | -2.870 | 0.004 |
| LPMv ← RCer | 0.289 | 0.493 | 0.065 | 4.439 | *** |
| LTHvpl ← RSII | 0.369 | 0.296 | 0.114 | 3.234 | 0.001 |
| LSII ← RSII | 1.289 | 0.624 | 0.185 | 6.975 | *** |
| LSII ← LMI | -0.505 | -0.222 | 0.197 | -2.567 | 0.010 |
| LSII ← LPMv | 0.787 | 0.220 | 0.296 | 2.662 | 0.008 |
| LTHvpl ← LPPC | 0.378 | 0.265 | 0.117 | 3.236 | 0.001 |
| LTHvpl ← RCer | 0.312 | 0.246 | 0.131 | 2.373 | 0.018 |
| LTHvpl ← LPMv | 0.925 | 0.429 | 0.208 | 4.450 | *** |
| LSII ← SMA | -0.686 | -0.205 | 0.264 | -2.594 | 0.009 |
| LSII ← LTHvl | 0.221 | 0.212 | 0.105 | 2.097 | 0.036 |
| Cing ← SMA | 0.379 | 0.429 | 0.143 | 2.659 | 0.008 |
| LMI ← Cing | -1.578 | -0.946 | 1.340 | -1.178 | 0.239 |
| Cing ← LMI | 0.460 | 0.767 | 0.144 | 3.186 | 0.001 |
| SMA ← LMI | 0.319 | 0.469 | 0.125 | 2.550 | 0.011 |
| RCer ← LMI | 0.010 | 0.009 | 0.247 | 0.042 | 0.967 |
| Cing ← RCer | 0.322 | 0.581 | 0.103 | 3.113 | 0.002 |
Estimate = estimate of the regression weight (e.g., when Intensity goes up by 1, LMIhand goes up by 36.228); Std. Estimate = estimate of the standardized regression weight (e.g., when Intensity goes up by 1 standard deviation, LMIhand goes up by 0.562 standard deviations); S.E. = standard error of the regression weight; C.R. = critical ratio for regression weight, which is computed by dividing the regression weight estimate by the estimate of its standard error (e.g., for the path from Intensity to LMIhand, the regression weight estimate is 1.819 standard errors above zero); P = level of significance for regression weight
P < 0.001 (Arbuckle, 2006b).