Sample Size N = 613 (473, 44% had missing data and were listwise deleted) Note: I tried a sample N = 995 with estimating missing data via maximum liklihood however, this led to worse entropy by aprox 10%, but similar models.
Model Fit Summary
| aBIC | LRT.p | LMR.p | BLRT.p | Entropy | |
|---|---|---|---|---|---|
| LCA_2 | 7086.49 | 0 | 0 | 0 | 0.66 |
| LCA_3 | 6911.565 | 0 | 1e-04 | 0 | 0.773 |
| LCA_4 | 6855.47 | 0.0302 | 0.0331 | 0 | 0.761 |
| LCA_5 | 6828.879 | 0.2667 | 0.2768 | 0 | 0.742 |
- LMR.p = p value for VUONG-LO-MENDELL-RUBIN LIKELIHOOD RATIO TEST
- LRT.p = p value for LO-MENDELL-RUBIN ADJUSTED LRT TEST
- BLRT.p = p value for BOOTSTRAPPED LIKELIHOOD RATIO TEST
- aBIC = Sample-Size Adjusted BIC
Class Counts
| class | count | proportion |
|---|---|---|
| LCA 2 | ||
| 1 | 338 | 0.55139 |
| 2 | 275 | 0.44861 |
| LCA 3 | ||
| 1 | 316 | 0.51550 |
| 2 | 265 | 0.43230 |
| 3 | 32 | 0.05220 |
| LCA 4 | ||
| 1 | 30 | 0.04894 |
| 2 | 216 | 0.35237 |
| 3 | 281 | 0.45840 |
| 4 | 86 | 0.14029 |
| LCA 5 | ||
| 1 | 192 | 0.31321 |
| 2 | 29 | 0.04731 |
| 3 | 277 | 0.45188 |
| 4 | 57 | 0.09299 |
| 5 | 58 | 0.09462 |
Plots of estimates of Activity Engagement and Pain Willingness per class membership
- AE = Activity Engagement
- PW = Pain Willingness
- VIs = Values Inventory Success Score (higher score = more success)
- VIi = Values Inventory Importance Score (higher score = more importance)
