TABLE 3.
Nested model comparisons. N = 294.
| Model | Free parameters | CFI | TLI | RMSEA [90% CI] | SRMR | χ2 | df | p | Δχ2 | Δdf | Δp |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Inhibitory control (Go/No-Go task) | |||||||||||
| CLPM | 44 | 0.91 | 0.77 | 0.12 [0.10, 0.15] | 0.06 | 84.04 | 16 | <0.01 | – | – | – |
| vs. RI-CLPM | 47 | 0.99 | 0.96 | 0.05 [0.00, 0.08] | 0.05 | 21.08 | 13 | 0.07 | 27.89 | 3 | <0.01 |
| RI-CLPM | |||||||||||
| vs. RI-CLPM with autoregressive equality constraints | 43 | 0.96 | 0.90 | 0.08 [0.05, 0.10] | 0.07 | 47.59 | 17 | <0.01 | 24.96 | 4 | <0.01 |
| vs. RI-CLPM with cross-lagged equality constraints† | 43 | 0.99 | 0.98 | 0.04 [0.00, 0.07] | 0.05 | 23.26 | 17 | 0.14 | 3.39 | 4 | 0.50 |
| Working memory (Nebraska Barnyard task) | |||||||||||
| CLPM | 44 | 0.88 | 0.70 | 0.15 [0.13, 0.18] | 0.09 | 126.12 | 16 | <0.01 | — | — | — |
| vs. RI-CLPM | 47 | 0.98 | 0.93 | 0.07 [0.04, 0.11] | 0.06 | 39.67 | 13 | <0.01 | 48.70 | 3 | <0.01 |
| RI-CLPM | |||||||||||
| vs. RI-CLPM with lag-2 autoregressive paths† | 51 | 0.98 | 0.90 | 0.08 [0.05, 0.12] | 0.05 | 27.86 | 9 | <0.01 | 11.01 | 4 | 0.03 |
| RI-CLPM with lag-2 autoregressive paths | |||||||||||
| vs. RI-CLPM with lag-2 autoregressive paths and autoregressive equality constraints | 43 | 0.97 | 0.92 | 0.08 [0.05, 0.10] | 0.06 | 46.62 | 17 | <0.01 | 19.38 | 8 | <0.01 |
| vs. RI-CLPM with lag-2 autoregressive paths and cross-lagged equality constraints | 47 | 0.98 | 0.90 | 0.08 [0.05, 0.11] | 0.06 | 38.09 | 13 | <0.01 | 10.34 | 4 | 0.04 |
| Flexible shifting (Shape School task) | |||||||||||
| CLPM | 44 | 0.92 | 0.80 | 0.11 [0.09, 0.14] | 0.06 | 77.44 | 16 | <0.01 | — | — | — |
| vs. RI-CLPM† | 47 | 0.99 | 0.98 | 0.03 [0.00, 0.07] | 0.04 | 17.28 | 13 | 0.19 | 16.13 | 3 | <0.01 |
| RI-CLPM | |||||||||||
| vs. RI-CLPM with autoregressive equality constraints | 43 | 0.99 | 0.97 | 0.04 [0.00, 0.07] | 0.06 | 29.38 | 17 | 0.03 | 10.42 | 4 | 0.03 |
| vs. RI-CLPM with cross-lagged equality constraints | 43 | 0.93 | 0.90 | 0.07 [0.04, 0.11] | 0.07 | 32.09 | 17 | 0.01 | 14.51 | 4 | <0.01 |
Indicates the best-fitting, most parsimonious model. Lag-2 autoregressive paths describe non-consecutive autoregressive paths (i.e., from grades 1 to 3 and grades 2 to 4). Fit statistics include the χ2 statistic (recommended to be nonsignificant); the comparative fit index (CFI) and Tucker-Lewis Index (TLI; recommended to be 0.90 or higher)39; and the root mean squared error of approximation (RMSEA) and the standard root mean residual (SRMR, recommended to be 0.05 or lower).40 χ2 difference tests (Δχ2) were conducted using the Satorra–Bentler method.
Abbreviations: CI, confidence interval; CLPM, cross-lagged panel model; df, degrees of freedom; RI-CLPM, random intercept cross-lagged panel model.