Results from the predictive models tested in this study.

Note. χ² = WLSMV chi square; df = degrees of freedom; RMSEA = Root mean square error of approximation; CI = 90% Confidence Interval for the RMSEA; CFI = Comparative fit index; TLI = Tucker-Lewis index; Δ since previous model; MDΔχ2: chi square difference test based on the Mplus DIFFTEST function for WLSMV estimation. With WLSMV estimation, the χ2 values are not exact, but "estimated" as the closest integer necessary to obtain a correct p-value. This explains why sometimes the χ2 and resulting CFI values can be non-monotonic with model complexity. Given that the MDΔχ2 tends to be oversensitive to sample size and to minor model misspecifications, as the chi-square itself, and to take into account the overall number of MDΔχ2 tests used in this study, the significance level for these tests was set at. 01 [52,53,54].* p Results from the predictive models tested in this study.

注：χ²：WLSMV卡方（WLSMV chi square）；df：自由度（degrees of freedom）；RMSEA：近似误差均方根（Root mean square error of approximation）；CI：RMSEA的90%置信区间（90% Confidence Interval）；CFI：比较拟合指数（Comparative fit index）；TLI：塔克-刘易斯指数（Tucker-Lewis index）；Δ：相较于前一模型的变化量；MDΔχ²：基于Mplus的DIFFTEST函数、针对WLSMV估计的卡方差异检验。采用WLSMV估计方法时，χ²值并非精确数值，而是为获取准确p值而取最接近整数的“估计值”。这也解释了为何部分情况下χ²与对应的CFI值不会随模型复杂度呈单调变化趋势。鉴于MDΔχ²与卡方统计量本身一样，往往对样本量和轻微的模型设定误差过于敏感，且考虑到本研究中使用的MDΔχ²检验总次数，本研究将此类检验的显著性水平设定为0.01[52,53,54]。* p：本研究中所检验的预测模型的结果。