Recovering Predictor-Criterion Relations Using Covariate-Informed Factor Score Estimates

Curran Patrick J.; Cole Veronica T.; Bauer Daniel J.; Rothenberg W. Andrew; Hussong Andrea M.

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Recovering Predictor-Criterion Relations Using Covariate-Informed Factor Score Estimates

机译：使用协变量信息因子得分估计值恢复预测因子与准则的关系

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Although it is currently best practice to directly model latent factors whenever feasible, there remain many situations in which this approach is not tractable. Recent advances in covariate-informed factor score estimation can be used to provide manifest scores that are used in second-stage analysis, but these are currently understudied. Here we extend our prior work on factor score recovery to examine the use of factor score estimates as predictors both in the presence and absence of the same covariates that were used in score estimation. Results show that whereas the relation between the factor score estimates and the criterion are typically well recovered, substantial bias and increased variability is evident in the covariate effects themselves. Importantly, using covariate-informed factor score estimates substantially, and often wholly, mitigates these biases. We conclude with implications for future research and recommendations for the use of factor score estimates in practice.

机译：尽管目前的最佳做法是在可行的情况下直接对潜在因素进行建模，但是在许多情况下，这种方法很难处理。协变量信息因子评分估计的最新进展可用于提供用于第二阶段分析的清单评分，但目前尚未对此进行深入研究。在这里，我们扩展了对因子得分恢复的先前工作，以检查在存在和不存在与得分估计相同的协变量的情况下，将因子得分估计用作预测因子的情况。结果表明，尽管因子得分估算值与标准之间的关系通常可以很好地恢复，但协变量效应本身可以证明存在明显的偏差和增加的变异性。重要的是，使用被协变量告知的因子得分估计值可以（通常是完全）减轻这些偏差。我们以对未来研究的启示和在实践中使用因子得分估计的建议作为结论。

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《Structural equation modeling》 |2018年第6期|860-875|共16页
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Curran Patrick J.; Cole Veronica T.; Bauer Daniel J.; Rothenberg W. Andrew; Hussong Andrea M.;
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Recovering Predictor-Criterion Relations Using Covariate-Informed Factor Score Estimates

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