Classe Franz, Kern Christoph
Deutsches Jugendinstitut e.V., Munchen, Germany.
Department of Statistics at Ludwig-Maximilians-University of Munich, Germany.
Educ Psychol Meas. 2024 Dec;84(6):1138-1172. doi: 10.1177/00131644241237502. Epub 2024 Apr 1.
We develop a (LV Forest) algorithm for the estimation of latent variable scores with one or more latent variables. LV Forest estimates unbiased latent variable scores based on (CFA) models with ordinal and/or numerical response variables. Through parametric model restrictions paired with a nonparametric tree-based machine learning approach, LV Forest estimates latent variable scores using models that are unbiased with respect to relevant subgroups in the population. This way, estimated latent variable scores are interpretable with respect to systematic influences of covariates without being biased by these variables. By building a tree ensemble, LV Forest takes parameter heterogeneity in latent variable modeling into account to capture subgroups with both good model fit and stable parameter estimates. We apply LV Forest to simulated data with heterogeneous model parameters as well as to real large-scale survey data. We show that LV Forest improves the accuracy of score estimation if parameter heterogeneity is present.
我们开发了一种用于估计具有一个或多个潜在变量的潜在变量得分的（LV森林）算法。LV森林基于具有有序和/或数值响应变量的（CFA）模型估计无偏潜在变量得分。通过将参数模型限制与基于非参数树的机器学习方法相结合，LV森林使用相对于总体中相关亚组无偏的模型来估计潜在变量得分。这样，估计的潜在变量得分在协变量的系统影响方面是可解释的，而不会受到这些变量的偏差影响。通过构建树集成，LV森林考虑了潜在变量建模中的参数异质性，以捕获具有良好模型拟合和稳定参数估计的亚组。我们将LV森林应用于具有异质模型参数的模拟数据以及真实的大规模调查数据。我们表明，如果存在参数异质性，LV森林可以提高得分估计的准确性。
