Validity of factor score predictors in Bayesian and Maximum likelihood confirmatory factor analyses

贝叶斯和最大似然验证性因子分析中因子评分预测因子的有效性

• 批准号: 456131052
• 负责人: Professor Dr. André Beauducel
• 金额: --
• 依托单位: Abteilung Methodenlehre und Diagnostik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/456131052?language=en
• 关键词: Validity; factor; score; predictors; Bayesian

The overarching goal of the intended project is to provide benchmarks for the magnitude for coefficients of determination and to minimize bias for coefficients of determination as indicators of the factorial validity of factor score predictors and unit-weighted sum scales computed from Bayesian and maximum likelihood confirmatory factor analysis. The focus on factor score predictors and unit-weighted sum scales is due to the necessity to estimate individual scores whenever individuals are assigned to jobs or interventions. Three subgoals are to be investigated in separated simulation studies: The first subgoal is the comparison of magnitude and bias of coefficients of determination computed from confirmatory factor analyses based on maximum likelihood estimation and Bayesian estimation. The consequences of model misspecification due to non-specified cross-loadings in maximum likelihood estimation and the consequences of specification of expected variations of non-salient loadings (priors) in Bayesian estimation are compared. Besides the effect of population models and sample size, the effect of categorical data versus continuous, multivariate normal distributed data will also be investigated. The second subgoal refers to the effect of magnitude and bias of coefficients of determination on criterion validities, as they can be computed from factor score predictors. A direct comparison of modelling predictor-criterion relationships within structural equation models with the predictor-criterion relationships based on factor score predictors will be performed. The magnitude of the coefficient of determination necessary to achieve an acceptable similarity of predictor-criterion relationships based on latent modeling and factor score predictors will be ascertained. The third subgoal is the investigation of the effect of the magnitude of determination coefficients on the estimation of between-group mean differences based on factor score predictors and unit-weighted sum scales. For this purpose, latent between-group mean differences will be generated in multifactorial population models. It will then be investigated in the samples to what degree between-group differences based on factor score predictors and unit-weighted sum scales can be identified on those factors on which they occur in the population. Not only the correct identification of between-group differences on the respective factors is in the focus but also the correct non-occurrence of between-group differences on factors, where no between-group differences occur in the population. In the context of the second and third subgoal the maximum likelihood estimation and the Bayesian estimation will also be compared for different population models, sample sizes, as well as for continuous and categorical variables.

预期项目的总体目标是为决定系数的大小提供基准，并尽量减少决定系数的偏差，作为贝叶斯和最大似然验证性因素分析计算的因子得分预测因子和单位加权总和尺度的因子有效性指标。对因子得分预测因子和单位加权总和量表的关注是由于每当个人被分配到工作或干预措施时，都有必要估计个人得分。三个子目标是在单独的模拟研究进行调查：第一个子目标是比较的幅度和偏差的确定系数计算的验证性因素分析的基础上最大似然估计和贝叶斯估计。比较了极大似然估计中由于非指定交叉载荷而导致的模型误定的后果和贝叶斯估计中指定非显著载荷（先验）的预期变化的后果。除了群体模型和样本量的影响外，还将研究分类数据与连续多变量正态分布数据的影响。第二个子目标是指的影响的大小和偏差的决定系数的标准效度，因为它们可以计算因子得分预测。将直接比较结构方程模型中的建模预测-标准关系与基于因子评分预测因子的预测-标准关系。将确定必要的决定系数的大小，以实现基于潜在建模和因子得分预测器的预测器-标准关系的可接受的相似性。第三个子目标是调查决定系数的大小对基于因子得分预测因子和单位加权和量表的组间平均差异估计的影响。为此，将在多因素群体模型中生成潜在组间平均差异。然后，将在样本中调查基于因子得分预测因子和单位加权总和量表的组间差异在多大程度上可以根据它们在人群中发生的那些因子来确定。重点不仅是正确识别各个因素的组间差异，而且是正确地不发生因素的组间差异，即在人口中不发生组间差异。在第二和第三个子目标的背景下，还将针对不同的人口模型、样本量以及连续变量和分类变量，比较最大似然估计和贝叶斯估计。