Loncke Justine, Eichelsheim Veroni I, Branje Susan J T, Buysse Ann, Meeus Wim H J, Loeys Tom
Department of Data Analysis, Ghent University, Ghent, Belgium.
Netherlands Institute for the Study of Crime and Law Enforcement, Amsterdam, Netherlands.
Front Psychol. 2018 Sep 19;9:1699. doi: 10.3389/fpsyg.2018.01699. eCollection 2018.
The family social relations model (SRM) is applied to identify the sources of variance in interpersonal dispositions in families, but the antecedents or consequences of those sources are rarely investigated. Simultaneous modeling of the SRM with antecedents or consequences using structural equation modeling (SEM) allows to do so, but may become computationally prohibitive in small samples. We therefore consider two factor score regression (FSR) methods: regression and Bartlett FSR. Based on full information maximum likelihood (FIML), we derive closed-form expressions for the regression and Bartlett factor scores in the presence of missingness. A simulation study in both a complete- and incomplete-case setting compares the performance of these FSR methods with SEM and an ANOVA-based approach. In both settings, the regression FIML factor scores as explanatory variable produces unbiased estimators with precision comparable to the SEM-estimators. When SRM-effects are used as dependent variables, none of the FSR methods are a suitable alternative for SEM. The latter result deviates from previous studies on FSR in more simple settings. As an example, we explore whether gender and past victimhood of relational and physical aggression are antecedents for family dynamics of perceived support, and whether those dynamics predict physical and relational aggression.
家庭社会关系模型(SRM)被用于识别家庭中人际倾向的差异来源,但这些来源的前因或后果很少被研究。使用结构方程模型(SEM)对SRM及其前因或后果进行同时建模可以做到这一点,但在小样本中可能在计算上令人望而却步。因此,我们考虑两种因子得分回归(FSR)方法:回归和巴特利特FSR。基于全信息最大似然法(FIML),我们推导出了存在缺失值时回归和巴特利特因子得分的闭式表达式。在完整案例和不完整案例设置下的模拟研究比较了这些FSR方法与SEM以及基于方差分析方法的性能。在这两种设置下,将回归FIML因子得分作为解释变量会产生无偏估计量,其精度与SEM估计量相当。当将SRM效应用作因变量时,没有一种FSR方法是SEM的合适替代方法。后一个结果与之前在更简单设置下关于FSR的研究结果不同。作为一个例子,我们探讨性别以及过去在关系攻击和身体攻击方面的受害经历是否是感知支持的家庭动态的前因,以及这些动态是否预测身体攻击和关系攻击。