Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models
Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models
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DOI：
10.1080/00273171.2016.1142856
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发表时间：
2016-01-01
影响因子：
3.8
通讯作者：
Wilhelm, Oliver
中科院分区：
文献类型：
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作者：
Hildebrandt, Andrea;Luedtke, Oliver;Wilhelm, Oliver
使用经验数据集，我们研究了连续调节变量中因子模型参数的变化，并展示了三种建模方法：多组均值和协方差结构（MGMCS）分析，局部结构方程模型（LSEM）和调节因子分析（MFA）。我们重点讨论了如何研究作为连续变量（如年龄、社会经济地位、能力水平、文化适应等）函数的因子模型参数的变化。具体来说，我们正式的LSEM方法详细与以前的工作相比，并研究其统计特性与分析推导和模拟研究。我们还提供了用于轻松实现LSEM的代码。方法的说明是基于从4岁到23岁的个体的横截面认知能力数据。关于年龄分化假设的因素负荷的变化进行了检查。LSEM和MFA收敛的结论。当有一个广泛的年龄范围内的群体和不同的指标变量之间的关系和跨年龄的共同因素，MGMCS产生扭曲的参数估计。我们讨论的优点LSEM相比，MFA和建议使用这两种工具作为互补的方法，调查温和的因素模型参数。
Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.