Multilevel SEM with random slopes in discrete data using the pairwise maximum likelihood

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS British Journal of Mathematical & Statistical Psychology Pub Date : 2023-01-12 DOI:10.1111/bmsp.12294
Maria T. Barendse, Yves Rosseel
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引用次数: 1

Abstract

Pairwise maximum likelihood (PML) estimation is a promising method for multilevel models with discrete responses. Multilevel models take into account that units within a cluster tend to be more alike than units from different clusters. The pairwise likelihood is then obtained as the product of bivariate likelihoods for all within-cluster pairs of units and items. In this study, we investigate the PML estimation method with computationally intensive multilevel random intercept and random slope structural equation models (SEM) in discrete data. In pursuing this, we first reconsidered the general ‘wide format’ (WF) approach for SEM models and then extend the WF approach with random slopes. In a small simulation study we the determine accuracy and efficiency of the PML estimation method by varying the sample size (250, 500, 1000, 2000), response scales (two-point, four-point), and data-generating model (mediation model with three random slopes, factor model with one and two random slopes). Overall, results show that the PML estimation method is capable of estimating computationally intensive random intercept and random slopes multilevel models in the SEM framework with discrete data and many (six or more) latent variables with satisfactory accuracy and efficiency. However, the condition with 250 clusters combined with a two-point response scale shows more bias.

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使用成对最大似然的离散数据中具有随机斜率的多层扫描电镜
成对极大似然估计是求解具有离散响应的多层模型的一种很有前途的方法。多层模型考虑到集群内的单元往往比来自不同集群的单元更相似。然后获得成对似然作为所有在集群内的单位和项目对的二元似然的乘积。在本研究中,我们研究了离散数据中计算密集的多水平随机截距和随机斜率结构方程模型(SEM)的PML估计方法。为了实现这一目标,我们首先重新考虑了SEM模型的一般“宽格式”(WF)方法,然后将WF方法扩展为随机斜率。在一项小型模拟研究中,我们通过改变样本量(250,500,1000,2000),响应尺度(两点,四点)和数据生成模型(具有三个随机斜率的中介模型,具有一个和两个随机斜率的因子模型)来确定PML估计方法的准确性和效率。总体而言,结果表明PML估计方法能够在具有离散数据和多个(6个或更多)潜在变量的SEM框架中估计计算密集的随机截距和随机斜率多水平模型,并且具有令人满意的精度和效率。然而,250个集群与两点反应量表相结合的情况显示出更大的偏差。
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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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