Subjects: Psychology >> Statistics in Psychology Subjects: Psychology >> Psychological Measurement submitted time 2022-03-08
Abstract：
In the first two decades of the twenty-first century, the hotspots of the methodological research on structural equation models (SEM) in China's mainland generally involve the following five aspects: model development, parameter estimation, model evaluation, measurement invariance and special data processing. Remarkably, there is more progress in model development (i.e., different variations of SEM) amongst the above aspects. After an overview of the background knowledge of these hotspots, we presented the main research topics and methodological achievements under each hotspot. We also discussed the recent progress of the foreign methodological studies on SEM and the future research directions.
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Subjects: Psychology >> Statistics in Psychology submitted time 2022-02-02
Abstract：
"Mediated moderation is frequently used in psychological research to reveal the phenomenon of a moderating effect being indirectly realized through mediating variables. This paper introduces the concept and advantages of a second type of mediated moderation (meMO-II). Then, we compare meMO-II with other models that combine mediation and moderation. Additionally, we propose the meMO-II modeling approach and analysis process, which we then demonstrated with a real example. We also introduce meMO-II analysis methods based on latent variables, advances in meMO-II modeling approaches, and variations in meMO-II. This offers a valuable contribution to moderating mechanism research.
Subjects: Psychology >> Statistics in Psychology submitted time 2021-12-09
Abstract：使用多元回归法进行调节效应分析在社科领域已常有应用。简述了目前多元回归法的调节效应分析存在的不足，包括人为变换检验模型、自变量和调节变量区分不足、误差方差齐性的假设难以满足、调节效应量指标△R2没有直接测量调节变量对自变量与因变量关系的调节程度。比较好的方法是用两水平回归模型进行调节效应分析并使用相应的效应量指标。在介绍新方法和新效应量后，总结出一套调节效应的分析流程，通过一个例子来演示如何用Mplus软件进行两水平回归模型的调节效应及其效应量分析。最后讨论了两水平回归模型的调节效应分析的发展，包括稳健的调节效应分析、潜变量的调节效应分析、有调节的中介效应分析和有中介的调节效应分析等。
Subjects: Psychology >> Statistics in Psychology submitted time 2021-10-12
Abstract： Analyzing the interaction effect of latent variables has become an important topic in both theoretical and empirical studies. Standardized estimation plays an important role in model interpretation and effect comparison. Although Wen et al. (2010) has formulated the appropriate standardized estimation for the latent interaction effects, there is no popular commercial software that provides the appropriate standardized estimation before the launch of Mplus 8.2 in 2019. Previous comparisons of methods for estimating latent interaction were based on the original estimation. In this study, through a simulation experiment, the appropriate standardized estimation of latent interaction effects is obtained respectively by four methods: the product indicator (PI) approach, Latent Moderated Structural Equations (LMS), Bayesian method without prior information (BN), and Bayesian method with prior information (BI). Then these estimations are compared in terms of the bias of estimation, the bias of standard error, type Ⅰ error rate and statistical power. The true model in the simulation is based on the structural equation η=0.4ξ_1+0.4ξ_2+γ_3 ξ_1 ξ_2+ζ where the latent variables η, ξ_1, ξ_2 each had three indicators with a standardized factor loading of 0.7. Experiment factors include the distribution of two exogenous latent variables (normal, non-normal), correlation ϕ_12 between two exogenous latent variables (0, 0.3 and 0.7), interaction effect γ_3 (0, 0.2), sample size N (100, 200, and 500) and estimation method (PI, LMS, BN, BI). There are five main findings. (1) the proportion of proper solution of LMS and the two Bayesian methods were close to 100% in all treatments, while PI was almost fully proper when N = 500. (2) Under the normal condition, the bias of standardized estimation of latent interaction obtained by LMS, BI and BN was ignorable, and PI was acceptable when N = 500. Under the non-normal condition, the bias of LMS and Bayesian methods inflated seriously with increasing correlation of two exogenous latent variables, but PI was still acceptable when N = 500. (3) Under both distribution conditions, the bias of standard error of standardized estimation of latent interaction obtained by LMS and BN was small and acceptable, while PI was acceptable when N = 500, and BI tended to overestimate the standard error. (4) Under normal conditions, the type I error rates of LMS were acceptable only when the sample size was large, while the other methods were acceptable in all conditions. Under the non-normal condition, the type I error rates of PI were still acceptable, while the other methods were acceptable only when the sample size was small or the correlation between two exogenous latent variables was low. (5) The statistical power of latent interaction obtained by PI was lower than that by any other method, and a large sample size (e.g., N=500) was required to ensure the PI with statistical power over 80%; LMS and BN had higher statistical power, while BI had the highest one in all conditions. For the latent interaction, the results of comparing different methods in standardized estimation are quite similar to those in the original estimation. Under the normal condition, it is recommended to use LMS to estimate the interaction effect of latent variables, with the caution of Type I error rate and effect size for inference. If accurate prior information can be obtained, Bayesian method is preferred, especially in the case of a small sample. When the variables are not normally distributed, the unconstrained product indicator approach is recommended, which is more robust than the other methods, but the sample size should be large enough (N =500 or above). If the correlation between exogenous latent variables is low (it can be estimated and tested by confirmatory factor analysis), Bayesian method without prior information can be considered for small samples.
Subjects: Psychology >> Statistics in Psychology Subjects: Psychology >> Psychological Measurement submitted time 2020-07-28
Abstract： "
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