Kevin J Grimm,Nilam Ram,Ryne Estabrook
Kevin J Grimm
Growth mixture models (GMMs; Muthén & Muthén, 2000; Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns....
An Idiographic Examination of Day-to-Day Patterns of Substance Use Craving, Negative Affect and Tobacco Use among Young Adults in Recovery [0.03%]
关于年轻戒瘾者日常的物质使用渴望、消极情绪和烟草使用的个别化研究
Yao Zheng,Richard P Wiebe,H Harrington Cleveland et al.
Yao Zheng et al.
Psychological constructs, such as negative affect and substance use cravings that closely predict relapse, show substantial intra-individual day-to-day variability. This intra-individual variability of relevant psychological states combined...
Joost R van Ginkel,Pieter M Kroonenberg
Joost R van Ginkel
As a procedure for handling missing data, Multiple imputation consists of estimating the missing data multiple times to create several complete versions of an incomplete data set. All these data sets are analyzed by the same statistical pro...
Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data [0.03%]
具有潜类依赖缺失数据的生长混合模型的贝叶斯推断
Zhenqiu Laura Lu,Zhiyong Zhang,Gitta Lubke
Zhenqiu Laura Lu
Growth mixture models (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GM...
Modeling Time-Dependent Association in Longitudinal Data: A Lag as Moderator Approach [0.03%]
纵向数据中时间依赖性关联的模型化：一种滞后作为调节变量的方法
James P Selig,Kristopher J Preacher,Todd D Little
James P Selig
We describe a straightforward, yet novel, approach to examine time-dependent association between variables. The approach relies on a measurement-lag research design in conjunction with statistical interaction models. We base arguments in fa...
Single and Multiple Ability Estimation in the SEM Framework: A Non-Informative Bayesian Estimation Approach [0.03%]
SEM框架下的单一和多项能力估计：一种非信息贝叶斯估计方法
Su-Young Kim,Youngsuk Suh,Jee-Seon Kim et al.
Su-Young Kim et al.
Latent variable models with many categorical items and multiple latent constructs result in many dimensions of numerical integration, and the traditional frequentist estimation approach, such as maximum likelihood (ML), tends to fail due to...
A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence [0.03%]
缺失数据下因素评分估计方法的比较：可靠性及其在尼古丁依赖中的应用
Ryne Estabrook,Michael Neale
Ryne Estabrook
Factor score estimation is a controversial topic in psychometrics, and the estimation of factor scores from exploratory factor models has historically received a great deal of attention. However, both confirmatory factor models and the exis...
Steven P Reise
Steven P Reise
Bifactor latent structures were introduced over 70 years ago, but only recently has bifactor modeling been rediscovered as an effective approach to modeling construct-relevant multidimensionality in a set of ordered categorical item respons...
Matthew S Fritz,Aaron B Taylor,David P Mackinnon
Matthew S Fritz
Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found...
Craig K Enders,Amanda J Fairchild,David P Mackinnon
Craig K Enders
Methodologists have developed mediation analysis techniques for a broad range of substantive applications, yet methods for estimating mediating mechanisms with missing data have been understudied. This study outlined a general Bayesian miss...