Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation [0.03%]
日常日记应对数据的多层次因素分析和结构方程建模：特征与状态变异模型化
Scott C Roesch,Arianna A Aldridge,Stephanie N Stocking et al.
Scott C Roesch et al.
The current study used multilevel modeling of daily diary data to model within-person (state) and between-person (trait) components of coping variables. This application included the introduction of multilevel factor analysis (MFA) and a co...
Elizabeth A Stuart,Nicholas S Ialongo
Elizabeth A Stuart
This work examines ways to make the best use of limited resources when selecting individuals to follow up in a longitudinal study estimating causal effects. In the setting under consideration, covariate information is available for all indi...
Further Insight and Additional Inference Methods for Polynomial Regression Applied to the Analysis of Congruence [0.03%]
多项式回归在一致性的分析中的进一步见解和附加推断方法
Ayala Cohen,Inbal Nahum-Shani,Etti Doveh
Ayala Cohen
In their seminal paper, Edwards and Parry (1993) presented the polynomial regression as a better alternative to applying difference score in the study of congruence. While this method is increasingly applied in congruence research, its comp...
A Dual-Process Discrete-Time Survival Analysis Model: Application to the Gateway Drug Hypothesis [0.03%]
一种双重过程离散时间生存分析模型及其在入门级毒品理论中的应用
Patrick S Malone,Dorian A Lamis,Katherine E Masyn et al.
Patrick S Malone et al.
The gateway drug model is a popular conceptualization of a progression most substance-users are hypothesized to follow as they try different legal and illegal drugs. Most forms of the gateway hypothesis are that "softer" drugs lead to "hard...
Marginal and Random Intercepts Models for Longitudinal Binary Data With Examples From Criminology [0.03%]
纵向二元数据的边际和随机效应模型及其在犯罪学中的应用举例
Jeffrey D Long,Rolf Loeber,David P Farrington
Jeffrey D Long
Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchi...
Alternative Model-Based and Design-Based Frameworks for Inference From Samples to Populations: From Polarization to Integration [0.03%]
从二元对立到整合：有关样本推断总体的模型化与现实框架之比较及对话研究方法论综述文献述评文章META-分析/R-包/API
Sonya K Sterba
Sonya K Sterba
A model-based framework, due originally to R. A. Fisher, and a design-based framework, due originally to J. Neyman, offer alternative mechanisms for inference from samples to populations. We show how these frameworks can utilize different t...
Evaluating Group-Based Interventions When Control Participants Are Ungrouped [0.03%]
以无结构对照组评估基于团体的干预措施
Daniel J Bauer,Sonya K Sterba,Denise Dion Hallfors
Daniel J Bauer
Individually randomized treatments are often administered within a group setting. As a consequence, outcomes for treated individuals may be correlated due to provider effects, common experiences within the group, and/or informal processes o...
Higher Order Invariance and Age Comparisons in Depression, Neuroticism, and Anxiety [0.03%]
抑郁、神经质和焦虑的年龄差异及其高阶不变性分析
Ryne Estabrook,Timothy A Salthouse,John R Nesselroade
Ryne Estabrook
Exploring the Sensitivity of Horn's Parallel Analysis to the Distributional Form of Random Data [0.03%]
霍恩平行分析法对随机数据分布形式敏感性的探讨
Alexis Dinno
Alexis Dinno
Horn's parallel analysis (PA) is the method of consensus in the literature on empirical methods for deciding how many components/factors to retain. Different authors have proposed various implementations of PA. Horn's seminal 1965 article, ...
Distinguishing between latent classes and continuous factors with categorical outcomes: Class invariance of parameters of factor mixture models [0.03%]
基于分类混合因子模型参数不变性区分潜在类别与连续因子及连续因子多少种可能性的问题探究：具有定性结果的分类模型参数不变性的区别研究
Gitta Lubke,Michael Neale
Gitta Lubke
Factor mixture models (FMM's) are latent variable models with categorical and continuous latent variables which can be used as a model-based approach to clustering. A previous paper covered the results of a simulation study showing that in ...