K B S Huth,B DeLong,L Waldorp et al.
K B S Huth et al.
Psychometric networks can be estimated using nodewise regression to estimate edge weights when the joint distribution is analytically difficult to derive or the estimation is too computationally intensive. The nodewise approach runs general...
Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends [0.03%]
时间序列分析中的非平稳性：建模随机性和确定性趋势
Oisín Ryan,Jonas M B Haslbeck,Lourens J Waldorp
Oisín Ryan
Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing fre...
Evidence That Growth Mixture Model Results Are Highly Sensitive to Scoring Decisions [0.03%]
增长混合模型结果受评分决策影响的证据
James Soland,Veronica Cole,Stephen Tavares et al.
James Soland et al.
Interest in identifying latent growth profiles to support the psychological and social-emotional development of individuals has translated into the widespread use of growth mixture models (GMMs). In most cases, GMMs are based on scores from...
Xiao Liu,Mark Eddy,Charles R Martinez
Xiao Liu
When studying effect heterogeneity between different subgroups (i.e., moderation), researchers are frequently interested in the mediation mechanisms underlying the heterogeneity, that is, the mediated moderation. For assessing mediated mode...
MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables [0.03%]
基于模型蕴含工具变量的探索性因素分析及其R程序包MIIVefa
Lan Luo,Kathleen M Gates,Kenneth A Bollen
Lan Luo
We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analys...
On the Latent Structure of Responses and Response Times from Multidimensional Personality Measurement with Ordinal Rating Scales [0.03%]
基于有序评分量表的多维人格测量中的反应及其反应时间的潜在结构
Inhan Kang
Inhan Kang
In this article, we propose latent variable models that jointly account for responses and response times (RTs) in multidimensional personality measurements. We address two key research questions regarding the latent structure of RT distribu...
Evaluating Contextual Models for Intensive Longitudinal Data in the Presence of Noise [0.03%]
含噪声情况下上下文模型在密集纵向数据中的评估
Anja F Ernst,Eva Ceulemans,Laura F Bringmann et al.
Anja F Ernst et al.
Nowadays research into affect frequently employs intensive longitudinal data to assess fluctuations in daily emotional experiences. The resulting data are often analyzed with moderated autoregressive models to capture the influences of cont...
A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series [0.03%]
基于心理时间序列的特征化聚类方法及其应用研究
Jannis Kreienkamp,Maximilian Agostini,Rei Monden et al.
Jannis Kreienkamp et al.
Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of dynamic measures that de...
Using Projective IRT to Evaluate the Effects of Multidimensionality on Unidimensional IRT Model Parameters [0.03%]
利用投影IRT方法评价多维性对一维IRT模型参数的影响
Steven P Reise,Jared M Block,Maxwell Mansolf et al.
Steven P Reise et al.
The application of unidimensional IRT models requires item response data to be unidimensional. Often, however, item response data contain a dominant dimension, as well as one or more nuisance dimensions caused by content clusters. Applying ...
On the Importance of Considering Concurrent Effects in Random-Intercept Cross-Lagged Panel Modelling: Example Analysis of Bullying and Internalising Problems [0.03%]
关于在随机拦截交叉滞後面板建模中考虑并发效应的重要性的思考——基于欺凌和内部化问题的实证分析为例
Lydia G Speyer,Xinxin Zhu,Yi Yang et al.
Lydia G Speyer et al.
Random-intercept cross-lagged panel models (RI-CLPMs) are increasingly used to investigate research questions focusing on how one variable at one time point affects another variable at the subsequent time point. Due to the implied temporal ...