Exploring the Effects of Sampling Variability, Scale Variability, and Node Aggregation on the Consistency of Estimated Networks [0.03%]
探索采样变异性、尺度变异性及节点聚合对估计网络一致性的影响
Arianne Herrera-Bennett,Mijke Rhemtulla
Arianne Herrera-Bennett
Work surrounding the replicability and generalizability of network models has increased in recent years, prompting debate on whether network properties can be expected to be consistent across samples. To date, certain methodological practic...
Model Selection for Mixed-Effects Location-Scale Models with Confidence Interval for LOO or WAIC Difference [0.03%]
混合效应位置尺度模型的选择以及LOO或WAIC差异的置信区间
Yue Liu,Fan Fang,Hongyun Liu
Yue Liu
LOO (Leave-One-Out cross-validation) and WAIC (Widely Applicable Information Criterion) are widely used for model selection in Bayesian statistics. Most studies select the model with the smallest value based on point estimates, often withou...
A Tutorial on the Use of Artificial Intelligence Tools for Facial Emotion Recognition in R [0.03%]
使用人工智能工具在R中进行面部情绪识别的教程
Austin Wyman,Zhiyong Zhang
Austin Wyman
Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion ...
Autoencoders for Amortized Joint Maximum Likelihood Estimation of Confirmatory Item Factor Models [0.03%]
确认性项目因子模型的联合极大似然估计的自编码器算法研究
Dylan Molenaar,Raoul P P P Grasman,Mariana Cúri
Dylan Molenaar
Neural networks like variational autoencoders have been proposed as a statistical tool to fit item factor models to data. Advantages are that high dimensional models can be estimated more efficiently as compared to conventional approaches. ...
TDCM: An R Package for Estimating Longitudinal Diagnostic Classification Models [0.03%]
TDCM：一个用于估计纵向诊断分类模型的R包
Matthew J Madison,Minjeong Jeon,Michael Cotterell et al.
Matthew J Madison et al.
Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or non-proficiency of specified latent attributes. Longitudinal DCMs have recently been developed as psychometric ...
Interrater Reliability for Interdependent Social Network Data: A Generalizability Theory Approach [0.03%]
广义测量理论在社会网络数据互倚性检验中的应用研究
Debby Ten Hove,Terrence D Jorgensen,L Andries van der Ark
Debby Ten Hove
We propose interrater reliability coefficients for observational interdependent social network data, which are dyadic data from a network of interacting subjects that are observed by external raters. Using the social relations model, dyadic...
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...