Anne-Charlotte J Belloeil
Anne-Charlotte J Belloeil
A Comparison of Regularization, Alignment, and a Traditional Method for Estimating Structural Relationships Across Two Groups [0.03%]
一种基于正则化、校准和传统方法的跨群体结构关系估计算法的比较研究
Emma Somer,Carl F Falk,Milica Miočević
Emma Somer
Establishing the correct partial measurement invariance model is crucial for ensuring unbiased comparisons of relationships between latent variables across multiple groups. While traditional approaches rely on detecting noninvariant items f...
A State Space Model of Daily Dynamics with Moderation Effects from Qualitative Text Data [0.03%]
基于定性文本数据调节效应的日度动态时空模型
Samuel D Aragones,Emorie D Beck,Emilio Ferrer
Samuel D Aragones
The last two decades have seen a dramatic increase in using intensive longitudinal data to capture psychological processes. Intensive longitudinal data allow researchers to study intraindividual change and variability. Multiple modeling app...
Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish [0.03%]
利用InterModel Vigorish评估确认性因素分析中二元结果的模型预测性能
Lijin Zhang,Charles Rahal,Klint Kanopka et al.
Lijin Zhang et al.
Confirmatory Factor Analysis (CFA) has been widely used to assess the fit of theoretical measurement models to observed data. We introduce the InterModel Vigorish (IMV) to the field; a predictive fit index that offers novel perspectives for...
Improving the Evaluation of Construct Change Over Time: Advantages of Longitudinal Moderated Nonlinear Factor Analysis Over Conventional First-Order Growth Models [0.03%]
改进随时间变化的结构改变的评价：纵向调制非线性因素分析优于传统一阶增长模型的优势
Siyuan Marco Chen,Daniel J Bauer
Siyuan Marco Chen
Conventional growth curve models, often fitted to sum or mean scores of scale responses, do not account for potential changes in item measurement unrelated to construct growth (i.e. differential item functioning; DIF). An untested assumptio...
Multi-Group Multidimensional Classification Accuracy Analysis (MMCAA): A General Framework for Evaluating the Practical Impact of Partial Invariance [0.03%]
多组多维分类准确性分析（MMCAA）：评估部分不变性实际影响的一般框架
Meltem Ozcan,Mark H C Lai
Meltem Ozcan
Measurement invariance (MI) is a prerequisite for the meaningful and valid comparison of test scores across individuals with different group membership. Given that tests are often used in high-stakes contexts (e.g., diagnosis), the practica...
Katrin Jansen,Steffen Nestler
Katrin Jansen
Often, primary studies that are pooled in a meta-analysis provide information on several outcomes of interest. Multivariate meta-analysis allows to analyze these outcomes simultaneously and model their relationship, and in addition can be m...
Diego Iglesias,Miguel A Sorrel,Ricardo Olmos
Diego Iglesias
Multilevel Models (MLMs) have become a valuable tool in the behavioral and social sciences, providing a framework for analyzing clustered data structures commonly encountered in these fields. Unlike single-level regression, R2 measures in M...
A Hierarchical Ordinal Regression Model for Treatment-Reversal Designs with Application to Non-Overlap Effect Sizes [0.03%]
一种用于治疗反转设计的层次有序回归模型及其在非重叠效应大小的应用研究
James Ohisei Uanhoro,Megan Rojo
James Ohisei Uanhoro
We present a hierarchical ordinal model for analyzing single-case designs (SCDs), with a focus on treatment-reversal designs. SCDs involve systematic measurement of outcomes for individual cases across different conditions or phases, aiming...
To Disaggregate or Not to Disaggregate: A Focus on Covariates in Multilevel Models [0.03%]
分离还是不解离：多水平模型中协变量的关注点
Remus Mitchell,Craig K Enders,Yi Feng
Remus Mitchell
It is routinely recommended that level-1 variables in multilevel models be disaggregated when they are of substantive importance. Yet, the consensus on the disaggregation of level-1 covariates is more mixed. Disaggregation clarifies interpr...