Titlebook: Quantitative Psychology; The 88th Annual Meet Marie Wiberg,Jee-Seon Kim,Heungsun Hwang,Hao Wu,Tr Conference proceedings 2024 The Editor(s)

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Comparing Maximum Likelihood to Markov Chain Monte Carlo Estimation of the Multivariate Social Relave a unique nesting structure in that dyads (pairs) are cross-classified within individuals, who can also be nested in different networks. The SRM is used to examine basic multivariate relations between components of dyadic variables at two levels: individual-level random effects and dyad-level resi

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Exploring Attenuation of Reliability in Categorical Subscore Reporting,ies and operational testing programs have not changed. This may be due to several real-world complications such as user demand, competitors, or contractual obligations. Given these challenges, some test providers have continued to report subscores but in a categorical format to mitigate misinterpret

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Assessing Cross-Level Interactions in Clustered Data Using CATE Estimation Methods,for many treatments and interventions. In environments where individuals are clustered within communities, effect heterogeneity is commonplace rather than an exception, as characteristics of communities often interact with a treatment implemented on members within the communities, and such interacti

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A Comparison of Full Information Maximum Likelihood and Machine Learning Missing Data Analytical Me handle ignorable missing data. However, they may lead to biased model estimation due to missing not at random data that often appear in longitudinal studies. Recently, machine learning methods, such as random forest (RF) and K-nearest neighbors (KNN) imputation methods, have been proposed to cope w

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Investigating Variable Selection Techniques Under Missing Data: A Simulation Study,ata collection strategies. Machine learning methods such as LASSO and elastic net regression have gained traction in the field but are limited in the types of problems for which they are suitable. As such, researchers have pulled more complex techniques, such as the genetic algorithm, from fields li

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Comparison of DIF Detection Methods,ical methods have been developed to detect DIF, and as a result, many simulation studies have been conducted to evaluate the performance of two or three methods under different test situations. To have an overall picture on how they compare for tests with dichotomous items with and without DIF, this

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Enhancing Multilevel Models Through Supervised Machine Learning,sts, regional house price predictions). In such data, observations within clusters exhibit dependencies, violating assumptions of independence and identical distribution. To address this, multilevel random effects are used instead of fixed effects. Starting from linear approaches, typical multilevel

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