Regression discontinuity (RD) designs are increasingly used for causal evaluations. However, the literature contains little guidance for conducting a moderation analysis within an RDD context. The current article focuses on moderation with a single binary variable. A simulation study compares: (1) different bandwidth selectors and (2) local…

Parallel analysis has been considered one of the most accurate methods for determining the number of factors in factor analysis. One major advantage of parallel analysis over traditional factor retention methods (e.g., Kaiser's rule) is that it addresses the sampling variability of eigenvalues obtained from the identity matrix, representing the…

Researchers continue to choose PCA in scale development and adaptation studies because it is the default setting and overestimates measurement quality. When PCA is utilized in investigations, the explained variance and factor loadings can be exaggerated. PCA, in contrast to the models given in the literature, should be investigated in…

Popular item response theory (IRT) models are considered complex, mainly due to the inclusion of a random factor variable (latent variable). The random factor variable represents the incidental parameter problem since the number of parameters increases when including data of new persons. Therefore, IRT models require a specific estimation method…

This study conducted a comprehensive comparison of Item Response Theory (IRT) linking methods applied to a bifactor model, examining their performance on both multiple choice (MC) and mixed format tests within the common item nonequivalent group design framework. Four distinct multidimensional IRT linking approaches were explored, consisting of…

The multiple imputation two-stage (MI2S) approach holds promise for evaluating the model fit of structural equation models for ordinal variables with multiply imputed data. However, previous studies only examined the performance of MI2S-based residual-based test statistics. This study extends previous research by examining the performance of two…

This study proposes a Bayesian method for diagnostic classification models (DCMs) for a partially known Q-matrix setting between exploratory and confirmatory DCMs. This Q-matrix setting is practical and useful because test experts have pre-knowledge of the Q-matrix but cannot readily specify it completely. The proposed method employs priors for…

Joint attention is essential for establishing effective social communication. However, children with Autism Spectrum Disorder (ASD) often exhibit deficits in joint attention, which hinder their social interactions with peers and adults. Traditional assessment methods predominantly rely on expert observation, lacking objective measures of…

In this scoping review on 34 studies, we examined the use of immersive virtual reality (IVR) in neurodevelopmental disorders (NDD). IVR was mostly used in connection with autism spectrum disorder (ASD) for assessment of and intervention in social skills, and in attention deficit hyperactivity disorder (ADHD) for assessment of executive…

The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the…

Complex competences, such as managing students' aggressive behaviour, are challenging to develop during teacher training. Recently, video-based simulations have been considered promising, yet suitable assessment instruments are limitedly available. This paper reports on the design and evaluation of a video-based assessment tool tailored to measure…

Researchers often study dynamic processes of latent variables in everyday life, such as the interplay of positive and negative affect over time. An intuitive approach is to first estimate the measurement model of the latent variables, then compute factor scores, and finally use these factor scores as observed scores in vector autoregressive…

The detection of differential item functioning (DIF) is important in psychological and behavioral sciences. Standard DIF detection methods perform an item-by-item test iteratively, often assuming that all items except the one under investigation are DIF-free. This article proposes a Bayesian adaptive Lasso method to detect DIF in graded response…

Makerspaces are interactive learning environments that provide students with opportunities to improve life and career skills. The importance of these spaces has grown over the years. In Türkiye, the Ministry of National Education has established makerspace-like environments, known as Design and Skills-Labs (Tasarim Beceri Atölyeleri or TBA in…

This study aims to create and validate an evaluation tool that assesses the learning effectiveness of extended reality applications (e.g., augmented reality, virtual reality, mixed reality, and the metaverse). Six research questions were formulated to guide this study. The Extended Reality in Education (XREd) questionnaire consists of 50-items…