Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…

Several variants of the autoregressive structural equation model were suggested over the past years, including, for example, the random intercept autoregressive panel model, the latent curve model with structured residuals, and the STARTS model. The present work shows how to place these models into a mixed-effects model framework and how to…

Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical…

Longitudinal data structures are frequently encountered in a variety of disciplines in the social and behavioral sciences. Growth curve modeling offers a highly extensible framework that allows for the exploration of rich hypotheses. However, owing to the presence of interrelated sources of potential data-model misfit at multiple levels, the…

Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study…

This study aimed to evaluate structural models and measurement models of student satisfaction in online learning. This was a quantitative study using a survey research design. Structural model testing was done by examining the relationship between several variables. The variables in question were the learning management system (LMS), admin…

Multilevel factor mixture modeling (FMM) is a hybrid of multilevel confirmatory factor analysis (CFA) and multilevel latent class analysis (LCA). It allows researchers to examine population heterogeneity at the within level, between level, or both levels. This tutorial focuses on explicating the model specification of multilevel FMM that considers…

There is a growing need to empirically evaluate the quality of online instructional interventions at scale. In response, some online learning platforms have begun to implement rapid A/B testing of instructional interventions. In these scenarios, students participate in series of randomized experiments that evaluate problem-level interventions in…

A slowdown or a speedup in response times across experimental conditions can be taken as evidence of online deployment of knowledge. However, response-time difference measures are rarely evaluated on their reliability, and there is no standard practice to estimate it. In this article, we used three open data sets to explore an approach to…

Background/purpose: This study aimed to reveal the accuracy of estimation of multiple-choice test items parameters following the models of the item-response theory in measurement. Materials/methods: The researchers depended on the measurement accuracy indicators, which express the absolute difference between the estimated and actual values of the…

Measuring innovation is key to realizing innovation in practice. One of the primary reasons why measurement is important stems from the fundamental principle that what is measured is, in turn, what garners attention and action. Systematic measurement of innovation can enable researchers and practitioners to propose and undertake strategic…

Why should our life science colleagues adopt, adapt, and implement fractal thinking into their research and teaching paradigms? While numerous articles and books have been written about the utility, power, and insight that can be gained by using fractal analysis of data and developing fractal models, other than just appreciating the beauty of…

Confirmatory factor analysis (CFA) is a commonly used statistical technique. Recommendations for evaluating CFA highlight scholars should outline the expected model, conduct data screening, report model estimation and evaluation, and report key information about results to provide evidence for latent variables. The purpose of the current study was…

The standard model of particle physics is the most successful theory describing the behavior of matter and energy in the subatomic realm. However, success doesn't mean it is perfect, and a recent measurement of the mass of a particle called the W boson is puzzling, as it disagrees with theoretical predictions and earlier precise measurements. If…

Purpose: The purpose of this paper is to use the case of York University in Canada to analyze the connection between University Social Responsibility and voluntary disclosure. The authors examine whether the university's voluntary air emissions disclosure is performative by exploring whether York University's espoused commitment to its community…