Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and…

Research Findings: During the COVID-19 pandemic, the education system faced unprecedented challenges, including global school closures, the cancellation of face-to-face teaching, and ultimately school step-wise or partial reopening. Childcare providers have faced additional significant stressors from the beginning of the outbreak. The present…

Scale validation is vital to psychological research because it ensures that scores from measurement scales represent the intended construct. Factor analysis fit indices are commonly used to provide quantitative evidence that a proposed factor structure is plausible. However, there is mismatch between guidelines for evaluating fit of factor models…

Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying…

Examining the academic locus of control, procrastination, and school satisfaction is crucial for understanding student well-being and educational outcomes. The purpose of this study was to explore the potential mediating role of academic procrastination in the association between academic locus of control and school satisfaction in a sample of…

Measurement invariance (MI) is required for validly comparing latent constructs measured by multiple ordinal self-report items. Non-invariances may occur when disregarding (group differences in) an acquiescence response style (ARS; an agreeing tendency regardless of item content). If non-invariance results solely from neglecting ARS, one should…

This study used a meta-analytic structural equation modeling approach to build extended versions of the simple view of reading (SVR) model in second and foreign language (SFL) learning contexts (i.e., SVR-SFL). Based on the correlation coefficients derived from primary studies, we replicated and integrated two previous extended meta-analytic SVR…

As colleges and universities grapple with uncertainty around current and future enrollment as well as increasingly vocal questions about the value of postsecondary education, it is critically important that institutions ascertain and invest in the elements of campus learning and engagement that add value to the undergraduate experience. This study…

Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…

Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…

The research presented here is based on the objective of analyzing whether there is a relationship among neurodidactics, educational inclusion and sustainability in a university context. The starting point was a non-experimental, descriptive, explanatory and correlational research, using an ad hoc Likert scale as a data collection instrument,…

This article provides an overview of different computational options for inference following normal theory maximum likelihood (ML) estimation in structural equation modeling (SEM) with incomplete normal and nonnormal data. Complete data are covered as a special case. These computational options include whether the information matrix is observed or…

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 aim of this study is to examine the mediating role of self-compassion in the relationship between perfectionism and academic procrastination in teacher candidates. Structural equation modeling, one of the quantitative research methods, was used in the study. The participants of the study consisted of a total of 478 teacher candidates, 328…

In a recent publication in the Journal of Intelligence, Dennis McFarland mischaracterized previous research using latent variable and psychometric network modeling to investigate the structure of intelligence. Misconceptions presented by McFarland are identified and discussed. We reiterate and clarify the goal of our previous research on network…