Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent…

Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify empirical evaluation, researchers typically…

Background: Structural Equation Modeling (SEM) is a powerful and broadly utilized statistical framework. Researchers employ these models to dissect relationships into direct, indirect, and total effects (Bollen, 1989). These models unpack the "black box" issues within cause-and-effect studies by examining the underlying theoretical…

The study investigated the structural equation modelling of nine variables consisting of research undertaking, digi-tech construct (digital nativity, category of adoption of digital technologies, digital literacy, digital citizenship, statistical software anxiety, self-efficacy and knowledge) and researcher statistical software skills in the…

In parallel process latent growth curve mediation models, the mediation pathways from treatment to the intercept or slope of outcome through the intercept or slope of mediator are often of interest. In this study, we developed causal mediation analysis methods for these mediation pathways. Particularly, we provided causal definitions and…

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…

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…

The consequence of a change in school leadership (e.g., principal turnover) on student achievement has important implications for education policy. The impact of such an event can be estimated via the popular Difference in Difference (DiD) estimator, where those schools with a turnover event are compared to a selected set of schools that did not…

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…

Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models…

The applicability of digital technologies is increasing boundlessly and so are the opportunities of the end-users, with ample opportunities to embed these technologies in the teaching and learning process. Nonetheless, classroom adoption of technologies, particularly in mathematics remains on the lower end of innovation in teaching and learning.…

Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Furthermore, in many cases only some students may be nested within a unit while other students may…

Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Further, in many cases only some students may be nested within a unit while other students may not.…

Affective factors such as the achievement emotions are considered critical for students' academic performance in STEM degree programmes and careers. In this study, a reciprocal causation model was tested between two affective factors: enjoyment and anxiety, and organic chemistry course performance. Each variable was measured three times in four…

Despite the prevalence of ordinal observed variables in applied structural equation modeling (SEM) research, limited attention has been given to model evaluation methods suitable for ordinal variables, thus providing practitioners in the field with few guidelines to follow. This study represents a first attempt to thoroughly examine the…