Imaginary latent variables are variables with negative variances and have been used to implement constraints in measurement models. This article aimed to advance this practice and rationalize the imaginary latent variables as a method to detect possible latent deficiencies in measurement models. This rationale is based on the theory of complex…

Exploratory structural equation (ESEM) has received increased attention in the methodological literature as a promising tool for evaluating latent variable measurement models. It overcomes many of the limitations attached to exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), while capitalizing on the benefits of each. Given…

Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scores. Using a two-fold simulation design with varying availability of a priori theoretical knowledge, this study implemented hybrid centrality to estimate…

Metaheuristics are optimization algorithms that efficiently solve a variety of complex combinatorial problems. In psychological research, metaheuristics have been applied in short-scale construction and model specification search. In the present study, we propose a bee swarm optimization (BSO) algorithm to explore the structure underlying a…

Simulation studies are needed to investigate how many score categories are sufficient to treat ordered categorical data as continuous, particularly for bifactor models. The current simulation study aims to address such needs by investigating the performance of estimation methods in the bifactor models with ordered categorical data. Results support…

Dynamic structural equation models (DSEM) are designed for time series analysis of latent structures. Inherent to the application of DSEM is model parameter estimation, which has to be addressed in many applications by a single time series. In this context, however, the methods currently available either lack estimation quality or are…

Multilevel structural equation models (MSEMs) are well suited for educational research because they accommodate complex systems involving latent variables in multilevel settings. Estimation using Croon's bias-corrected factor score (BCFS) path estimation has recently been extended to MSEMs and demonstrated promise with limited sample sizes. This…

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…

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…

When compared to research centered on the executive function development of white, middle-class children, relatively little is known about their non-white, low socioeconomic status peers. In an effort to harmonize how executive functions are measured within under-represented contexts, the present study addresses gaps in the evaluation of everyday…

Data in social and behavioral sciences typically contain measurement errors and also do not have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze such data. This article discuss issues in latent-variable modeling as compared to regression analysis with composite-scores. Via logical reasoning and analytical results…

The Motivated Strategies for Learning Questionnaire (MSLQ) is one of the most widely used instruments to measure students' motivation and self-regulated learning. However, the MSLQ was developed and has been predominantly used in the Western context, is a domain-general measure, and is quite lengthy. Hence, adapting the MSLQ to the Chinese…

Purpose and Background: Despite the flexibility of multilevel structural equation modeling (MLSEM), a practical limitation many researchers encounter is how to effectively estimate model parameters with typical sample sizes when there are many levels of (potentially disparate) nesting. We develop a method-of-moment corrected maximum likelihood…

Planned missing data designs allow researchers to have highly-powered studies by testing only a fraction of the traditional sample size. In two-method measurement planned missingness designs, researchers assess only part of the sample on a high-quality expensive measure, while the entire sample is given a more inexpensive, but biased measure. The…

Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error…