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The increasing mainstream adoption of immersive virtual reality (iVR) in education has triggered research about key variables explaining acceptance of iVR by teachers. In this study we adopted the UTAUT2 acceptance model as a theoretical framework enriched with the variable personal innovativeness. 379 Flemish secondary education teachers watched a video about iVR learning experiences, after which an online survey concerning their perceptions was administered. General linear modeling was performed to test the hypotheses. Results indicate performance expectancy, social influence, facilitating conditions, hedonic motivation and personal innovativeness to be significantly associated with behavioural intention to use. No moderating effect of age, gender or experience was observed. The results account for 54% of the variance in behavioural intention to use. The findings help to understand which factors are key in the acceptance of mobile iVR by secondary education teachers and might help defining successful iVR implementation strategies.

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 in the factor scores. The method of Croon (MOC) is a well-known bias correction technique. However, its standard implementation can render poor quality estimates in small samples (e.g. less than 100). This article aims to develop a small sample correction (SSC) that integrates two different modifications to the standard MOC. We conducted a simulation study to compare the empirical performance of (a) standard SEM, (b) the standard MOC, (c) naive FSR, and (d) the MOC with the proposed SSC. In addition, we assessed the robustness of the performance of the SSC in various models with a different number of predictors and indicators. The results showed that the MOC with the proposed SSC yielded smaller mean squared errors than SEM and the standard MOC in small samples and performed similarly to naive FSR. However, naive FSR yielded more biased estimates than the proposed MOC with SSC, by failing to account for measurement error in the factor scores.

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and binary data, as evidence regarding the accuracy of such criteria in the latter case is scarce. Simulation results, based on scenarios resulting from varying sample size, communalities from major factors, interfactor correlations, skewness, and correlation measure, show that EGA outperforms the traditional factor retention criteria considered in most cases in terms of bias and accuracy. In addition, we show that factor retention decisions for binary data are preferably made using Pearson, instead of tetrachoric, correlations, which is contradictory to popular belief.

Factor score regression (FSR) is a popular alternative for structural equation modeling. Naively applying FSR induces bias for the estimators of the regression coefficients. Croon proposed a method to correct for this bias. Next to estimating effects without bias, interest often lies in inference of regression coefficients or in the fit of the model. In this article, we propose fit indices for FSR that can be used to inspect the model fit. We also introduce a model comparison test based on one of these newly proposed fit indices that can be used for inference of the estimators on the regression coefficients. In a simulation study we compare FSR with Croon's corrections and structural equation modeling in terms of bias of the regression coefficients, Type I error rate and power.

In structural equation modeling (SEM), the measurement and structural parts of the model are usually estimated simultaneously. In this article, we revisit the long-standing idea that we should first estimate the measurement part, and then estimate the structural part. We call this the "structural-after-measurement" (SAM) approach to SEM. We describe a formal framework for the SAM approach under settings where the latent variables and their indicators are continuous. We review earlier SAM methods and establish how they are specific instances of the SAM framework. Decoupled estimation for the measurement and structural parts using SAM possesses three key advantages over simultaneous estimation in standard SEM. First, estimates are more robust against local model misspecifications. Second, estimation routines are less vulnerable to convergence issues in small samples. Third, estimates exhibit smaller finite sample biases under correctly specified models. We propose two variants of the SAM approach. "Local" SAM expresses the mean vector and variance–covariance matrix of the latent variables as a function of the observed summary statistics and the parameters of the measurement model. "Global" SAM holds the parameters of the measurement part fixed while estimating the parameters of the structural part. Our framework includes two-step corrected standard errors, and permits computing both local and global fit measures. Nonetheless, the SAM approach is an estimation strategy, and should not be regarded as a model-building tool. Investigating structural relations (such as causal effects) between variables is a tenet of empirical research in the social and behavioral sciences. Because these variables are often latent, they must each be measured using (typically multiple) manifest indicators or items. Statistical models used to infer the structural relations usually consist of two parts: a structural part parameterizing the relations between the latent variables and a measurement part specifying the associations between each latent variable and its indicators. Ubiquitous structural equation modeling (SEM) estimation methods traditionally fit both parts simultaneously but are susceptible to various forms of statistical biases and computational convergence issues, particularly in small samples. In this article, we formalize a framework called "structural-after-measurement" (SAM), where estimation of the measurement part precedes estimation of the structural part. We build on a long-standing idea in the literature and demonstrate how earlier methods are specific SAM implementations. As a result, SAM estimators are robust to the vulnerabilities associated with traditional SEM estimators. We implement estimation methods using SAM as part of the lavaan package in the open-source statistical computing environment R. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore whether the relationship between teachers' person-organisation (P-O) fit, job satisfaction and intention to move to another school varies depending on the way a bundle of human resource (HR) practices is configured in education. Drawing on strategic human resource management literature, a strategic and human resource orientation is considered to provide insight into the configuration of HR practices. A mixed methods design was used to collect both qualitative and quantitative data from principals and teachers in 56 Flemish primary schools. The qualitative data, which provides insight into principals' configuration of HR practices, were converted into numerical scores and analysed, together with the quantitative teacher data, using multiple group path analysis. The key findings show that the relation between P-O fit and intention to move operates differently depending on the HR orientation of the teacher's principal but not on their strategic orientation. This finding supports the need for closer attention to be given to the investment of new teachers and the impact this has on the direct and indirect effect of teachers' P-O fit on teachers' intention to move.

In the past, several assessment reports on writing repeatedly showed that elementary school students do not develop the essential writing skills to be successful in school. In this respect, prior research has pointed to the fact that cognitive and motivational challenges are at the root of the rather basic level of elementary students' writing performance. Additionally, previous research has revealed gender and achievement-level differences in elementary students' writing. In view of providing effective writing instruction for all students to overcome writing difficulties, the present study provides more in-depth insight into (a) how cognitive and motivational challenges mediate and correlate with students' writing performance and (b) whether and how these relations vary for boys and girls and for writers of different achievement levels. In the present study, 1,577 fifth- and sixth-grade students completed questionnaires regarding their writing self-efficacy, writing motivation, and writing strategies. In addition, half of the students completed two writing tests, respectively focusing on the informational or narrative text genre. Based on multiple group structural equation modeling (MG-SEM), we put forward two models: a MG-SEM model for boys and girls and a MG-SEM model for low, average, and high achievers. The results underline the importance of studying writing models for different groups of students in order to gain more refined insight into the complex interplay between motivational and cognitive challenges related to students' writing performance.

Teacher turnover is an international issue of continuing concern in education. While different antecedents of teachers' turnover intention have been identified, teachers' fit within the school (i.e. person-organisation [P-O] fit) in the context of turnover intention is still an emerging field of research. Building on the unfolding model of voluntary turnover, this study investigates the moderating role of teachers' perceived employability on teachers' P-O fit--job satisfaction-turnover intention relationship. Using survey data from 997 teachers across 74 schools, we conducted moderated mediation analysis. The analysis revealed different results for the hypothesised relationships depending on the specific dimension of turnover (i.e. intention to leave the profession or intention to move to another school). While P-O fit is directly related to the intention to move to another school, no evidence was found for a direct relation between P-O fit and the intention to leave the profession. Moreover, the results showed that the relation between P-O fit and the intention to leave/intention to move is mediated by job satisfaction. Yet, only for intention to leave, a small but significant moderating effect of perceived employability was found. These findings provide insight into the psychological processes of teachers wanting to leave the teaching profession or move to another school. Limitations, suggestions for further research and implications are also discussed in this article.

In confirmatory factor analysis (CFA), model parameters are usually estimated by iteratively minimizing the Maximum Likelihood (ML) fit function. In optimal circumstances, the ML estimator yields the desirable statistical properties of asymptotic unbiasedness, efficiency, normality, and consistency. In practice, however, real-life data tend to be far from optimal, making the algorithm prone to convergence failure, inadmissible solutions, and bias. In this study, we revisited some old, yet largely neglected non-iterative alternatives and compared their performance to more recently proposed procedures in an extensive simulation study. We conclude that closed-form expressions may serve as viable alternatives for ML, with the Multiple Group Method – the oldest method under consideration – showing favorable results across all settings. [ABSTRACT FROM AUTHOR]

In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I error rate, and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, mean square error, power, and type I error rate.

In the psychology and educational science programs at Ghent University, only 36.1% of the new incoming students in 2011 and 2012 passed all exams. Despite availability of information, many students underestimate the scientific character of social science programs. Statistics courses are a major obstacle in this matter. Not all enrolling students master the basic mathematical skills needed to pass statistics courses. Therefore, we propose a test that measures these skills. Our aim is to examine the predictive validity of the test with regard to the statistics course and also as to overall academic success. The results indicate that a test of very basic mathematics skills helps identify at-risk students at and before the start of the academic year. The practical implications of these results are discussed. The test aids the efficient use of means for remedial interventions and supports future students in choosing a higher education program that suits their potential.

A relationship between motor and mathematical skills has been shown by previous research. However, the question of whether subtypes can be differentiated within developmental coordination disorder (DCD) and/or mathematical learning disability (MLD) remains unresolved. In a sample of children with and without DCD and/or MLD, a data-driven model-based clustering was used to identify subgroups of individuals with relatively homogeneous profiles on measures associated with motor and mathematical skills. One subgroup of children with motor problems was found based on motor variables. Based on mathematical variables, two clinical clusters were found: a subtype with number fact retrieval problems and a subtype with procedural calculation problems. Clustering with motor and mathematical skills revealed two clinical clusters: a cluster with number fact retrieval as well as procedural calculation problems and below average motor and visual-motor integration skills. A second cluster of children had only procedural calculation and visual-motor problems. Our results raise questions about the usefulness of placing children who have below average mathematical skills into a single diagnostic category. Furthermore, we inform ongoing debates about the overlap between DCD and MLD, as below average motor skills were found in both MLD subgroups, although a different motor profile is linked to a different mathematical profile.