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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 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.

Student motivation as well as student perception of interpersonal teacher behaviour are linked to the sense of wellbeing at student level. However, while most of the variance in the measurement of student wellbeing was situated at student level, eleven percent of variance was found at classroom level. In this article we focus on this variance at classroom level and the moderating role that student perception of interpersonal teacher behaviour has. From an interpersonal perspective on teaching, the relationship between teacher wellbeing, perceptions of interpersonal teacher behaviour, and student wellbeing is examined. Grade 9 students of technical and vocational training schools are participating in this study. In the analyses a distinction is made between teaching academic subjects and teaching vocational subjects. There appears to be a direct link between the wellbeing of the teachers of academic subjects and the wellbeing of their students. Students who perceive their academic teacher as leading, helpful and friendly score higher on wellbeing, while wellbeing decreases when an academic teacher is perceived as strict and admonishing. The relationship between the teacher of vocational subjects who typifies himself as strict and admonishing, and the wellbeing of his students, is moderated by student perceptions of teacher interpersonal behaviour. A direct relationship between the wellbeing of the vocational teacher and the wellbeing of students is not found. Only when the vocational teacher's wellbeing is high and student perceptions of uncertain or dissatisfied interpersonal teacher behaviour is low, does student wellbeing increase. We conclude that for vocational subjects, student perceptions of interpersonal teacher behaviour are crucial moderators. Finally, students who are highly motivated to learn practical subjects, have a higher score on student wellbeing. By contrast, the fact that education is inherently obligatory has a negative influence on student wellbeing.

Byline: Karen Petegem (1), Antonia Aelterman (1), Yves Rosseel (2), Bert Creemers (3) Keywords: classroom climate; interpersonal teacher behaviour; student perceptions; student wellbeing; teacher perceptions; teacher wellbeing Abstract: Student motivation as well as student perception of interpersonal teacher behaviour are linked to the sense of wellbeing at student level. However, while most of the variance in the measurement of student wellbeing was situated at student level, eleven percent of variance was found at classroom level. In this article we focus on this variance at classroom level and the moderating role that student perception of interpersonal teacher behaviour has. From an interpersonal perspective on teaching, the relationship between teacher wellbeing, perceptions of interpersonal teacher behaviour, and student wellbeing is examined. Grade 9 students of technical and vocational training schools are participating in this study. In the analyses a distinction is made between teaching academic subjects and teaching vocational subjects. There appears to be a direct link between the wellbeing of the teachers of academic subjects and the wellbeing of their students. Students who perceive their academic teacher as leading, helpful and friendly score higher on wellbeing, while wellbeing decreases when an academic teacher is perceived as strict and admonishing.The relationship between the teacher of vocational subjects who typifies himself as strict and admonishing, and the wellbeing of his students, is moderated by student perceptions of teacher interpersonal behaviour. A direct relationship between the wellbeing of the vocational teacher and the wellbeing of students is not found. Only when the vocational teacher's wellbeing is high and student perceptions of uncertain or dissatisfied interpersonal teacher behaviour is low, does student wellbeing increase. We conclude that for vocational subjects, student perceptions of interpersonal teacher behaviour are crucial moderators. Finally, students who are highly motivated to learn practical subjects, have a higher score on student wellbeing. By contrast, the fact that education is inherently obligatory has a negative influence on student wellbeing. Author Affiliation: (1) Department of Educational Sciences, Ghent University, H. Dunantlaan 2, Gent, 9000, Belgium (2) Data Analysis, Ghent University, H. Dunantlaan 1, Gent, 9000, Belgium (3) Education and Sociology, University of Groningen, Grote Kruisstraat 2/1, Groningen, 9712 TS, The Netherlands Article History: Registration Date: 29/08/2006 Received Date: 07/07/2006 Accepted Date: 28/08/2006 Online Date: 11/10/2006