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 (2001) and the bias correcting method of Croon (2002). The bias correcting method is extended to include a reliable standard error. The four methods are compared to each other and to 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, MSE, power and type I-error rate.

Abstract. Theoretical researchers consider Structural Equation Modeling (SEM) to be the preferred method to study the relationships among latent variables. However, SEM has the disadvantage of requiring a large sample size, especially if the model is complex. Furthermore, since SEM estimates all parameters simultaneously, one misspecification in the model may influence the whole model. For these reasons, applied researchers often use a two-step Factor Score Regression (FSR) approach. In the first step, factor scores are calculated for the latent variables, which are used to perform a linear regression in the second step. However, this method results in incorrect regression coefficients. Croon (2002) developed a method that corrects for this bias. We combine this method of Croon (2002) with path analysis, resulting in Factor Score Path Analysis. This method results in correct path coefficients and has some advantages over SEM: it requires smaller sample sizes, can handle more complex models and the method is less sensitive to misspecifications, because of its stepwise nature. In conclusion, this method can be a suitable alternative for SEM, when one is dealing with a complex model and small sample sizes.

The COVID-19 pandemic is a defining global health crisis of our time. While the impact of COVID-19, including its mental health impact, is increasingly being documented, there remain important gaps regarding the specific consequences of the pandemic on particular population groups, including refugees and migrants. This study aims to uncover the impact of the COVID-19 pandemic on the mental health of refugees and migrants worldwide, disentangling the possible role of social and daily stressors, i.e., experiences of discrimination and daily living conditions. Descriptive analysis and structural equation modeling were used to analyze the responses of N = 20,742 refugees and migrants on the self-reporting global ApartTogether survey. Survey findings indicated that the mental health of refugees and migrants during the COVID-19 pandemic was significantly impacted, particularly for certain subgroups, (i.e., insecure housing situation and residence status, older respondents, and females) who reported experiencing higher levels of increased discrimination and increases in daily life stressors. There is a need to recognize the detrimental mental health impact of the COVID-19 pandemic on particular refugee and migrant groups and to develop interventions that target their unique needs.

The central aim of this study was to investigate which pupil, classroom and school level characteristics are related to primary school pupils' actual ICT competences. A sample of 378 pupils in 58 schools in Flanders (the Dutch speaking part of Belgium) completed a performance-based ICT competence test in order to measure their actual proficiency in retrieving and processing digital information, and in communicating through a computer. To gather information on the factors at each different level, questionnaires were administered to the pupils, their parents (n=378), their teachers (n=83) and the ICT coordinators (n=58) of the schools. Pupils on average have a low to medium score on the developed ICT competence test. The results of a hierarchical regression analysis with multilevel design show that the differences in ICT competences can be mainly attributed to differences in pupil level characteristics. The results indicate that especially non-ICT related pupil characteristics are associated with differences in primary school pupils' ICT competences, such as introjected regulation, controlling learning style, analytic intelligence, sex and socioeconomic status. Furthermore, the final model also indicates that parental ICT attitudes are related to primary school pupils' ICT competences. With regard to the classroom level characteristics, educational use of ICT as an information tool is significantly related to pupils' ICT competences.

While scholarly literature indicates that both refugee and non-refugee migrant young people display increased levels of psychosocial vulnerability, studies comparing the mental health of the two groups remain scarce. This study aims to further the existing evidence by examining refugee and non-refugee migrants' mental health, in relation to their migration history and resettlement conditions. The mental health of 883 refugee and 483 non-refugee migrants (mean age 15.41, range 11-24, 45.9% girls, average length of stay in the host country 3.75 years) in five European countries was studied in their relation to family separation, daily material stress and perceived discrimination in resettlement. All participants reported high levels of post-traumatic stress symptoms. Family separation predicted post-trauma and internalizing behavioral difficulties only in refugees. Daily material stress related to lower levels of overall well-being in all participants, and higher levels of internalizing and externalizing behavioral difficulties in refugees. Perceived discrimination was associated with increased levels of mental health problems for refugees and non-refugee migrants. The relationship between perceived discrimination and post-traumatic stress symptoms in non-refugee migrants, together with the high levels of post-traumatic stress symptoms in this subsample, raises important questions on the nature of trauma exposure in non-refugee migrants, as well as the ways in which experiences of discrimination may interact with other traumatic stressors in predicting mental health.