School Improvement in a neo‐liberal world

This paper uses the concept of paradigm to examine the current state of School Improvement and its relationship to quantitative School Effectiveness studies. It argues that a significant and explicit paradigm change occurred in the early 1990s, resulting in a managerialist approach; a reductionist use of the concept culture; and an inadequate response to the challenges of poverty and under‐achievement. Some arguments are presented to show how this paradigm relates to a broader neo‐liberal context, and an alternative paradigm is outlined.     

The role of school psychologists in the assessment,prevention, and intervention of cyberbullying

Many school‐aged children have experienced the cruelty of bullying. The impact and effects of bullying have been described extensively in scholarly literature. Unfortunately, the impact of bullying has also made contemporary headlines with the recent rash of school shootings and other forms of school‐based violence. The current body of students brings an additional threat to bullying. Due to their sophisticated use of technology, today's students are using contemporary delivery systems to taunt, tease, and threaten their classmates. Cyberbullying is the newest form of bullying, and it brings additional challenges for school personnel. Because of the unique aspects of cyberbullying, school psychologists need strategies to guide school communities faced with this threatening behavior. © 2008 Wiley Periodicals, Inc.     

Assessment matters: In search of the wizard of assessment

At first, the shift in measurement of educational success from bodies in seats to learning outcomes sent student affairs educators at California State University, Northridge, into a panic. Persistence, support, and a clear framework for understanding learning led to the discovery that there was a little bit of assessment wizard in all of them.     

Integrating ICT in the classroom: a case study of two contrasting lessons

A detailed analysis of two contrasting primary school lessons, where ICT plays a dominant role, reveals how it is the teacher's implicit understanding of the principles of teaching and learning for understanding which determines whether the pedagogical potential of the technology is fully realised and the desired lesson outcomes achieved.     

Influences on teacher education student attitudes toward youth who are HIV+

The expectations, attributions, and reactions toward children and youth who have contracted HIV/AIDS warrant close attention from parents, teachers, related service personnel, and others who will interact with these students in the schools. Those who are misinformed or unaware of their beliefs may inadvertently discriminate against children and youth with the disease. This study examined factors that influence judgments about children and youth with HIV/AIDS in a sample of teacher education students. Course of infection of HIV accounted for significant differences in the participants' judgments about responsibility and blame for contracting the disease and also fearfulness and attitudes reflecting isolation for these youth. Educational professionals should be aware of the many negative attributions placed on adolescents who are diagnosed as HIV⁺ and be prepared to act as facilitators when designing appropriate interventions to assist these students. © 2004 Wiley Periodicals, Inc. Psychol Schs 41: 211–219, 2004.     

Revisiting the Model Size Effect in Structural Equation Modeling

Fitting a large structural equation modeling (SEM) model with moderate to small sample sizes results in an inflated Type I error rate for the likelihood ratio test statistic under the chi-square reference distribution, known as the model size effect. In this article, we show that the number of observed variables (p) and the number of free parameters (q) have unique effects on the Type I error rate of the likelihood ratio test statistic. In addition, the effects of p and q cannot be fully explained using degrees of freedom (df). We also evaluated the performance of 4 correctional methods for the model size effect, including Bartlett’s (1950), Swain’s (1975), and Yuan’s (2005) corrected statistics, and Yuan, Tian, and Yanagihara’s (2015) empirically corrected statistic. We found that Yuan et al.’s (2015) empirically corrected statistic generally yields the best performance in controlling the Type I error rate when fitting large SEM models.