Students’ Perceptions of Grades and Grade Inflation in Counselor Training

Previous scholars have suggested grade inflation has been in higher education for decades, may devalue high grade point averages, and blur important differences between qualified and unqualified job candidates. In counselor training programs, grade inflation may cause students to overestimate their abilities to handle challenging real-world situations, impede faculty evaluation practices, and promote unfavorable student learning environments. In this exploratory study, we surveyed 240 counseling students on their perceptions of their grades and their peers’ grades before and during the COVID-19 pandemic. Our results suggest that students perceived their peers succeeded academically despite inappropriate and unethical behaviors, especially during the COVID-19 pandemic. Counselor educators should consider how grade inflation might impact student learning and training, and program alignment with CACREP standards

Differentiating the EdD and the PhD in Higher Education: A Survey of Characteristics and Trends

Purpose: Higher education, as a field of study, is one of the few programmatic areas that offer two doctoral degrees: The Doctor of Education (EdD) and the Doctor of Philosophy (PhD). In the United States, the two degrees are often conflated. Conversations, to this point, have done more to contribute to the theoretical debate than to operationally distinguish between the two degree paths. Method: The current study analyzed data collected from a review of the 188 doctoral programs at 145 institutions listed with the Association for the Study of Higher Education (ASHE). Results: Results suggest that while there is a shift toward operational differences between the EdD and the PhD in higher education, the similarities in entrance requirements, formats, and research expectations, to list a few, suggest that the EdD and PhD still require further refinement to reach the theoretical clarity common in many conversations about the education doctorate

No le tema a los datos perdidos: enfoques modernos para el manejo de datos perdidos

La mayoría de los datos en ciencias sociales y educación presentan valores perdidos debido al abandono del estudio o la ausencia de respuesta. Los métodos para el manejo de datos perdidos han mejorado gramáticamente en los últimos años, y los programas computacionales ofrecen en la actualidad una variedad de opciones sofisticadas. A pesar de la amplia disponibilidad de métodos considerablemente justificados, muchos investigadores e investigadoras siguen confiando en técnicas viejas de imputación que pueden crear análisis sesgados. Este artículo presenta una introducción conceptual a los patrones de datos perdidos. Seguidamente, se introduce el manejo de datos perdidos y el análisis de los mismos con base en los mecanismos modernos del método de máxima verosimilitud con información completa (FIML, siglas en inglés) y la imputación múltiple (IM). Asimismo, se incluye una introducción a los diseños de datos perdidos así como nuevas herramientas computacionales tales como la función Quark y el paquete semTools. Se espera que este artículo incentive el uso de métodos modernos para el análisis de los datos perdid

On the Measurement of Preservice Teacher Commitment: Examining the Relationship Between Four Operational Definitions and Self-Efficacy Beliefs

Using a sample of 287 preservice teachers from a large Midwestern university in the United States, this study examined the predictive relationships between four unique measures of commitment and a commonly used measure of teacher self-efficacy, as frequently employed in teacher commitment research. Differential response patterns and predictive relationships were examined with multiple-group structural equation models to compare results of decisions about instrumentation. Results suggest that while there were no differential response patterns to the instruments, the predictive relationships significantly differed as a function of group identification. Suggestions for measuring and interpreting the relationships shared with commitment are further discussed

Effects of Self-Efficacy, Emotional Intelligence, and Perceptions of Future Work Environment on Preservice Teacher Commitment

This study was designed to examine the effects of self-efficacy, expectations of future work environment, and emotional intelligence on preservice teacher commitment to the teaching profession on a sample of 209 preservice teachers. The purpose of the study was to add to the existing knowledge surrounding preservice teacher commitment and promote new ways to approach teacher education. Using correlation and regression analyses, we found that preservice teacher self-efficacy expectations surrounding classroom management, instructional strategies, and student engagement were positively correlated with commitment. Preservice teacher expectations of future work environment influenced their satisfaction with the profession, which was also positively correlated with their commitment to enter the profession. Preservice teachers with greater emotional awareness and pro-motivational thinking also demonstrated higher levels of commitment. Copyright Taylor & Francis

No le tema a los datos perdidos: enfoques modernos para el manejo de datos perdidos

Most of the social and educational data have missing observations due to either attrition or nonresponse.Missing data methodology has improved dramatically in recent years, and popular computer programs as well as software now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, many researchers still rely on old imputation techniques that can create biased analysis. This article provides conceptual introductions to the patterns of missing data. In line with that, this article introduces how to handle and analyze the missing information based on modern mechanisms of full-information maximum likelihood (FIML) and multiple imputation (MI). An introduction about planned missing designs is also included and new computational tools like Quark function, and semTools package are also mentioned. The authors hope that this paper encourages researchers to implement modern methods for analyzing missing data.La mayoría de los datos en ciencias sociales y educación presentan valores perdidos debido al abandono del estudio o la ausencia de respuesta. Los métodos para el manejo de datos perdidos han mejorado gramáticamente en los últimos años, y los programas computacionales ofrecen en la actualidad una variedad de opciones sofisticadas. A pesar de la amplia disponibilidad de métodos considerablemente justificados, muchos investigadores e investigadoras siguen confiando en técnicas viejas de imputación que pueden crear análisis sesgados. Este artículo presenta una introducción conceptual a los patrones de datos perdidos. Seguidamente, se introduce el manejo de datos perdidos y el análisis de los mismos con base en los mecanismos modernos del método de máxima verosimilitud con información completa (FIML, siglas en inglés) y la imputación múltiple (IM). Asimismo, se incluye una introducción a los diseños de datos perdidos así como nuevas herramientas computacionales tales como la función Quark y el paquete semTools. Se espera que este artículo incentive el uso de métodos modernos para el análisis de los datos perdid

Relationships Between Positive Parenting, Overparenting, Grit, and Academic Success

Grit is a trait that describes the drive to succeed above and beyond natural intelligence. While grit has been associated with positive academic outcomes, few researchers have investigated the predictors of grit, such as parenting. We examined the influence of parenting on grit and academic success in 226 college students. Results indicated that overparenting and parental acceptance and involvement were significantly related to grit and grit was found to have a significant predictive relationship with academic success. Further, grit mediated the relationship between overparenting and academic success

Examining the Relationship Between the Research Training Environment, Course Experiences, and Graduate Students\u27 Research Self-Efficacy Beliefs

This study examined the relationship between graduate students\u27 research training environment, course experience, and research self-efficacy beliefs. The findings of the descriptive and regression analyses suggest that graduate students\u27 (n = 161) general research, quantitative, and qualitative research self-efficacy beliefs varied and that these beliefs were related to different aspects of the research training environment and course experiences, including their own personal research experiences. While course experience variables were significant predictors of quantitative and qualitative research self-efficacy, they were not predictive of general research methods self-efficacy. Also, while mentorship was a significant predictor of general research methods self-efficacy, it was not a significant predictor of quantitative and qualitative research self-efficacy. The implications of this study for research and graduate education are discussed