Single case experimental designs and their statistics

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Assessment programs and their components : a network approach

Exams and other assessments in health science education are not random events; rather, they are part of a bigger assessment program that is constructively aligned with the intended learning outcomes at different stages of a health science curriculum. Depending on topical and temporal distance, assessments in the program are correlated with each other to a more or lesser extent. Although correlation does not equate causation, once we come to understand the correlational structure of an assessment program, we can use that information to make predictions of future performance, to consider early intervention for students who are otherwise likely to drop out, and to inform revisions in either assessment or teaching. This article demonstrates how the correlational structure of an assessment program can be represented in terms of a network, in which the assessments constitute our nodes and the degree of connectedness between any two nodes can be represented as a thicker or thinner line connecting these two nodes, depending on whether the correlation between the two assessments at hand is stronger or weaker. Implications for educational practice and further research are discussed

In God We Trust, All Others Bring Data: A Bayesian Approach to Standard Setting

Standard setting is an inherent part of pass/fail decisions in assessment. Although various standard setting methods are available, they all have their limitations and no method provides a golden solution to all our standard setting headaches. Some methods require potentially labor-intensive standard setting panels of judges who have specific knowledge. Other methods require student cohorts of ‘sufficient’ size. However, small cohorts are quite prevalent in medical programs across the globe, and standard setting panels are not always feasible due to logistic or financial constraints or may result in inadequate judgments due to bias or a lack of specific knowledge. This manuscript presents a new standard setting method, which is based on the Bayesian principle of updating our knowledge or beliefs about a phenomenon of interest with incoming data, uses information that is not considered in methods already available and can be applied to both small and larger cohorts regardless of whether standard setting panels are available. As demonstrated in this manuscript through a worked example, the new method is easy to implement and requires only a minimum of calculations which can be done in zero-cost, user-friendly Open Source software. Options for future research comparing different standard setting methods are discussed

MS

thesisThe purpose of this study was to determine whether or not a relationship exists between the uterine placental site and the birth weight of infants delivered from a low risk population and to establish the Doppler as a reliable instrument for placental localization before delivery. The sample consisted of 26 Caucasian primiparous and multiparous women who were delivering a full-term infant by elective Cesarian section. Twenty-one of the 26 infants were considered for statistical analysis. Sixteen women were examined for location of the placenta with the Doppler before delivery. There was no significant relationship between placental site, segment and wall and infant birth weight. However, a significant correlation was found between placental site and birth length, placental weight, infant's gestational age, and maternal gravida. A comparison was made between placental site and the mean value for maternal gravida and months since last pregnancy, infant's gestational age on Dubowitz assessment, placental weight, birth weight, length, and occipital frontal circumference (OFC). It was noted that the greatest birth weight for placental site was associated with an anterior/ posterior fundal position and the second ranking birth weight with a placental site in the upper uterine segment on the anterior/posterior wall. The agreement between the placenta localization with the Doppler before delivery and manual removal varied considerably. When the placenta was located in the fundus on the anterior wall, 100% accuracy was achieved, 70% with an upper uterine segment posterior wall placental site, 50% with a posterior fundal site, 30% with an anterior upper uterine segment site, and 0% accuracy with an anterior lower uterine segment site

Revisiting the quantitative–qualitative-mixed methods labels : Research questions, developments, and the need for replication

The goal of science is to establish laws and principles that can help us explain phenomena in our world and universe in a systematic manner and, in many cases, how we may be able to predict and/or influence these phenomena. In this endeavour, qualitative and quantitative research methods can provide us with useful tools. However, these methods have been assigned several unconstructive labels that, although perceived as useful anchors, can result in ill-founded choices of methods used in a study. This article discusses several of these frequently encountered labels and argues that they may contribute to a continued quantitative–qualitative divide, as we have witnessed in the field of medical education, but are not constructive for the practice of science. To establish laws and principles, we need well-designed scientific studies and replications of these studies. Regardless of which methods we use, to enable replication, we need to document all choices and decisions made throughout a study

THE SUBJECT OF STATISTICS IN NATURAL SCIENCE CURRICULA: A CASE STUDY

Statistics is considered to be an indispensable part of a wide range of curricula across the globe, natural science curricula included. Teachers and curriculum developers are typically confronted with four questions with regard to the role and position of statistics in a curriculum: (1) how to integrate statistics in the curriculum; (2) which topics to cover and in what detail; (3) how much time to allocate to statistics in a curriculum; and (4) how to organize a course and which study materials to select. This paper addresses these four questions through a case study: four curricula at Charles University, Prague, Czech Republic, are compared in terms of how they address these four questions. Placing this comparison in a framework of cognitive load theory and two decades of research inspired by this theory, this paper concludes with a number of guidelines for addressing the aforementioned four questions when designing a curriculum

How Similar Are the Disorders Included Under the Umbrella of Obsessive-Compulsive Disorder and Related Disorders?-Reply

Wellcome Trus

The bridge between design and analysis

The overall purpose of the ‘Statistical Points and Pitfalls’ series is to help readers and researchers alike increase awareness of how to use statistics and why/how we fall into inappropriate choices or interpretations. We hope to help readers understand common misconceptions and give clear guidance on how to avoid common pitfalls by offering simple tips to improve your reporting of quantitative research findings. Each entry discusses a commonly encountered inappropriate practice and alternatives from a pragmatic perspective with minimal mathematics involved. We encourage readers to share comments on or suggestions for this section on Twitter, using the hashtag: #mededstats

Revisiting cognitive load theory : second thoughts and unaddressed questions

In cognitive load theory (CLT), learning is the development of cognitive schemas in a long-term memory with no known limits and can happen only if our limited working memory can process new information presented and the amount of information that does not contribute to learning is low. According to this theory, learning is optimal when instructional support is decreased going from worked examples via completion problem to autonomous problem solving and learners do not benefit from practicing retrieval with complex content. However, studies on productive failure and retrieval practice have provided clear evidence against these two guidelines. In this article, issues with CLT and research inspired by this theory, which remain largely ignored among cognitive load theorists but have likely contributed to these contradictory findings, are discussed. This article concludes that these issues should make us question the usefulness of CLT in health science education, medical education and other complex domains, and presents recommendations for both educational practice and future research on the matter