CAUSAL LANGUAGE AND STATISTICS INSTRUCTION: EVIDENCE FROM A RANDOMIZED EXPERIMENT

Best (2001) asserts that “all statistics are socially constructed.” This paper investigates three questions. (1) Is Best’s thesis true? (2) If so, should this fact be taught in introductory statistics courses? (3) If so, what general principles involving the social construction of statistics can be taught that don’t rely on detailed knowledge of a particular subject? Isaacson (2011) notes that the social construction of statistics is readily presented by asking students “Where do statistics come from?” Results of a small survey show that statistical educators are seriously divided on whether this topic should be taught. Examples of questions and principles used in teaching Statistical Literacy at Augsburg College are presented. This paper argues that the Isaacson question, “Where do statistics come from?” is fundamental to statistical education and should be at the center of any course titled “statistical literacy”. If there isn’t time to teach this material in introductory statistics courses, then statistical educators should support offering a separate statistical literacy course in order to cover this important topic.

The Education Statistics course is a basic part of arithmetic. Statistics courses are available in almost every college. Studying in Higher Education places great emphasis on the aspect of independence. In this article, researchers will analyze the learning independence of 2020 PGSD Study Program students in the Education Statistics course. The data collection technique used is random sampling with a questionnaire in the form of a Google Form. The research subjects in this article were 2020 PGSD Study Program students. The method used is a quantitative descriptive method. The results showed that the level of learning independence of PGSD Study Program students class of 2020 in the Education Statistics course was in the high category, on the six indicators of student learning independence, namely independence from others by 68.3% in the high category, having self-confidence of 54 .8% is in the fairly high category, 82.1% disciplined behavior is in the high category, 75.2% has a sense of responsibility in the high category, 66.4% self-initiated behavior is in the high category, and exercising self-control by 61.8% is in the high category.

CIRDIS is an Italian Interuniversity Research Centre for Statistical Education located at the Universities of Padova, Palermo, Perugia and “Roma la Sapienza”. The Centre, constituted on 1991, has the general objective of promoting the development of research on statistical education in Italy, especially at the undergraduate level. For this purpose the Centre: carries out research on statistical education; develops teaching materials; carries out statistical training courses for school teachers. The Centre has recently renewed its internet site (http://cirdis.stat.unipg.it) to meet teachers’ increasing demand for an effective teaching of statistics in Italian schools. The site, which has been visited since April 2003 about 8.000 times, is an environment from which teachers and students can find information on Centre’s activities and can get a valuable support to motivate and improve the learning of statistics, by accessing to a wide variety of resources and links. The site contains materials to help teaching statistics in an active way, with a multidisciplinary approach, so showing the importance for every citizen of being statistically literate. The section “Teaching/learning resources” aims at providing real datasets and teaching materials from a variety of subjects, in order to give teachers interesting examples for their statistics course. Many of the teaching resources available on the site have been developed by CIRDIS researchers.

The purpose of this study was to analyze student satisfaction with the use of the Quizizz application during midterm exams in educational statistics courses. This study uses a survey method. Data obtained from primary and secondary data sources. The population in this study were all Islamic Religious Education students in semester III at IAIN Manado for the 2021/2022 academic year. While the sample was selected using a random sampling technique, amounting to forty-two students. The instruments used were tests (UTS questions) and questionnaires to see student satisfaction. The results showed that the UTS average score was in the high category (82.15% with a standard deviation of 5.13%). For the student satisfaction questionnaire with the use of Quizizz with an index result of 4.95, it can be said that students give a good response to the use of Quizizz. Based on the results of the research that has been done, it can be concluded that Islamic Religious Education students are very satisfied using the Quizizz application in educational statistics courses.

New Undergraduate Degree in Statistics at the University of Missouri

Understanding the causes of biodiversity change is essential for addressing environmental challenges. While causal attribution has advanced in other fields, ecologists remain cautious about causal claims or misinterpret predictive models as causal. With growing spatio‐temporal data, computational power and cross‐disciplinary collaboration, discussions on improving attribution methods in ecology are gaining momentum. However, practical guidance remains limited for non‐experts. Here, we identify the challenges and decisions involved in detecting and attributing biodiversity change and provide an overview of suitable methods based on available data and specific research questions. The first challenge we address pertains to biodiversity and driver data. Unlike controlled experimental data in other disciplines, ecological data often stem from monitoring programs or field samplings with varying degrees of rigour, which complicates the analysis due to sampling biases, interacting drivers, measurement error or spatio‐temporal variations. We specifically outline how data structure (e.g. structured vs. opportunistic data) and data coverage along the spatial and temporal scale impact detection and attribution. The second challenge involves the ability to detect directional change in the system of interest, which is associated with numerous hurdles. We provide an overview of the most relevant approaches to deal with sampling variability, gaps and biases in the data, non‐linearity in the temporal trends and to identify the most appropriate spatio‐temporal resolution. For the third challenge, causal attribution, we focus on data‐driven approaches. We review recent frameworks that draw on methodologies from other disciplines, offering analytical roadmaps and step‐by‐step guidance for causal inference. These include constructing theoretical causal models a priori, full causal models based on data and theory and posterior causal interpretation tailored to specific data and research questions. Moving forward, it is essential to foster interdisciplinary collaboration to adapt and refine methodologies from other fields, ensure robust data collection and sharing practices, promote the integration of advanced computational tools and improve the link between data‐driven and theory‐driven approaches. This approach will enhance our ability to make robust causal inferences; thereby improving our understanding of biodiversity changes and informing effective conservation strategies.

Many undergraduate degrees require the completion of an introductory statistics course, but it is unclear to what extent taking a statistics course improves statistical literacy. We conducted an online survey with a simple random sample of undergraduate students at the University of Waterloo, Canada. We then compared students who have completed at least one statistics course to those who have completed none and found that taking a statistics course did not improve statistical literacy on the questions asked (causation versus correlation, margin of error, and others). Introductory statistics courses may want to address statistical literacy as a learning outcome more explicitly for a better understanding of public policies.

Many postgraduate students face difficulties in learning the educational statistics topics because of their mathematical nature. Adopting innovative learning technologies has opened up a wide range of options to extend the learning strategies in higher education and support students` learning outcomes. The current study aimed to assess the effectiveness of a blended learning BL approach with 18 Master’s Degree students enrolled in two postgraduate courses in statistics i.e. (introduction to educational statistics and quantitative research data analysis courses) taught at the Arabian Gulf University Distance Teaching & Training Master Program in Bahrain. The courses were enrolled as Blended Learning with the College of Graduate Studies-CGS Moodle Learning Management System during the second semester of the academic year 20182019/. The students were assessed using online quizzes, 2 written tests, two assignments: one in descriptive and the other in inferential statistics, one open book exam and a final examination. Data analysis revealed that the blended learning approach helped the students to learn the course content i.e. (students` grades ranged from B+ to A). The learning satisfaction survey administered at the end of the course revealed a high degree of satisfaction with the course material and the teaching approach used. In conclusion, blended learning is viewed as feasible for teaching statistics courses and is beneficial to both students and instructors.

Research in statistics education and measurement suggest the use of quality instruments with good psychometric characteristics to measure students’ learning outcomes. However, very few quality instruments have been developed, evaluated, and are available to researchers to measure students’ statistical knowledge. This paper reports the development of the Goals and Outcomes Associated with Learning Statistics (GOALS) instrument, which was designed to measure statistical reasoning learning outcomes in a first course in statistics.

The purpose of the study was to evaluate the effects of feedback timing and quality on student self-regulatory behavior. Using a non-equivalent control-group design, students were assigned to either an immediate-detailed or delayed-grade-only feedback condition within the online statistics homework program, Aplia. The groups were then compared on levels of self-regulation and ratings of perceived homework effectiveness. Results indicate no significant differences between groups. Implications and the need for updated and more context specific measurements are discussed. Keywords: online homework; self-regulation; Aplia; graduate student; statistics course

This paper describes a unique graduate-level course that prepares teachers of introductory statistics at the college and high school levels. The course was developed as part of a graduate degree program in statistics education. Although originally taught in a face-to-face setting, the class has been converted to an online course to be accessible to more students. The course serves students who are pursuing graduate degrees in a variety of disciplines but who want to teach statistics as part of their careers. It also serves current teachers in high school who are teaching the Advanced Placement Statistics course as well as teachers at two-year and four-year colleges. The curriculum for the course is based on the theory that good teachers of statistics need to be developed, as opposed to being trained. Building on recent teacher preparation theory, we describe a course that models and builds specific knowledge about teaching and learning statistics. In addition, this course is organized around the six recommendations of the ASA-endorsed Guidelines for Assessment and Instruction in Statistics Education (GAISE).

Background and Purpose. The literature on teaching statistics at the university level provides support for the use of tools such as graphic organizers to help students understand the often confusing and sometimes complex process of selecting the appropriate statistical test for a given research question or design. This is the first of 2 reports that present several graphic organizers (also known as decision trees or flow charts) as decision-making guides for students to aid in the selection of appropriate statistical tests representative of those commonly used in physical therapy. Method/Model Description and Evaluation. To use the graphic organizers, the student learns 4 fundamental statistical concepts and then follows a decision-making process that leads to an appropriate statistical analysis. A prospective 2-group, reverse-design study on students who were familiar with the statistical textbook but not with the graphical organizer showed significant differences between the graphical organizer and textbook in both the time taken to select an appropriate statistical test and student preference. The graphic organizer led to correct statistical selection 3 times more often than the textbook. Outcomes. For 4 years, we have used the graphic organizer in the curriculum for students enrolled in a Master of Physical Therapy (MPT) program, receiving excellent student feedback. Discussion and Conclusion. The graphic organizers presented in this publication can be used to simplify the decision-making process for professional (entry-level) or postprofessional physical therapist students in choosing a statistical analysis for research questions that investigate group differences. The graphic organizers are convenient to use and can provide a visual organization of the decision-making process while highlighting the concepts discussed in a traditional statistics course. Key Words: Graphic organizer, Statistics, Research design, Group differences. BACKGROUND AND PURPOSE This is the first of 2 reports that presents a decision-making guide for the appropriate matching of statistical tests to research designs. The article presents several graphic organizers (also known as decision trees or flow charts) that focus on research designs and statistical approaches representative of those commonly used in physical therapy. We based the content of the graphic organizers on a compilation of statistical analyses reported in Physical Therapy and the Journal of Orthopedic and Sports Physical Therapy from January 2001 through June 2002. This analysis showed that approximately 65% of articles examined group differences, while approximately 35% were correlation/ regression designs (Table 1). This paper will focus on statistics for the former. The literature on teaching statistics at the university level provides support for the use of tools such as graphic organizers to help students understand the often confusing and sometimes complex process of selecting the appropriate statistical test for a given research question or design. As suggested by Pan and Tang,1 the use of a statistics cheat sheet can decrease graduate students' statistics anxiety without damaging the learning process, since the students must know the concepts in order to use the information. Additionally, an illustration that is clear, self-contained, easily grasped, and provokes reflection may enhance the Aha! experience of the statistics student.2 Several authors3-6 have noted that even though students can learn how to use statistical analysis, they often do not know how to apply the analysis to research scenarios. Statistical tests are commonly taught 1 test at a time without selection skills;7 the integration of selection skills could enhance the practical application of that knowledge.3-6,8 Manual computational procedures may not be as necessary to learn with the availability of computer programs, but instructors still need to help students develop skills necessary to select the appropriate statistical test. …

Statistics instructors and others interested in the teaching of statistics will find many print and nonprint resources on this topic. The print literature on the teaching of statistics is largely anecdotal and comprises mainly recommendations for instruction based on the experiences and intuitions of individual instructors. Less than 30% of the print literature reports the results of empirical studies, but these cover a broad range of topics, including the use of computers in statistics instruction, teaching materials, and teaching strategies. A large portion of the nonempirical literature is devoted to descriptions of statistics courses and specific lessons that, though untested, still provide a resource for instruction. Recently numerous nonprint (electronic) resources for instruction, problem solving, and discussions about statistics instruction have also become available. These include many data sets and other instructional resources, statistics discussion groups, and the electronic Journal of Statistics Education.

Functional roles and cultural modulations of the medial prefrontal and parietal activity associated with causal attribution

In causal inference, a randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive costs, so an efficient experimental design is necessary. We propose ABC3, a Bayesian active learning policy for causal inference. We show a policy minimizing an estimation error on conditional average treatment effect is equivalent to minimizing an integrated posterior variance, similar to Cohn criteria. We theoretically prove ABC3 also minimizes an imbalance between the treatment and control groups and the type 1 error probability. Imbalance-minimizing characteristic is especially notable as several works have emphasized the importance of achieving balance. Through extensive experiments on real-world data sets, ABC3 achieves the highest efficiency, while empirically showing the theoretical results hold.