Search

Linguistic landscape studies (LLS) involve analyzing public signs in specific areas (e.g., streets, entire cities) to reveal the socio-cultural and sociolinguistic structures present. Turkey has been influenced by various cultures, particularly following the refugee influx starting in 2011 and recent internationalization efforts. Such social phenomenon's linguistic repercussions on multiculturalism, linguistic diversity, and vitality is an underexplored area. Therefore, this study explores the linguistic landscape of Turkey's capital, Ankara, to understand the extent of multilingualism in public spaces. Over a year, 1,291 signs were photographed across central areas of Ankara. Researchers were trained to ensure data collection, sorting, and analysis consistency. The study employed descriptive statistics with a quantitative approach to analyze language choice and prominence. Findings revealed that the linguistic landscape of Ankara was mainly Turkish monolingual and English-Turkish bilingual. The study's implications are mainly for language teacher educators,so they embed linguistically aware language teacher education models, and for language education policymakers, they incorporate authentic, multimodal, and multilingual LL to English as a foreign language education.

Higher education professionals often are tasked with providing evidence to stakeholders that programs, services, and practices implemented on their campuses contribute to student success. Furthermore, in the absence of a solid base of evidence related to effective practices, higher education researchers and practitioners are left questioning what works--and what does not--on college campuses. The goal of this essay is to advance a conversation about evidence within the higher education literature through an illustration of a nonrandom research study designed to evaluate the impact of a national interfaith organization on institutions' efforts to promote interfaith learning and development. We briefly provide an overview of the relationship between evidence and evaluation efforts, then move to illustrating one project as a vehicle for understanding how evidence emerging from a nonrandom evaluation design can be done effectively in a national study related to one dimension of student success, and finally, offer support for the essay's central thesis--"that evaluation designs can present reliable evidence of impact in the absence of randomization."

In this research, multistage adaptive tests (MST) were compared according to sample size, panel pattern and module length for top-down and bottom-up test assembly methods. Within the scope of the research, data from PISA 2015 were used and simulation studies were conducted according to the parameters estimated from these data. Analysis results for each condition were compared in terms of mean RMSE and bias. According to the results obtained from the MST simulation based on the top-down test assembly method, mean RMSE values reduced when the module length increased and when the panel pattern changed from 1-2 to 1-2-2 and 1-2-3 for MST applied to small and large samples. Within the scope of the research, data from PISA 2015 were used and simulation studies were conducted using the parameters estimated from these data. Analysis results for each condition were compared in terms of mean RMSE and bias.

This research aimed to determine the trends related to blended learning studies conducted in science education through descriptive content analysis. This study was performed using the document review method. For this purpose, 120 studies on blended learning in science education were determined between 2005 and 2022 in the Web of Science (WoS) database. The studies were examined by considering themes such as keywords, country, purpose, method, sample, online system, and result. The research findings showed that more studies occurred between 2020 and 2022. The findings showed that studies mostly use quantitative research methods and experimental designs. Most of the studies were conducted with undergraduate-level sample groups. Findings revealed that the blended learning environment is designed more to teach physics subjects, and parametric tests were generally used for data analysis. The more preferred course management systems in the studies were the online systems designed by the schools. A considerable number of studies revealed that blended learning environments designed for science education have a positive effect on variables such as achievement, skill, and motivation.

When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable inferences about the target population. We describe a nested trial design where the randomized clusters are embedded within a cohort of trial-eligible clusters from the target population and where clusters are selected for inclusion in the trial with known sampling probabilities that may depend on cluster characteristics (e.g., allowing clusters to be chosen to facilitate trial conduct or to examine hypotheses related to their characteristics). We develop and evaluate methods for analyzing data from this design to generalize causal inferences to the target population underlying the cohort. We present identification and estimation results for the expectation of the average potential outcome and for the average treatment effect, in the entire target population of clusters and in its non-randomized subset. In simulation studies, we show that all the estimators have low bias but markedly different precision. Cluster randomized trials where clusters are selected for inclusion with known sampling probabilities that depend on cluster characteristics, combined with efficient estimation methods, can precisely quantify treatment effects in the target population, while addressing objectives of trial conduct that require oversampling clusters on the basis of their characteristics.

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta-analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardized for age and sex. We develop three methods for mapping between aggregate BMI data using known or estimated relationships between measurements on different scales at the individual level. The first is an analytical method based on the mathematical definitions of z-scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual-level relationships. We verify and contrast our methods using simulation studies and trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non-convergence.

Building on previous work on the civic returns of adult learning, this article examines the association between adult education, personality traits, and demands for civic participation or volunteering. Based on National Education Panel Study data, the study finds openness to be a crucial personality trait for participating in further training, as well as for civic participation, as it relates to competence and self-confidence in dealing with new demands. Extraversion is required for social interaction and communication with other participants. The study also shows that participants in further private training engage more at the management level of volunteering, where demands such as leadership and organizational talent are emphasized. The findings consider sociological factors such as the role of peers.

Not knowing the population size is a common problem in data-limited contexts. Drawing on work in Sierra Leone, this short take outlines a four-step solution to this problem: (1) estimate the population size using expert interviews; (2) verify estimates using interviews with participants sampled; (3) triangulate using secondary data; and (4) reconfirm using focus group discussions.

Emergent technologies present platforms for educational researchers to conduct randomized controlled trials (RCTs) and collect rich data to study students' performance, behavior, learning processes, and outcomes in authentic learning environments. As educational research increasingly uses methods and data collection from such platforms, it is necessary to consider the most appropriate ways to analyze this data to draw causal inferences from RCTs. Here, we examine whether and how analysis results are impacted by accounting for multilevel variance in samples from RCTs with student-level randomization within one platform. We propose and demonstrate a method that leverages auxiliary non-experimental "remnant" data collected within a learning platform to inform analysis decisions. Specifically, we compare five commonly-applied analysis methods to estimate treatment effects while accounting for, or ignoring, class-level factors and observed measures of confidence and accuracy to identify best practices under real-world conditions. We find that methods that account for groups as either fixed effects or random effects consistently outperform those that ignore group-level factors, even though randomization was applied at the student level. However, we found no meaningful differences between the use of fixed or random effects as a means to account for groups. We conclude that analyses of online experiments should account for the naturally-nested structure of students within classes, despite the notion that student-level randomization may alleviate group-level differences. Further, we demonstrate how to use remnant data to identify appropriate methods for analyzing experiments. These findings provide practical guidelines for researchers conducting RCTs in similar educational technologies to make more informed decisions when approaching analyses.

Purpose: This study describes participant diversity in Williams syndrome (WS) intervention studies. Methods: A literature search was conducted to identify prospective treatment studies including participants with WS. Data was extracted on the reporting of and information provided on age, sex, cognitive ability, socioeconomic status, race, and ethnicity. Results: Eleven eligible articles were identified. Reporting rates of demographic factors varied considerably, with the highest rates for age and sex (100%) and the lowest reporting rates for race (18%) and ethnicity (9%). Combining demographic data from the two studies that reported on race and/or ethnicity (n = 33), 88% of participants were White. The combined participant mean age was 20.9 years. Conclusion: There is a low frequency of reporting on several demographic factors including socioeconomic status, race, and ethnicity in WS intervention studies. There is a need for increased representation of racial and ethnic minority groups, older participants, and more cognitively impaired patients in WS research.

Sampling distributions are fundamental for statistical inference, yet their abstract nature poses challenges for students. This research investigates the development of high school students' conceptions of sampling distribution through informal significance tests with the aid of digital technology. The study focuses on how technological tools contribute to forming these conceptions, guided by an emerging theory that describes this process. A workshop for high school students was organized, involving 36 participants working in pairs across four sessions, each with access to a computer. These sessions involved problem-solving activities, with the teacher introducing key concepts in the initial three sessions. The analysis, employing grounded theory, aimed to characterize the nature of students' conceptions of sampling distribution as evident in their responses. The findings reveal a transition from empirical to informal conceptions of sampling distribution among students, facilitated by computational mediation. This transition is marked by an abstraction process that includes mathematization, processing, uncertainty/randomness, and conditional reasoning. The study underscores the role of digital simulations in teaching statistical concepts, facilitating students' conceptual shift critical for grasping statistical inference.

Educational robots have unrivaled advantages and value in developing students' computational thinking. Currently, there are fewer studies on the overall effects of educational robots on K12 students' computational thinking, especially at the instructional style and cross-grade level. In order to investigate the overall effect of educational robots on students' computational thinking, the study used a meta-analytic approach to analyze 27 experimental and quasi-experimental research papers from the last decade. It was found that: educational robots have a positive and significant effect on K12 students' computational thinking development (SMD = 0.558), and their application effects are influenced by moderating variables such as school level, teaching style, participation style, experimental period, and sample size. Specifically: educational robots have a positive and significant effect on K12 students, with the most significant effect at the early childhood level; there is a significant difference in the development of students' computational thinking when using educational robots in different teaching modes, with the best effect being achieved in the design-based teaching mode; (3) participating in educational robots in small groups is more likely to promote the development of computational thinking in K12 students; (4) Different experimental cycles have a positive effect on students' computational thinking, but a long experimental cycle does not mean a good effect, etc.; (5) There is a significant difference between different sample sizes of educational robots on K12 students' computational thinking, and arranging a medium-sized (50-100 students) educational robotics activity can achieve a better learning effect.

This study investigates a range of non-normative ideas that pre-service teachers (PSTs) employ in reasoning about sampling variability. This issue was studied in the context of a content course on statistics and probability for pre-service middle grade teachers at a Midwestern American university. Analysis of seven PSTs' video and screen records of task-based interviews has articulated fundamental facets of sampling variability that have not yet been fully explicated in the literature, especially with middle grade PSTs. With the content expectation of sampling variability for middle grade students as suggested by policy reports in the United States of America, this study is particularly fertile ground for designing curricula that can support middle grade PSTs' development of critical specialized content knowledge on sampling variability.

Fetters et al.'s (2013) mixed methods integration framework uses construction metaphors: building, connecting, merging, and embedding. In a similar vein, this article uses an architectural metaphor and introduces design patterns as building blocks for mixed methods research design. A design pattern embeds one specific design decision into its preceding design decisions. As an example, I will describe a design pattern for mixed methods matched comparisons sampling. Using one exemplar study, I will demonstrate the pattern's three design phases. Embedding matched comparisons sampling into its preceding design decisions shows that, to optimize the comparison of subpopulations in the qualitative research strand, the quantitative sample of this design pattern should aim to include almost all members of one small target population.

Underrepresentation of socioeconomically, culturally, and/or linguistically diverse (SCLD) children with neurodevelopmental disorders (NDD) and their families has become a focal point for researchers. This systematic review aimed to identify researchers' strategies for recruiting and retaining SCLD families of children with NDD, published between 1993 and 2018. One hundred twenty-six articles were included, and study samples were categorized as "High SCLD" and "Low SCLD". Chi-square tests of independence were used to determine associations between sample composition (i.e., High/Low SCLD sample) and study characteristics reported. Significant associations were found between sample composition and studies that explicitly stated intention to recruit SCLD families, X[superscript 2](1) = 12.70, p < 0.001, Phi = 0.38 (moderate); and for studies that reported the following participant characteristics: language, X[superscript 2](1) = 29.58, p < 0.001, Phi = 0.48 (moderate-to-large); and race/ethnicity + SES + language, X[superscript 2](1) = 19.26, p < 0.001, Phi = 0.39 (moderate). However, associations were not found between recruitment and retention approaches and whether studies included High SCLD or Low SCLD samples. Further study of NDD researchers' recruitment and retention approaches that successfully include SCLD families is needed.

Access to authentic research is limited at the 7-12 science education level. At the same time, many local restoration projects would benefit from, but don't have access to a long-term system of monitoring. This project seeks to unite those two needs by developing a protocol for 7-12 classrooms to be able to participate in authentic research through long-term monitoring of a local restoration project. The protocol developed in this project was used by Jenison High School students at Grand Ravines Park. Grand Ravines Park is a recently acquired Ottawa County park with a history of anthropogenic disturbances. Shortly after the acquisition of the final section of the park, Ottawa County Parks and Recreation (OCPR) staff seeded the portions of the north side of the park with native grasses and forbs, and the south side of the park was not supplementally seeded. This study aimed to develop protocols for the student collection and analysis of vegetation and invertebrate biodiversity data in both the north and south sides of the park. Various metrics of plant and invertebrate biodiversity were compared between the seeded and unseeded areas. Students from Jenison High School were involved in the data collection and analysis, increasing their exposure to scientific research and field sampling and developing their scientific literacy. A long-term database was developed, with the goals of facilitating park management decisions and driving future student research questions.

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift, covariate-shift. Succinctly, these events drove changes in the stationarity of the target variable and the predictors. The result is a student body with unknown performance qualities entirely distinct from previous cohorts. This study explains and illustrates: (1) how to test for academic model drift in academe; and (2) sets forth two methods used to repair vitiated student-body performance properties. Formally, it frames the data-drift outcome as a One Class problem which allows the deployment of two well-known One-Class algorithms: Support Vector Machines and Isolated Random Forests. The study shows their use in reconstructing a representative sample of the student-body

This study analyzed studies done in Turkey in the context of curriculum evaluation (CE) by asking, "How is it made? The study was carried out in two stages. In the first stage, the document analysis method used 215 theses written between 1991 and 2020 on CE were analyzed according to the "thesis review form." In the second stage, depth analysis was made through semi-structured interviews with the authors (students) and the field experts (supervisors of the authors) of the theses to make the results of the first stage more understandable. Interviews were conducted with 32 participants. A maximum sampling method was used to determine the participants. The data analysis calculated percentage and frequency values for the data obtained in the first stage. In the second stage, descriptive analysis and content analysis were carried out with the MAXQDA 2020 qualitative data analysis program. The majority of theses did not employ a CE model as a consequence of the research, and the CIPP model was the most popular CE model. Many of the theses were not justified in using the CE model. Model usage increased as time passed to the present day. Many theses used quantitative models but did not explicitly state the sampling technique. Teachers were mainly used in this research as a source for data gathering, and participant numbers ranged from 10 to 50. Additionally, most studies used questionnaires and interviews as the primary data-gathering tools. All of these findings suggest that CE studies have several flaws.

Language sample analysis (LSA) is valuable clinical tool and an important component of comprehensive language assessment. However, LSA is underutilized in real-world practice. SLPs have identified time constraints, insufficient training, and lack of confidence in LSA skills as barriers to regular LSA implementation. Communication science and disorders (CSD) programs have opportunities to provide LSA instruction to address these barriers and prepare students to reliably, feasibly, and confidently use LSA in clinical practice. This pilot study examined CSD students' perspectives on LSA instruction using the Language ENvironment Analysis System (LENA). Undergraduate students (n = 38) completed a series of two electronic surveys prior to and following LSA instruction using LENA. Changes in students' self-ratings of knowledge and skills in collecting and analyzing language samples, attitudes towards studying LSA and child language development, and critical thinking skills were assessed. Significantly higher student ratings were found for all items measuring language sampling knowledge and skills following digital LSA instruction compared to baseline. In addition, student ratings of enthusiasm for and confidence in studying LSA and child language development were also significantly higher. Students' self-ratings of critical thinking skills did not increase significantly following instruction in digital LSA. Implications for LSA teaching and learning are discussed.

The rapid development in the world of technology and communication has contributed directly to the teaching and learning process in schools. Therefore, a paradigm shift towards learning methods in the education system needs to be implemented to meet the educational aims of the 21st century. This is due to the current methods of delivery in teaching and learning are still conventional. Therefore, this study aims to develop Trig-GO apps for mathematics in secondary schools by focusing on the topic of trigonometry. The development process of this application is guided by the model of Dick, Carey and Carey. This paper describes the design and development of Trig-GO apps prototype based on Design and Development Research (DDR) methods and approaches.The findings of the study indicated that a prototype of the Trig-GO application has been developed. The results of the development of the application prototype were evaluated through qualitative methods. The data was obtained from five experienced teachers and six students via observations, interviews and document analysis. Therefore, the results of the analysis showed that the Trig-GO application prototype developed need improvement. Thus, this also will give impact to the quality and production of the application prototype. It is expected to assist students in solving trigonometry questions at the highest level. The development of this prototype application is intended to assist teachers in providing teaching aids that can be used in the teaching of trigonometry through a digital approach.

This study is designed to reveal the research trends of graduate theses published in the field of computer programming in K-12 between 2018 and 2022. Document analysis was used for data collection in this study. The data was divided into 9 categories, and the results demonstrated that the scholars in the Departments of Computer Educational and Instructional Technologies (CEIT) at Atatürk University and Ondokuz Mayis University of Türkiye mainly published master's theses in this field in 2019. Another result was that though CEIT is also the main department for doctoral theses, no difference exists in terms of years or universities. "Teaching, learning, and training programming" and "computational thinking" are the most frequently used keywords in graduate programs, respectively. While quantitative and mixed method (parallel, convergent), convenience sampling, and psychometric scales are preferred in master's theses, mixed method (explanatory, mixed), purposive sampling, interviews/focus group interviews, and psychometric scales are preferred in doctoral theses, respectively. Observation and semi-structured interview forms are used as data collection tools in the theses. Although quantitative methods are mainly utilized in data analysis, parametric and non-parametric statistical tests are also used, and content analysis is resorted to as part of qualitative methods. Whilst validity and reliability studies have been carried out measurement tools are employed in master's theses, scales have been developed in doctoral theses. The present study also offers some suggestions for future research on teaching computer programming.

In the context of Artificial Intelligence, Generative Adversarial Nets (GANs) allow the creation and reproduction of artificial data from real datasets. The aims of this work are to seek to verify the equivalence of synthetic data with real data and to verify the possibilities of GAN in educational research. The research methodology begins with the creation of a survey that collects data related to the self-perceptions of university teachers regarding their digital competence and technological-pedagogical knowledge of the content (TPACK model). Once the original dataset is generated, twenty-nine different synthetic samples are created (with an increasing N) using the COPULA-GAN procedure. Finally, a two-stage cluster analysis is applied to verify the interchangeability of the synthetic samples with the original, in addition to extracting descriptive data of the distribution characteristics, thereby checking the similarity of the qualitative results. In the results, qualitatively very similar cluster structures have been obtained in the 150 tests carried out, with a clear tendency to identify three types of teaching profiles, based on their level of technical-pedagogical knowledge of the content. It is concluded that the use of synthetic samples is an interesting way of improving data quality, both for security and anonymization and for increasing sample sizes.

The quality of mixing methods has been widely debated in the field of applied linguistics (AL) and the integration of data from both quantitative and qualitative research paradigms has always been open to controversy. The present study was aimed at recognizing the status quo of MMR in AL, investigating the nature of various sections of MMR studies, and specifying the way the pattern of employing MMR has changed over the past few years. From a total of 1,314 articles in seven peer-reviewed accredited AL journals, 220 articles were finally identified to be mixed in method which were subsequently scrutinized based on already established frameworks regarding their research questions, research designs, sampling designs, and inference quality to explore the status quo of mixed methods research (MMR) in AL. We went through two phases of screening to identify the articles which met the principles of MMR and analyzed the selected articles qualitatively based on a coding scheme. The findings revealed (a) only a few research questions were hybrid, (b) both concurrent research designs and concurrent sampling designs were employed more frequently than their sequential counterparts, and (c) only a few articles made their inferences mixed in a principled manner. The findings of several extracts and an open-ended questionnaire showed a growing interest in MMR and the challenges and problems of conducting MMR, respectively. The results might imply that the new paradigms of research favor mixing methods and that the researchers employ it more due to its strengths.

To date, there has been mounting research evidence on the impact of written corrective feedback. In response to this, reviews have been conducted to condense either findings or methodologies through the combined analysis of EFL and ESL studies. Although syntheses shed light on the topic, no one provides exclusive insights into EFL realities. Therefore, this systematic literature review was proposed to synthesize methodologies, findings, and research suggestions of EFL articles analyzing direct and indirect feedback, two widely debated and employed strategies in classrooms, with either a focused or unfocused scope. Through a seven-step protocol suggested by Petticrew and Roberts (2006), 21 EFL primary studies published between 2012 and 2022 were selected from the bibliometric dataset of Crosthwaite et al. (2022), which contains articles retrieved from the Scopus database. The papers were read through to answer five research questions on methodologies, findings, and study suggestions. The results show that most EFL studies were quasi-experimental adopting a quantitative approach with a pretest-posttest-delayed-posttest or pretest-posttest design, and the strategies significantly impinged on grammar and non-grammar aspects. Suggestions are given to further study the impact of feedback under different conditions such as targets, proficiency levels, and strategies and scope with a comparative nature.

When constructed-response items are administered for a second time, it is necessary to evaluate whether the current Time B administration's raters have drifted from the scoring of the original administration at Time A. To study this, Time A papers are sampled and rescored by Time B scorers. Commonly the scores are compared using the proportion of exact agreement across times and/or t-statistics comparing Time A means to Time B means. It is common to treat these rescores with procedures that assume a multinomial sampling model, which is incorrect. The correct, product-multinomial model reflects the stratification of Time A scores. Using direct computation, the research report demonstrates that both proportion of exact agreement and the t-statistic can deviate substantially from expected behavior, providing misleading results. Reweighting the rescore table gives each statistic the correct expected value but does not guarantee that the usual sampling distributions hold. It is also noted that the results apply to a wider class of situations in which a set of papers is scored by one group of raters or scoring engine and then a sample is selected to be evaluated by a different group of raters or scoring engine.

The aim of this research is to examine the postgraduate studies conducted in the field of mathematics education with individuals diagnosed as gifted and talented students in Türkiye. For this purpose, master's and doctoral studies in the database of the National Thesis Center of the Council of Higher Education [CHE] of Türkiye were examined. The first graduate thesis carried out in the field of mathematics education with gifted and talented students was carried out in 2009. In this context, the research includes 55 master's and doctoral studies from 2009 to 2020. In this study, in which the qualitative research approach was preferred, thematic content analysis (meta-synthesis) one of the content analysis types was used. The theses included in the scope of the study were examined under different headings such as year, type, subject, approach, research design, sample, data collection tool, and data analysis techniques. This research is important in terms of giving a general idea about the studies carried out in the field of gifted students and mathematics education, providing information pertaining to the relevant literature for the studies to be carried out, helping expand the field with studies with more different content by determining the subjects and scopes that have not been researched in the field, and giving inspiration related to different research ideas that will contribute to the literature.

When designing a study, the sampling method for selecting research participants is an important decision with a host of considerations. When designing a professional development (PD) program with a limited number of spaces, the method of choosing participants from the applicants is also important. When research and professional development are entwined, sets of sampling criteria could conflict. Additionally, in a mixed methods study, such as the one undertaken by the researchers, additional trade-offs exist when considering the many potential methods of sampling and participant selection. In this report, we present a novel solution to the problem of multiple-criterion-focused selection of research and PD participants when the number of applicants to participate outnumbers the availability of resources. By using a weighted ranking system, we were able to incorporate multiple purposive sampling criteria in a simultaneous, rather than sequential, fashion. This allowed us to focus our evaluation of chemistry-specific free-response questions on a narrower pool of finalists and more consistently rate their responses. This multipurposive sampling method and a novel tool developed herein have broad implications for both sampling for chemistry education research and participant selection in professional development.

When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to incorporate primary studies reporting sample medians in meta-analysis, yet there are currently no comprehensive software tools implementing these methods. In this paper, we present the "metamedian" R package, a freely available and open-source software tool for meta-analyzing primary studies that report sample medians. We summarize the main features of the software and illustrate its application through real data examples involving risk factors for a severe course of COVID-19.

While many instructors are aware of the "Literary Digest" 1936 poll as an example of biased sampling methods, this article details potential further explorations for the "Digest's" 1924-1936 quadrennial U.S. presidential election polls. Potential activities range from lessons in data acquisition, cleaning, and validation, to basic data literacy and visualization skills, to exploring one or more methods of adjustment to account for bias based on information collected at that time. Students can also compare how those methods would have performed. One option could be to give introductory students a first look at the idea of "sampling adjustment" and how this principle can be used to account for difficulties in modern polling, but the context is rich in other opportunities that can be discussed at various times in the course or in more advanced sampling courses.

In sample survey analysis, accurate population mean estimation is an important task, but traditional approaches frequently ignore the intricacies of real-world data, leading to biassed results. In order to handle uncertainties, indeterminacies, and ambiguity, this work presents an innovative approach based on neutrosophic statistics. We proposed novel neutrosophic factor type exponential estimators that use auxiliary information to improve population mean precision. The suggested estimators are highly useful for computing results while working with unclear, hazy, and neutrosophic-type data. These estimators produce answers that are interval-form rather than single-valued, which may give our population parameter a better chance of being off. Since we now have an estimated interval with the population mean's unknown value supplied a minimal MSE or highest Percentage Relative Efficiency (PRE), the estimators are more effective. The investigation of alpha constants in the neutrosophic framework, extending from -1 to +1, is at the heart of this research. These constants have a significant impact on how estimates are made and enable flexible accuracy modification. We choose the best neutrosophic values for characterizing constants, emphasizing their importance in obtaining precise estimations. This study further expands its originality by including preexisting estimators into the neutrosophic framework, showcasing its versatility and adaptability. We demonstrate the estimators' superiority to traditional techniques through empirical assessments employing neutrosophic temperature information and simulation assessments. The ensuing interval-based results, influenced by alpha constants, offer concrete insights into the world of uncertainty, enabling more well-informed decision-making.

The iterative process of ethnography not only constructs theory, but its methodology should embody theory. Developing a theoretical framework often demands adjustments in methodology, to leverage previous work and to avoid assumptions compounding through the magnification of blind spots. New theory in policy-engaged ethnography has emphasised the importance of analysing interactions between teachers. However, such analyses have not yet emerged in research on education in Japan, partly because of enduring conceptualisations of teachers as homogeneous. Methodological tools that do exist are grounded in anglosphere data and remain unsuited to research in Japan's schools. As such, participants are positioned amongst, but not in relation to, other teachers and school administrators. Yet, taking teacher heterogeneity seriously enhances the salience of selection bias. The same teachers who are enthusiastic about participating in the research are often those who are enthusiastic about the topic of the research. What is difficult in school ethnography is procuring, and preferably ensuring, participation from teachers who are "not" enthusiasts toward the topic of the research. Designing research with both the heterogeneity of teachers and the specifics of the local context in mind can provide more diverse data with potential to inform analyses of micro-politics in Japan's schools. In reference to a five-year research project and grounded in data from Japan, the paper proposes some procedures to access a diverse range of teachers based on a theory developed to conceptualise teachers' orientations towards policy work.

Trend scoring constructed response items (i.e. rescoring Time A responses at Time B) gives rise to two-way data that follow a product multinomial distribution rather than the multinomial distribution that is usually assumed. Recent work has shown that the difference in sampling model can have profound negative effects on statistics usually used to monitor rater drift. In this paper, three statistics, termed E-statistics, that account for the product-multinomial sampling by comparing conditional distributions, are introduced. A simulation compares performance with the paired t-test and Stuart's Q in detecting rater drift. Both the paired t-test and Q suffered extreme Type I error inflation for certain rescore study designs. The new E-statistics maintained good Type I error control and had good power to detect rater drift across occasions.

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights.

Measurement errors may significantly distort the properties of an estimator. In this paper, estimators of the finite population variance using the information on first and second raw moments of the study variable are developed under stratified random sampling that incorporate the variance of a measurement error component. Additionally, combined and separate estimators are also developed for estimating the finite population variance using supplementary information in terms of one or two auxiliary variables. An empirical study is carried out to study the effect of measurement error on the relative efficiencies of the proposed estimators.

The data we encounter in real life often contain missing values. In sampling methods, missing value imputation is done with different methods. This article proposes novel logarithmic type imputation methods for estimating the population mean in the presence of missing data under ranked set sampling (RSS). According to the determined theoretical results, the proposed imputation methods are found to be the most efficient in comparison to popularly known imputation methods like mean imputation, Al-Omari and Bouza (2014) imputation methods, Sohail et al. (2018) imputation methods, and Bhushan and Pandey (2016) type imputation methods utilizing RSS. Apart from this, a simulation study has been accomplished utilizing artificially drawn symmetric and asymmetric populations. The outcomes are encountered to be rather satisfactory, showing improvement over all existing imputation methods.

This article introduces an innovative sampling scheme, the median sampling (MS), utilizing individual observations over time to efficiently estimate the mean of a process characterized by a symmetric (non-uniform) probability distribution. The mean estimator based on MS is not only unbiased but also boasts enhanced precision compared to its simple random sampling-based counterpart. Moreover, a new EWMA chart based on the mean estimator within the MS scheme is proposed. The performance of the EWMA charts, derived from both simple random sampling (SRS) and MS schemes, is evaluated using the metrics of steady-state average run-length and average number of items-to-signal. The findings underscore the superiority of the EWMA-MS chart over the EWMA-SRS chart. Additionally, as the magnitude of ranking errors escalates, the behavior of the EWMA-MS chart converges toward that of the EWMA-SRS chart. The practical implementation of the newly introduced EWMA chart is demonstrated through an illustrative example.

Interactive computer simulations are commonly used as pedagogical tools to support students' statistical reasoning. This paper examines whether and how these simulations enable their intended effects. We begin by contrasting two theoretical frameworks--"dual processes" and "grounded cognition"--in the context of people's conceptions about statistical sampling, setting the stage for the potential benefits of simulations in learning such conceptions. Then, we continue with reviewing the educational literature on statistical sampling simulations. Our review tentatively suggests benefits of the simulations for building "statistical habits of mind." However, challenges seem to persist when more specific concepts and skills are investigated. With and without simulations, students have difficulty forming an aggregate view of data, interpreting sampling distributions, showing a process-based understanding of the law of large numbers, making statistical inferences, and context-independent reasoning. We propose that grounded cognition offers a framework for understanding these findings, highlighting the bidirectional relationship between perception and conception, perceptual design features, and guided perceptual routines for supporting students' meaning making from simulations. Finally, we propose testable instructional strategies for using simulations in statistics education.

Undergraduate institutions serve as stepping stones to students' careers. Teaching and learning in science classrooms require quality and integrated teaching-research learning experiences that prepare students to advance their careers. Using publicly available data sets and open-access data analyses software can be impactful for engaging students in meaningful research experiences while keeping institutional expenses associated with implementing and supporting research opportunities low. In this article, we share two examples of student-led data research that utilized open data and software to analyze microbial sequence data. As part of this process, we also recognized the need for discipline-specific databases that can hold a record of publicly available data. Such a setup will support undergraduate educators and students in accessing data relevant to their requirements, thereby narrowing the gap between data availability and accessibility. Finally, both examples illustrate the benefits of using open resources in supporting student learning, with the possibility of using such resources to develop course-based undergraduate research experiences as part of course curricula.

Simple matrix sampling planned missing (SMS PD) design, introduce missing data patterns that lead to covariances between variables that are not jointly observed, and create difficulties for analyses other than mean and variance estimations. Based on prior research, we adopted a new multigroup confirmatory factor analysis (CFA) approach to handle missing data in such designs, in comparison to a regular CFA with full information maximum likelihood estimator. In Study 1, we tested the two approaches in 36 scenarios (4 sample sizes x 3 inter-item correlations x 3 numbers of x-set items) given a total of 20 items. We found that, the multigroup CFA approach performed with acceptable convergence rates, power to recover population values, acceptable standard errors and model fit in certain scenarios by larger sample size, higher bivariate correlation, and more items in the x-set. We found a few scenarios where regular CFA with FIML performed well. These findings suggested that the approaches can be implemented to handle the special missing data introduced by the SMS PM designs, and, thereby, enhance the utility of SMS PM data. In Study 2, we applied the multigroup CFA approach in real-world data to demonstrate the feasibility and analytic value of this approach.