Academic article descriptive statistics.

BackgroundIn medical practice, clinically unexpected measurements might be quite properly handled by the remeasurement, removal, or reclassification of patients. If these habits are not prevented during clinical research, how much of each is needed to sway an entire study?Methods and ResultsBelieving there is a difference between groups, a well-intentioned clinician researcher addresses unexpected values. We tested how much removal, remeasurement, or reclassification of patients would be needed in most cases to turn an otherwise-neutral study positive. Remeasurement of 19 patients out of 200 per group was required to make most studies positive. Removal was more powerful: just 9 out of 200 was enough. Reclassification was most powerful, with 5 out of 200 enough. The larger the study, the smaller the proportion of patients needing to be manipulated to make the study positive: the percentages needed to be remeasured, removed, or reclassified fell from 45%, 20%, and 10% respectively for a 20 patient-per-group study, to 4%, 2%, and 1% for an 800 patient-per-group study. Dot-plots, but not bar-charts, make the perhaps-inadvertent manipulations visible. Detection is possible using statistical methods such as the Tadpole test.ConclusionsBehaviours necessary for clinical practice are destructive to clinical research. Even small amounts of selective remeasurement, removal, or reclassification can produce false positive results. Size matters: larger studies are proportionately more vulnerable. If observational studies permit selective unblinded enrolment, malleable classification, or selective remeasurement, then results are not credible. Clinical research is very vulnerable to “remeasurement, removal, and reclassification”, the 3 evil R's.

Feature Articles on Employment and Labour - Statistics on Job Vacancies

Dataset for the statistical analysis of the article "Empowerment through Participatory Game Creation: A Case Study with Adults with Intellectual Disability".

Supplementary materials for the article: De Winter, J. C. F., Dodou, D., & Wieringa, P. A. (2009). Exploratory factor analysis with small sample sizes. Multivariate Behavioral Research, 44, 147–181.

All statistics were done in R Studio

This item contains supplemental material referenced by the paper by Tang et al., “Misuse, Misreporting, Misinterpretation of Statistical Methods in Usable Privacy and Security Papers” at the Symposium on Usable Security and Privacy (SOUPS), 2025.It contains two files:List of Papers: The list of all SOUPS papers, along with the year of publication, considered in this study.Tests and Statistics Considered: The table contains the statistical tests considered in this study along with the associated statistics.

Firearms are the leading cause of death for minors in the United States and US gun culture is often discussed as a reason behind the prevalence of school shootings. Yet, few studies systematically analyze if there is a connection between the two: Do school shooters show a distinct gun culture? This article studies gun culture in action in school shootings. It studies if school shooters show distinct meanings and practices around firearms prior to the shooting, as well as patterns in access to firearms. To do so, I analyze a full sample of US school shootings. Relying on publicly available court, police, and media data, I combine qualitative in-depth analyses with cross-case comparisons and descriptive statistics. Findings suggest most school shooters come from a social setting in which firearms are a crucial leisure activity and hold meanings of affection, friendship, and bonding. These meanings translate into practices: all school shooters had easy access to the firearms they used for the shooting. Findings contribute to research on firearms and youth violence, public health, as well as the sociology of culture.

Descriptive estimates and inferences related to key variables from the two SESTAT surveys when following alternative analytic approaches.

Note: Sample size is 4,351 respondents in 146 neighborhoods.

Bibliometric studies offer numerous ways of analyzing scientific work. For example, co-citation and bibliographic coupling networks have been widely used since the 1960s to describe the segmentation of research and to look the development of the scientific frontier. In addition, co-authorship and collaboration networks have been employed for more than 30 years to explore the social dimension of scientific work. This paper introduces publication authorship as a complement to these established approaches. Three data sets of academic articles from accounting, astronomy, and gastroenterology are used to illustrate the benefits of publication authorship for bibliometric studies. In comparison to bibliographic coupling, publication authorship produces significantly better intra-cluster cosine similarities across all data sets, which in the end yields a more fine-grained picture of the research field in question. Beyond this finding, publication authorship lends itself to other types of documents such as corporate reports or meeting minutes to study organizations, movements, or any other concerted activity.

Although prehospital emergency anesthesia (PHEA), with a specific focus on intubation attempts, is frequently studied in prehospital emergency care, there is a gap in the knowledge on aspects related to adherence to PHEA guidelines. This study investigates adherence to the “Guidelines for Prehospital Emergency Anesthesia in Adults” with regard to the induction of PHEA, including the decision making, rapid sequence induction, preoxygenation, standard monitoring, intubation attempts, adverse events, and administration of appropriate medications and their side effects. This retrospective study examined PHEA interventions from 01/01/2020 to 12/31/2021 in the city of Aachen, Germany. The inclusion criteria were adult patients who met the indication criteria for the PHEA. Data were obtained from emergency medical protocols. A total of 127 patients were included in this study. All the patients met the PHEA indication criteria. Despite having a valid indication, 29 patients did not receive the PHEA. 98 patients were endotracheally intubated. For these patients, monitoring had conformed to the guidelines. The medications were used according to the guidelines. A significant increase in oxygen saturation was reported after anesthesia induction (p < 0.001). The patients were successfully intubated endotracheally on the third attempt. Guideline adherence was maintained in terms of execution of PHEA, rapid sequence induction, preoxygenation, monitoring, selection, and administration of relevant medications. Emergency physicians demonstrated the capacity to effectively respond to cardiorespiratory events. Further investigations are needed on the group of patients who did not receive PHEA despite meeting the criteria. The underlying causes of decision making in these cases need to be evaluated in the future.

For each main and supporting figures, the linear mixed models, statistical inference tests, and p-values are shown. (XLSX)

Descriptive statistics of participants.

Collecting and utilizing data to understand population trends, make predictions, and guide decisions is becoming increasingly common in today's world. In particular, statistical learning allows users to infer relationships between variables, learn patterns, and predict outcomes for previously unseen data via concepts and techniques from statistics and machine learning. Although many of the results of this practice have been beneficial, the data used often contain sensitive information, such as medical records or financial information, so maintaining privacy is of paramount importance when releasing statistics, parameter estimates, and other results. Differential privacy (DP) is the state-of-the-art framework for guaranteeing privacy when releasing aggregate information and statistics from a dataset. It provides a provable bound on the incurred privacy loss via the injection of random noise, at the cost of a reduction in utility. While many works have been devoted to establishing DP guarantees for various analysis tools in the past two decades since DP's introduction, many popular statistical learning approaches still lack a DP counterpart. This dissertation addresses this issue in three original research topics, as listed below.

First, the dissertation presents the first differentially private algorithm for general weighted empirical risk minimization (wERM), along with theoretical DP guarantees. It evaluates the performance of the DP-wERM framework applied to outcome weighted learning (OWL), a method for learning individualized treatment rules, in both simulation studies and in a real clinical trial. The results demonstrate the feasibility of training OWL models via wERM with DP guarantees while maintaining sufficiently robust model performance.

Second, the dissertation presents several original approaches with proven DP guarantees for linear mixed-effects (LME) models. LME models are popular, especially among statisticians, but lack sufficient work on integrating DP. The work leverages some recent advancements in the DP literature, particularly in DP stochastic gradient descent (SGD), to estimate LME model parameters with DP guarantees with better privacy-utility trade-offs. Theoretical results for an upper bound for the mean squared error between private parameter estimates vs the true parameters for DP-SGD-based approaches are provided, and a simulation study and a real-world case study provide further empirical evidence for the feasibility of the approaches at practically reasonable privacy budgets.

Third, this dissertation introduces SAFES, a Sequential PrivAcy and Fairness Enhancing data Synthesis procedure that sequentially combines DP data synthesis with a fairness-aware data transformation. Alongside privacy, the fairness of decisions made by a statistical learning model is also crucial to address, though the vast majority of existing literature treats the two concerns independently. For methods that do consider privacy and fairness simultaneously, they often only apply to a specific machine learning task, limiting their generalizability. SAFES allows full control over the privacy-fairness-utility trade-off via tunable privacy and fairness parameters. SAFES is illustrated by combining a graphical model-based DP data synthesizer with a popular fairness-aware data pre-processing transformation, and empirical evaluations on two popular benchmark datasets demonstrate that for reasonable privacy loss, SAFES-generated synthetic data achieve significantly improved fairness metrics with relatively low utility loss.

The datasets containing simulation performance results during the current study, in addition to the code to replicate the simulation study in its entirety, are available here. See the README file for a description the Stata do-files, R-script files, tips to run the code, and the performance result dataset dictionaries.

This video lecture and slide set presents a pragmatic statistical philosophy, including both frequentist and Bayesian ideas as well as providing careful definitions of inference, hypothesis testing, and P values.Latest slide set with video, MMED 2017:- 'Dushoff-StatsPhilosophy.pdf'- 'Dushoff-Intro to Statistical Philosophy.mp4'Latest slide set, MMED 2018:'DushoffStatisticalPhilosophyMMED2018.pdf'

Open datasets for the Exeter Cascade Project 1-25.

Feature Articles on Population - Enhanced Method for Compiling Statistics on Hong Kong Residents Having Resided / Having Stayed Substantially in the Mainland

ITS data collected as part of Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study. Code used to analyse the ITS studies.