BackgroundThere is widespread evidence that statistical methods play an important role in original research articles, especially in medical research. The evaluation of statistical methods and reporting in journals suffers from a lack of standardized methods for assessing the use of statistics. The objective of this study was to develop and evaluate an instrument to assess the statistical intensity in research articles in a standardized way.MethodsA checklist-type measure scale was developed by selecting and refining items from previous reports about the statistical contents of medical journal articles and from published guidelines for statistical reporting. A total of 840 original medical research articles that were published between 2007–2015 in 16 journals were evaluated to test the scoring instrument. The total sum of all items was used to assess the intensity between sub-fields and journals. Inter-rater agreement was examined using a random sample of 40 articles. Four raters read and evaluated the selected articles using the developed instrument.ResultsThe scale consisted of 66 items. The total summary score adequately discriminated between research articles according to their study design characteristics. The new instrument could also discriminate between journals according to their statistical intensity. The inter-observer agreement measured by the ICC was 0.88 between all four raters. Individual item analysis showed very high agreement between the rater pairs, the percentage agreement ranged from 91.7% to 95.2%.ConclusionsA reliable and applicable instrument for evaluating the statistical intensity in research papers was developed. It is a helpful tool for comparing the statistical intensity between sub-fields and journals. The novel instrument may be applied in manuscript peer review to identify papers in need of additional statistical review.

Brochure Theme: A0 – Analysis and studies – General Under Theme: A000.01 – Statistical studies

This dataset contains tabular files with information about the usage preferences of speakers of Maltese English with regard to 63 pairs of lexical expressions. These pairs (e.g. truck-lorry or realization-realisation) are known to differ in usage between BrE and AmE (cf. Algeo 2006). The data were elicited with a questionnaire that asks informants to indicate whether they always use one of the two variants, prefer one over the other, have no preference, or do not use either expression (see Krug and Sell 2013 for methodological details). Usage preferences were therefore measured on a symmetric 5-point ordinal scale. Data were collected between 2008 to 2018, as part of a larger research project on lexical and grammatical variation in settings where English is spoken as a native, second, or foreign language. The current dataset, which we use for our methodological study on ordinal data modeling strategies, consists of a subset of 500 speakers that is roughly balanced on year of birth. Abstract: Related publication In empirical work, ordinal variables are typically analyzed using means based on numeric scores assigned to categories. While this strategy has met with justified criticism in the methodological literature, it also generates simple and informative data summaries, a standard often not met by statistically more adequate procedures. Motivated by a survey of how ordered variables are dealt with in language research, we draw attention to an un(der)used latent-variable approach to ordinal data modeling, which constitutes an alternative perspective on the most widely used form of ordered regression, the cumulative model. Since the latent-variable approach does not feature in any of the studies in our survey, we believe it is worthwhile to promote its benefits. To this end, we draw on questionnaire-based preference ratings by speakers of Maltese English, who indicated on a 5-point scale which of two synonymous expressions (e.g. package-parcel) they (tend to) use. We demonstrate that a latent-variable formulation of the cumulative model affords nuanced and interpretable data summaries that can be visualized effectively, while at the same time avoiding limitations inherent in mean response models (e.g. distortions induced by floor and ceiling effects). The online supplementary materials include a tutorial for its implementation in R.

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.

Brochure Theme: A0 – Analysis and studies – General

Under Theme: A000.01 – Statistical studies

Pen-and-paper homework and project-based learning are both commonly used instructional methods in introductory statistics courses. However, there have been few studies comparing these two methods exclusively. In this case study, each was used in two different sections of the same introductory statistics course at a regional state university. Students’ statistical literacy was measured by exam scores across the course, including the final. The comparison of the two instructional methods includes using descriptive statistics and two-sample t-tests, as well authors’ reflections on the instructional methods. Results indicated that there is no statistically discernible difference between the two instructional methods in the introductory statistics course.

File S1 includes Appendix S1, Appendix S2, Appendix S3, Appendix S4. Appendix S1: Search terms used to identify studies of one year mortality on antiretroviral therapy. Appendix S2: Full citations for studies reviewed. Appendix S3: Illustration of a distribution used to impute CD4 count with bands. Appendix S4: CD4 coefficient (bottom) and model fit (F-statistic – top) for the relationship between one year mortality on ART and baseline CD4 count using varying assumptions about the amount of mortality among those lost to follow-up. (DOCX)

UNIDO pub. Handbook concerning the uses of statistical analysis of systems and processes of an industrial enterprise - designed to assist managers and engineers in interpreting the effects of variation and in measuring and improving performance. Statistics, graphs, diagrams, practice problems. Additional references: data collecting, factory organization, management techniques.

This dataset is about books. It has 1 row and is filtered where the book is College crime : a statistical study of offenses on American campuses. It features 7 columns including author, publication date, language, and book publisher.

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.

The global Clinical Data Management and Statistical Analysis market is projected to reach USD XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The increasing demand for efficient and accurate clinical trials, rising adoption of electronic health records (EHRs), and growing focus on personalized medicine are the major factors driving the growth of the market. Additionally, the increasing number of clinical trials and the need for regulatory compliance are further contributing to the market's expansion. North America is expected to hold the largest market share over the forecast period due to the presence of a large number of pharmaceutical companies, CROs, and academic research institutions. Asia Pacific is projected to be the fastest-growing region owing to the rising prevalence of chronic diseases and the increasing investment in healthcare infrastructure. Key players in the market include Clinipace, Charles River Laboratories, LabCorp, ICON PLC, Parexel, IQVIA, Pharmaron, Pharmaceutical Product Development LLC (PPD), WuXi AppTec, Elixir Clinical Research, Yikefu Technology, Taimei Medical Technology, Medidata, Clinflash Healthcare Technology, Bioknow, ArisGlobal, Yidu Tech Inc., WeTrial, Lingxian Pharmaceutical Technology, Oracle, and Zhongxing Zhengyuan Technology.

The case-cohort study design combines the advantages of a cohort study with the efficiency of a nested case-control study. However, unlike more standard observational study designs, there are currently no guidelines for reporting results from case-cohort studies. Our aim was to review recent practice in reporting these studies, and develop recommendations for the future. By searching papers published in 24 major medical and epidemiological journals between January 2010 and March 2013 using PubMed, Scopus and Web of Knowledge, we identified 32 papers reporting case-cohort studies. The median subcohort sampling fraction was 4.1% (interquartile range 3.7% to 9.1%). The papers varied in their approaches to describing the numbers of individuals in the original cohort and the subcohort, presenting descriptive data, and in the level of detail provided about the statistical methods used, so it was not always possible to be sure that appropriate analyses had been conducted. Based on the findings of our review, we make recommendations about reporting of the study design, subcohort definition, numbers of participants, descriptive information and statistical methods, which could be used alongside existing STROBE guidelines for reporting observational studies.

This dataset includes spreadsheets with statistical data (mean and median absolute error) used in deciding which interpolation method best fit the corresponding dataset. All statistical data were paired with a visual inspection of the interpolation prior to determining the final raster product. All spreadsheets were generated using an automated Python script (Jahn, 2020).

The Annual Health Establishment Statistics (AHS) is a mandatory comprehensive administrative survey, the main source of data on all health facilities in France. The “statistical” databases are a set of files that improve the data of the “administrative” databases by treating total and partial non-response, intended for statistical studies. Each file corresponds to the answers to one of the SAE’s thematic slips. The Annual Health Establishment Statistics (AHS) is a mandatory comprehensive administrative survey, the main source of data on all health facilities in France. The “statistical” databases are a set of files that improve the data of the “administrative” databases by treating total and partial non-response, intended for statistical studies. Each file corresponds to the answers to one of the SAE’s thematic slips. The Annual Health Establishment Statistics (AHS) is a mandatory comprehensive administrative survey, the main source of data on all health facilities in France. The “statistical” databases are a set of files that improve the data of the “administrative” databases by treating total and partial non-response, intended for statistical studies. Each file corresponds to the answers to one of the SAE’s thematic slips. The Annual Health Establishment Statistics (AHS) is a mandatory comprehensive administrative survey, the main source of data on all health facilities in France. The “statistical” databases are a set of files that improve the data of the “administrative” databases by treating total and partial non-response, intended for statistical studies. Each file corresponds to the answers to one of the SAE’s thematic slips. The Annual Health Establishment Statistics (AHS) is a mandatory comprehensive administrative survey, the main source of data on all health facilities in France. The “statistical” databases are a set of files that improve the data of the “administrative” databases by statistical reprocessing of non-response and manifest declarative errors. Each file corresponds to the answers to one of the SAE’s thematic slips.

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

The market for statistical analysis software is segmented by various factors, including:

Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

This is the replication package for the analysis done in the paper "Evolution of statistical analysis in empirical software engineering research: Current state and steps forward" (DOI: https://doi.org/10.1016/j.jss.2019.07.002, preprint: https://arxiv.org/abs/1706.00933).

The package includes CSV files with data on statistical usage extracted from 5 journals in SE (EMSE, IST, JSS, TOSEM, TSE). The data was extracted from papers between 2001 - 2015. The package also contains forms, scripts and figures (generated using the scripts) used in the paper.

The extraction tool mentioned in the paper is available in dockerhub via: https://hub.docker.com/r/robertfeldt/sept