This dataset contains background data and supplementary material for a methodological study on the use of ordinal response scales in linguistic research. For the literature survey reported in that study, which examines how rating scales are used in current linguistic research (4,441 papers from 16 linguistic journals, published between 2012 and 2022), it includes a tabular file listing the 406 research articles that report ordinal rating scale data. This file records annotated attributes of the studies and rating scales. Further the dataset includes summary data gathered in a review of the psychometric literature on the interpretation of quantificational expressions that are often used to build graded scales. Empirical findings are collected for five rating scale dimensions: agreement (1 study), intensity (3 studies), frequency (17 studies), probability (11 studies), and quality (3 studies). Finally, the post includes new data from 20 informants on the interpretation of the quantifiers "few", "some", "many", and "most". Abstract: Related publication Ordinal scales are commonly used in applied linguistics. To summarize the distribution of responses provided by informants, these are usually converted into numbers and then averaged or analyzed with ordinary regression models. This approach has been criticized in the literature; one caveat (among others) is the assumption that distances between categories are known. The present paper illustrates how empirical insights into the perception of response labels may inform the design and analysis stage of a study. We start with a review of how ordinal scales are used in linguistic research. Our survey offers insights into typical scale layouts and analysis strategies, and it allows us to identify three commonly used rating dimensions (agreement, intensity, and frequency). We take stock of the experimental literature on the perception of relevant scale point labels and then demonstrate how psychometric insights may direct scale design and data analysis. This includes a careful consideration of measurement-theoretic and statistical issues surrounding the numeric-conversion approach to ordinal data. We focus on the consequences of these drawbacks for the interpretation of empirical findings, which will enable researchers to make informed decisions and avoid drawing false conclusions from their data. We present a case study on yous(e) in British and Scottish English, which shows that reliance on psychometric scale values can alter statistical conclusions, while also giving due consideration to the key limitations of the numeric-conversion approach to ordinal data analysis.

BackgroundNational Health Systems managers have been subject in recent years to considerable pressure to increase concentration and allow mergers. This pressure has been justified by a belief that larger hospitals lead to lower average costs and better clinical outcomes through the exploitation of economies of scale. In this context, the opportunity to measure scale efficiency is crucial to address the question of optimal productive size and to manage a fair allocation of resources.Methods and findingsThis paper analyses the stance of existing research on scale efficiency and optimal size of the hospital sector. We performed a systematic search of 45 past years (1969–2014) of research published in peer-reviewed scientific journals recorded by the Social Sciences Citation Index concerning this topic. We classified articles by the journal’s category, research topic, hospital setting, method and primary data analysis technique. Results showed that most of the studies were focussed on the analysis of technical and scale efficiency or on input / output ratio using Data Envelopment Analysis. We also find increasing interest concerning the effect of possible changes in hospital size on quality of care.ConclusionsStudies analysed in this review showed that economies of scale are present for merging hospitals. Results supported the current policy of expanding larger hospitals and restructuring/closing smaller hospitals. In terms of beds, studies reported consistent evidence of economies of scale for hospitals with 200–300 beds. Diseconomies of scale can be expected to occur below 200 beds and above 600 beds.

In the United States in 2022, the majority of diagnostic vendors only shared data to health information exchanges (HIE) on a regional or state level. While around ** percent said they contributed data to a private HIE.

The Atlas of Canada National Scale Data 1:5,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas medium scale (1:5,000,000 to 1:15,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

The Atlas of Canada National Scale Data 1:1,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas large scale (1:1,000,000 to 1:4,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

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.

Data archived here were used to create the Roosevelt Island Ice Core gas age and ice age time scales. Data include methane concentrations, nitrogen and oxygen isotope ratios of N2 and O2, total air content and the D/H ratio of the ice. Derived products included here include ice age and gas age time scales.

Scales are collected annually from smolt trapping operations in Maine as wellas other sampling opportunities (e.g. marine surveys, fishery sampling etc.). Scale samples are imaged and age, origin, and measurement data are collected as needed for specific growth-related research.

The natural amenities scale is a measure of the physical characteristics of a county area that enhance the location as a place to live. The scale was constructed by combining six measures of climate, topography, and water area that reflect environmental qualities most people prefer. These measures are warm winter, winter sun, temperate summer, low summer humidity, topographic variation, and water area. The data are available for counties in the lower 48 States. The file contains the original measures and standardized scores for each county as well as the amenities scale.

The Q-Herilearn scale is a probabilistic scale of summative estimates that measures different aspects of the learning process in Heritage Education. It consists of seven factors (Knowing, Understanding, Respecting, Valuing, Caring, Enjoying and Transmitting). Each dimension is measured by means of seven indicators scored on a 4-point frequency response scale (1 = Never or almost never; 2 = Sometimes; 3 = Quite often; 4 = Always or almost always). Sufficient evidence of content validity has been obtained through a concordance analysis —which employed multi-facet logistic models (Many Facet Rasch Model MFRM)— of the scores of 40 judges, who estimated the relevance, adequacy, and clarity of each item. The metric properties of the scores were determined using ESEM —Exploratory Structural Equation Modeling—, EGA Exploratory Graph Analysis and Network Analysis. The scale was calibrated using Item Response Theory models: the Nominal Response Model and the Graded Response Model.

Context

This dataset was inspired by the experiments conducted by Siegler, which investigate the different levels of knowledge and learning of children. They had to deduce the physical law behind the balance and predict if it would tilt to the left or to the right, or if would be balanced. Can a computer deduce this laws based on images?

Content

Directory named "Images" containing all possible combinations of 5 weights and distances. The filenames have the following structure:

"LD_{i}_LW_{j}_RD_{k}_RW_{n}.png" i: Left Distance j: Left Weight k: Right Distance n: RIght Weight

Inspiration

Klahr, David, and Robert S. Siegler. "The representation of children's knowledge." Advances in child development and behavior. Vol. 12. JAI, 1978. 61-116. Dataset that I took inspiration from.

Hyper-scale Data Center Market size was valued at USD 37,500.50 million in 2024 and the revenue is expected to grow at a CAGR of 10.8% from 2025 to 2032

Questions, answers, and metadata collected from 15035 Consideration of Future Consequences Scales. The data was hosted on OpenPsychometrics.org a nonprofit effort to educate the public about psychology and to collect data for psychological research. Their notes on the data collected in the codebook.txt

From Wikipedia:

The consideration of future consequences (CFC) is a personality trait defined as the extent to which individuals consider the potential future outcomes of their current behaviour and the extent to which they are influenced by the imagined outcomes. Individuals who score highly on a measure such as the Consideration of Future Consequences Scale typically focus on the future implications of their behaviour, whereas those low on CFC typically focus more on their immediate needs and concerns.

Comprehensive dataset containing 807 verified Scale supplier businesses in United States with complete contact information, ratings, reviews, and location data.

This repository contains all of the data used in the manuscript "Linearizing the vertical scale of an interferometric microscope and its effect on step-height measurement," by Thomas A. Germer, T. Brian Renegar, Ulf Griesmann, and Johannes A. Soons, which has been published in Surface Topography: Metrology and Properties volume 12, number 2, article 025012 on 8 May 2024. The repository also contains a Python Jupyter notebook that performs the analysis of the data and generates the figures in the manuscript.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

Between January and September 2024, healthcare organizations in the United States saw 491 large-scale data breaches, resulting in the loss of over 500 records. This figure has increased significantly in the last decade. To date, the highest number of large-scale data breaches in the U.S. healthcare sector was recorded in 2023, with a reported 745 cases.

This data set contains demographic information (i.e., gender, age, ethnicity, educational attainment, employment status, relationship status, and housing status) and Perceived Stress Scale scores. Data was collected during the first week of the Text4Hope program launch.

Research Hypothesis: Using the concept of Neutrosophy to deal Indeterminacy in Feedback

Data: Feedback given by customers of a restaurant. Questionnaire based on six factors, i.e., Quality of Food, Service, Hygiene, Value for money, Ambiance, Overall Experience. Each question (based on the factor) has five membership values as follows: , Positive, Positive Indeterminate, Indeterminate, Negative Indeterminate and Negative.