ObjectiveTo examine the use of the term ‘metric’ in health and social sciences’ literature, focusing on the interval scale implication of the term in Modern Test Theory (MTT).Materials and methodsA systematic search and review on MTT studies including ‘metric’ or ‘interval scale’ was performed in the health and social sciences literature. The search was restricted to 2001–2005 and 2011–2015. A Text Mining algorithm was employed to operationalize the eligibility criteria and to explore the uses of ‘metric’. The paradigm of each included article (Rasch Measurement Theory (RMT), Item Response Theory (IRT) or both), as well as its type (Theoretical, Methodological, Teaching, Application, Miscellaneous) were determined. An inductive thematic analysis on the first three types was performed.Results70.6% of the 1337 included articles were allocated to RMT, and 68.4% were application papers. Among the number of uses of ‘metric’, it was predominantly a synonym of ‘scale’; as adjective, it referred to measurement or quantification. Three incompatible themes ‘only RMT/all MTT/no MTT models can provide interval measures’ were identified, but ‘interval scale’ was considerably more mentioned in RMT than in IRT.Conclusion‘Metric’ is used in many different ways, and there is no consensus on which MTT metric has interval scale properties. Nevertheless, when using the term ‘metric’, the authors should specify the level of the metric being used (ordinal, ordered, interval, ratio), and justify why according to them the metric is at that level.

Likert scales are useful for collecting data on attitudes and perceptions from large samples of people. In particular, they have become a well-established tool in soundscape studies for conducting in situ surveys to determine how people experience urban public spaces. However, it is still unclear whether the metrics of the scales are consistently interpreted during a typical assessment task. The current work aims at identifying some general trends in the interpretation of Likert scale metrics and introducing a procedure for the derivation of metric corrections by analyzing a case study dataset of 984 soundscape assessments across 11 urban locations in London. According to ISO/TS 12913-2:2018, soundscapes can be assessed through the scaling of 8 dimensions: pleasant, annoying, vibrant, monotonous, eventful, uneventful, calm, and chaotic. The hypothesis underlying this study is that a link exists between correlations across the percentage of assessments falling in each Likert scale category and a dilation/compression factor affecting the interpretation of the scales metric. The outcome of this metric correction value derivation is introduced for soundscape, and a new projection of the London soundscapes according to the corrected circumplex space is compared with the initial ISO circumplex space. The overall results show a general non-equidistant interpretation of the scales, particularly on the vibrant-monotonous direction. The implications of this correction have been demonstrated through a Linear Ridge Classifier task for predicting the London soundscape responses using objective acoustic parameters, which shows significant improvement when applied to the corrected data. The results suggest that the corrected values account for the non-equidistant interpretation of the Likert metrics, thereby allowing mathematical operations to be viable when applied to the data.

Contingency table of the combined use of ‘metric’ and ‘interval scale’ by paradigm.

This study developed interval-level measurement scales for evaluating police officer performance during real or simulated deadly force situations. Through a two-day concept mapping focus group, statements were identified to describe two sets of dynamics: the difficulty (D) of a deadly force situation and the performance (P) of a police officer in that situation. These statements were then operationalized into measurable Likert-scale items that were scored by 291 use of force instructors from more than 100 agencies across the United States using an online survey instrument. The dataset resulting from this process contains a total of 685 variables, comprised of 312 difficulty statement items, 278 performance statement items, and 94 variables that measure the demographic characteristics of the scorers.

Interval Data Analytics Market Outlook

According to our latest research, the global Interval Data Analytics market size reached USD 3.42 billion in 2024, demonstrating robust growth across key verticals. The market is expected to advance at a CAGR of 13.8% from 2025 to 2033, leading to a projected value of USD 10.13 billion by 2033. This impressive expansion is primarily driven by rising demand for advanced analytics solutions capable of processing time-stamped and interval-based data, especially as organizations seek to optimize operations, enhance predictive capabilities, and comply with evolving regulatory requirements.

One of the most significant growth factors propelling the Interval Data Analytics market is the exponential increase in data generation from IoT devices, smart meters, and connected infrastructure across industries. Organizations in sectors such as utilities, manufacturing, and healthcare are increasingly reliant on interval data for resource optimization, real-time monitoring, and predictive maintenance. The ability of interval data analytics to handle vast amounts of granular, time-series data enables businesses to uncover actionable insights, reduce operational costs, and improve asset utilization. Additionally, the growing adoption of smart grids and intelligent energy management systems further amplifies the need for sophisticated interval data analytics solutions that support real-time decision-making and regulatory compliance.

Another pivotal driver for the Interval Data Analytics market is the rapid digital transformation and integration of artificial intelligence (AI) and machine learning (ML) technologies into analytics platforms. These advancements allow for more accurate forecasting, anomaly detection, and automated response mechanisms, which are critical in sectors like finance, healthcare, and telecommunications. As organizations continue to prioritize data-driven strategies, the demand for interval data analytics tools that can seamlessly integrate with existing IT ecosystems and provide scalable, cloud-based solutions is accelerating. Furthermore, the shift towards cloud computing and the proliferation of big data platforms are making it easier for enterprises of all sizes to deploy and scale interval data analytics capabilities, thus broadening the market's reach and potential.

Regulatory pressures and the increasing need for transparency and accountability in data handling are also fueling the growth of the Interval Data Analytics market. Industries such as banking and financial services, healthcare, and energy are subject to stringent compliance requirements that necessitate precise monitoring and reporting of interval data. The ability of interval data analytics platforms to provide auditable, time-stamped records and support regulatory reporting is becoming a critical differentiator for vendors in this space. Moreover, as data privacy laws evolve and enforcement intensifies, organizations are investing in analytics solutions that offer robust security features, data lineage tracking, and comprehensive audit trails, further boosting market adoption.

From a regional perspective, North America continues to lead the Interval Data Analytics market, driven by early technology adoption, a strong presence of leading analytics vendors, and substantial investments in digital infrastructure. However, the Asia Pacific region is rapidly emerging as a key growth engine, fueled by large-scale digitalization initiatives, expanding industrial automation, and increasing penetration of IoT devices. Europe also represents a significant market, underpinned by regulatory mandates and a mature industrial base. Latin America and the Middle East & Africa, while currently smaller in market share, are witnessing accelerated adoption as organizations in these regions recognize the value of interval data analytics in enhancing operational efficiency and competitiveness.

Component Analysis

The Interval Data Analytics market is segmented by component into software and services, each playing a distinct role in

Dataset Title: Data and Code for: "Universal Adaptive Normalization Scale (AMIS): Integration of Heterogeneous Metrics into a Unified System" Description: This dataset contains source data and processing results for validating the Adaptive Multi-Interval Scale (AMIS) normalization method. Includes educational performance data (student grades), economic statistics (World Bank GDP), and Python implementation of the AMIS algorithm with graphical interface. Contents: - Source data: educational grades and GDP statistics - AMIS normalization results (3, 5, 9, 17-point models) - Comparative analysis with linear normalization - Ready-to-use Python code for data processing Applications: - Educational data normalization and analysis - Economic indicators comparison - Development of unified metric systems - Methodology research in data scaling Technical info: Python code with pandas, numpy, scipy, matplotlib dependencies. Data in Excel format.

Yearly citation counts for the publication titled "Derivation and testing of an interval-level score for measuring locomotor disability in epidemiological studies of middle and old age".

SAIVT-BuildingMonitoring

Overview

The SAIVT-BuildingMonitoring database contains footage from 12 cameras capturing a single work day at a busy university campus building. A portion of the database has been annotated for crowd counting and pedestrian throughput estimation, and is freely available for download. Contact Dr Simon Denman for more information.

Licensing

The SAIVT-BuildingMonitoring database is © 2015 QUT, and is licensed under the Creative Commons Attribution-ShareAlike 4.0 License.

Attribution

To attribute this database, use the citation provided on our publication at eprints:

S. Denman, C. Fookes, D. Ryan, & S. Sridharan (2015) Large scale monitoring of crowds and building utilisation: A new database and distributed approach. In 12th IEEE International Conference on Advanced Video and Signal Based Surveillance, 25-28 August 2015, Karlsruhe, Germany.

Acknowledgement in publications

In addition to citing our paper, we kindly request that the following text be included in an acknowledgements section at the end of your publications:

'We would like to thank the SAIVT Research Labs at Queensland University of Technology (QUT) for freely supplying us with the SAIVT-BuildingMonitoring database for our research'.

Installing the SAIVT-BuildingMonitoring Database

Download, join, and unzip the following archives

Annotated Data

  Part 1 (2GB, md5sum: 50e63a6ee394751fad75dc43017710e8)
  Part 2 (2GB, md5sum: 49859f0046f0b15d4cf0cfafceb9e88f)
  Part 3 (2GB, md5sum: b3c7386204930bc9d8545c1f4eb0c972)
  Part 4 (2GB, md5sum: 4606fc090f6020b771f74d565fc73f6d)
  Part 5 (632 MB, md5sum: 116aade568ccfeaefcdd07b5110b815a)


Full Sequences

  Part 1 (2 GB, md5sum: 068ed015e057afb98b404dd95dc8fbb3)
  Part 2 (2GB, md5sum: 763f46fc1251a2301cb63b697c881db2)
  Part 3 (2GB, md5sum: 75e7090c6035b0962e2b05a3a8e4c59e)
  Part 4 (2GB, md5sum: 34481b1e81e06310238d9ed3a57b25af)
  Part 5 (2GB, md5sum: 9ef895c2def141d712a557a6a72d3bcc)
  Part 6 (2GB, md5sum: 2a76e6b199dccae0113a8fd509bf8a04)
  Part 7 (2GB, md5sum: 77c659ab6002767cc13794aa1279f2dd)
  Part 8 (2GB, md5sum: 703f54f297b4c93e53c662c83e42372c)
  Part 9 (2GB, md5sum: 65ebdab38367cf22b057a8667b76068d)
  Part 10 (2GB, md5sum: bb5f6527f65760717cd819b826674d83)
  Part 11 (2GB, md5sum: 01a562f7bd659fb9b81362c44838bfb1)
  Part 12 (2GB, md5sum: 5e4a0d4bb99cde17158c1f346bbbdad8)
  Part 13 (2GB, md5sum: 9c454d9381a1c8a4e8dc68cfaeaf4622)
  Part 14 (2GB, md5sum: 8ff2b03b22d0c9ca528544193599dc18)
  Part 15 (2GB, md5sum: 86efac1962e2bef3afd3867f8dda1437)

To rejoin the invidual parts, use:

cat SAIVT-BuildingMonitoring-AnnotatedData.tar.gz.* > SAIVT-BuildingMonitoring-AnnotatedData.tar.gz

cat SAIVT-BuildingMonitoring-FullSequences.tar.gz.* > SAIVT-BuildingMonitoring-FullSequences.tar.gz

At this point, you should have the following data structure and the SAIVT-BuildingMonitoring database is installed:

SAIVT-BuildingMonitoring +-- AnnotatedData +-- P_Lev_4_Entry_Way_ip_107 +-- Frames +-- Entry_ip107_00000.png +-- Entry_ip107_00001.png +-- ... +-- GroundTruth.xml +-- P_Lev_4_Entry_Way_ip_107-20140730-090000.avi +-- perspectivemap.xml +-- ROI.xml

+-- P_Lev_4_external_419_ip_52 +-- ...

+-- P_Lev_4_External_Lift_foyer_ip_70 +-- Frames +-- Entry_ip107_00000.png +-- Entry_ip107_00001.png +-- ... +-- GroundTruth.xml +-- P_Lev_4_External_Lift_foyer_ip_70-20140730-090000.avi +-- perspectivemap.xml +-- ROI.xml +-- VG-GroundTruth.xml +-- VG-ROI.xml

+-- ...

+-- Calibration +-- Lev4Entry_ip107.xml +-- Lev4Ext_ip51.xml +-- ...

+-- FullSequences +-- P_Lev_4_Entry_Way_ip_107-20140730-090000.avi +-- P_Lev_4_external_419_ip_52-20140730-090000.avi +-- ...

+-- MotionSegmentation +-- Lev4Entry_ip107.avi +-- Lev4Entry_ip107-Full.avi +-- Lev4Ext_ip51.avi +-- Lev4Ext_ip51-Full.avi +-- ...

+-- Denman 2015 - Large scale monitoring of crowds and building utilisation.pdf +-- LICENSE.txt +-- README.txt

Data is organised into two sections, AnnotatedData and FullSequences. Additional data that may be of use is provided in Calibration and MotionSegmentation.

AnnotatedData contains the two hour sections that have been annotated (from 11am to 1pm), alongside the ground truth and any other data generated during the annotation process. Each camera has a directory, the contents of which depends on what the camera has been annotated for.

All cameras will have:

a video file, such as P_Lev_4_Entry_Way_ip_107-20140730-090000.avi, which is the 2 hour video from 11am to 1pm
a Frames directory, that has 120 frames taken at minute intervals from the sequence. There are the frames that have been annotated for crowd counting. Even if the camera has not been annotated for crowd counting (i.e. P_Lev_4_Main_Entry_ip_54), this directory is included.

The following files exist for crowd counting cameras:

GroundTruth.xml, which contains the ground truth in the following format: 

The file contains a list of annotated frames, and the location of the approximate centre of mass of any people within the frame. The interval-scale attribute indicates the distance between the annotated frames in the original video.

perspectivemap.xml, a file that defines the perspective map used to correct for perspective distortion. Parameters for a bilinear perspective map are included along with the original annotations that were used to generate the map.
ROI.xml, which defines the region of interest as follows:

This defines a polygon within the image that is used for crowd counting. Only people within this region are annotated.

For cameras that have been annotated with a virtual gate, the following additional files are present:

VG-GroundTruth.xml, which contains ground truth in the following format: 

The ROI is repeated within the ground truth, and a direction of interest (the tag) is also included, which indicates the primary direction for the gait (i.e. the direction that denotes a positive count. Each pedestrian crossing is represented by a tag, which contains the approximate frame the crossing occurred in (when the centre of mass was at the centre of the gait region), the x and y location of the centre of mass of the person during the crossing, and the direction (0 being the primary direction, 1 being the secondary). VG-ROI.xml, which contains the region of interest for the virtual gate

The Calibration directory contains camera calibration for the cameras (with the exception of ip107, which has an uneven ground plane and is thus difficult to calibrate). All calibration is done using Tsai's method.

FullSequences contains the full sequences (9am - 5pm) for each of the cameras.

MotionSegmentation contains motion segmentation videos for all clips. Segmentation videos for both the full sequences and the 2 hour annotated segments are provided. Motion segmentation is done using the ViBE algorithm. Motion videos for the entire sequence have Full in the file name before the extension (i.e. Lev4Entry_ip107-Full.avi).

Further information on the SAIVT-BuildingMonitoring database in our paper: S. Denman, C. Fookes, D. Ryan, & S. Sridharan (2015) Large scale monitoring of crowds and building utilisation: A new database and distributed approach. In 12th IEEE International Conference on Advanced Video and Signal Based Surveillance, 25-28 August 2015, Karlsruhe, Germany.

This paper is also available alongside this document in the file: 'Denman 2015 - Large scale monitoring of crowds and building utilisation.pdf'.

To help determine when winter conditions were changing to spring conditions annually in our four study areas, we determined the first eight-day interval (in accordance with the scale limitations of satellite data we used to assess the presence of snow) during which the first amphibian of the season called at each of our study wetlands in those areas. To do this, we examined contour plots of summaries of all the acoustic data we collected at that site in a given year to identify the unique call signatures of individual amphibian species by date and time. When necessary due to potential confounding on a contour plot, we also examined relevant individual five-minute recordings aurally and visually to confirm whether a call occurred. When we confirmed the date of the first call we recorded in a given season, we identified the eight-day interval in which that date fell, with the first such interval beginning on January 1 of each year.

Encuesta de Fecundidad: Fem., 15-49 years, married sometime, at least one child live birth level of studies duration of protogenesic interval. National. Females by level of studies and duration of protogenesic interval (rel.fig.).

Companion data for the creation of a banksia plot:

Background:

In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.

Methods:

The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.

Results:

In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.

Conclusions:

The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.

This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1

First as a noun, then as adjective/adverb.

Encuesta de Fecundidad: Fem.marri. somet., no pregn., no ster.,and who can have children level of studies duration of last open interval between births. National. Females by level of studies and duration open interval between births.

Encuesta de Fecundidad: Females married sometime, with a live birth or pregnant level of studies duration of last closed interval between births. National. Fem. by lev. of stud. and durat. last closed interv. between births.

The Analysis Scales market plays a crucial role across various industries, providing standardized measures that enable researchers, businesses, and educators to quantify complex phenomena reliably. Analysis scales, which include nominal, ordinal, interval, and ratio scales, are utilized to assess data in fields such

Synonyms and definitions of ‘metric’.

Contingency table of paradigm by Type.

Themes exposing the incompatible views concerning the ability to obtain interval measures using RMT and IRT metrics.

Understanding of interactional dynamics between several processes is one of the most important challenges in psychology and psychosomatic medicine. Researchers exploring behavior or other psychological phenomena mostly deal with ordinal or interval data. Missing values and consequential non-equidistant measurements represent a general problem of longitudinal studies from this field. The majority of process-oriented methodologies was originally designed for equidistant data measured on ratio scales. Therefore, the goal of this article is to clarify the conditions for satisfactory performance of longitudinal methods with data typical in psychological and psychosomatic research. This study examines the performance of the Johansen test, a procedure incorporating a set of sophisticated time series techniques, in reference to data quality utilizing a Monte Carlo method. The main results of the conducted simulation studies are: (1) Time series analyses require samples of at least 70 observations for an accurate estimation and inference. (2) Discrete data and failing equidistance of measurements due to irregular missing values appear unproblematic. (3) Relevant characteristics of stationary processes can be adequately captured using 5- or 7-point ordinal scales. (4) For trending processes, at least 10-point scales are necessary to ensure an acceptable quality of estimation and inference.

The World Health Organisation Quality of Life (WHOQOL) questionnaires are widely used around the world and can claim strong cross-cultural validity due to their development in collaboration with international field centres. To enhance conceptual equivalence of quality of life across cultures, optional national items are often developed for use alongside the core instrument. The present study outlines the development of national items for the New Zealand WHOQOL-BREF. Focus groups with members of the community as well as health experts discussed what constitutes quality of life in their opinion. Based on themes extracted of aspects not contained in the existing WHOQOL instrument, 46 candidate items were generated and subsequently rated for their importance by a random sample of 585 individuals from the general population. Applying importance criteria reduced these items to 24, which were then sent to another large random sample (n = 808) to be rated alongside the existing WHOQOL-BREF. A final set of five items met the criteria for national items. Confirmatory factor analysis identified four national items as belonging to the psychological domain of quality of life, and one item to the social domain. Rasch analysis validated these results and generated ordinal-to-interval conversion algorithms to allow use of parametric statistics for domain scores with and without national items.