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.

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.

Cronbach’s alpha (coefficient α) is the conventional statistic communication scholars use to estimate the reliability of multi-item measurement instruments. For many, if not most communication measures, α should not be calculated for reliability estimation. Instead, coefficient omega (ω) should be reported as it aligns with the definition of reliability itself. In this primer, we review α and ω, and explain why ω should be the new ‘gold standard’ in reliability estimation. Using Mplus, we demonstrate how ω is calculated on an available data set and show how preliminary scales can be revised with ‘ω if item deleted.’ We also list several easy-to-use resources to calculate ω in other software programs. Communication researchers should routinely report ω instead of α.

Most equivalence scales that are applied in research on inequality do not depend on income, even though there is strong empirical evidence that equivalence scales are actually income-dependent. This paper explores the consistency of results derived from income-independent and income-dependent scales. We show that applying income-independent scales when income-dependent scales would be appropriate leads to violations of the transfer principle. Surprisingly, there are some exceptions, but these require unrealistic and strong assumptions. Thus, the use of income-dependent equivalence scales almost always leads to different assessments of inequality than the use of income-independent equivalence scales. Two examples illustrate our findings.

This is the extended data for the manuscript submitted to F1000 Research titled:

Precision medicine implementation and research-practice partnerships: implications of measurement scale differential item functioning (DIF).

The data provided includes redacted survey responses from a card sort exercise carried out to find out how the study participants sorted and ranked various measures of factors thought to influence precision medicine implementation at health systems level.

Performance of the Johansen test in time series measured on different scales.

These data are from soil salinity surveys conducted on California irrigated farmland between 1991 and 2017. The data consist of: (i.) geospatial field survey measurements of bulk soil electrical conductivity (ECa) and (ii.) laboratory determinations of soil salinity (ECe) and saturation percentage (SP) made on soil core sections extracted from the surveyed fields. The data consist of 277,624 ECa measurements and 8,575 ECe and SP determinations. Soil bulk electrical conductivity (ECa) is relatively easy to measure in agricultural fields using electromagnetic induction (EMI) instrumentation. EMI instruments are readily mobilized and thus can be used to characterize in detail the spatial variability of ECa within fields (Corwin, 2005; 2008). ECa is a useful property because it often correlates with difficult-to-measure soil physical and chemical properties that affect crop production, including soil water content, clay percentage, bulk density, PH, and especially soil salinity. The standard quantitative measure of soil salinity is defined to be the electrical conductivity of the soil saturation paste extract, or ECe (U.S. Salinity Laboratory Staff, 1954). Saturation percentage (SP) is the dry-weight moisture percentage of the saturation paste. The data can be used to test and explore model relationships between ECe, SP, and ECa (EMv and EMh), as well as their spatial variability. In particular, the data may be useful for comparing and testing modeling approaches that account for both deterministic and random components of soil spatial variability at single-field and multi-field scales, and to support high-resolution digital soil mapping studies across irrigated lands. Data Files Data are stored column-wise in two comma-delimited text files, ECe_USDA_ARS_USSL_v01.csv and ECa_USDA_ARS_USSL_v01.csv. Joining the files on the 'ID' column returns data for geolocations at which field measurements of ECa and laboratory determinations of ECe and SP both exist. For example: ECe <- read.csv('ECe_USDA_ARS_USSL_v01.csv') ECa <- read.csv('ECa_USDA_ARS_USSL_v01.csv') dat <- plyr::join(ECe, ECa, 'ID') plot3D::scatter3D(dat$ECe, dat$EMv_grd, dat$EMh_grd, zlab='EMh (dS/m)', xlab='ECe (dS/m)', ylab='EMv (dS/m)', clab = c("dS/m"), bty = "b2") Salinity Survey Identifiers (DATASET) The DATASET label in each file indicates the survey or field campaign from which the data are taken. DATASET_1. Survey of the Broadview Water District in California performed by Corwin and co-workers in 1991 (Corwin et al, 1999). Data include: (i.) ECe and SP determinations on 1,889 soil samples (depths) from 315 soil cores (locations) and (ii.) 2613 ECa (EMv and EMh) field measurements. Data from this survey have been used previously for interpreting the spatial variability of soil salinity at the regional scale (Corwin, 2005). DATASET_2. Survey of Coachella Valley, California farmland conducted between 2005 and 2008 and led by the Coachella Water District. Data consist of: (i.) ECe and SP determinations on 2,088 samples from 476 soil cores and (ii.) 133,037 ECa (EMv and EMh) measurements across the Coachella Valley. This dataset has been used in previous work for validating linear approaches to regional-scale ECa and ECe calibration (Corwin and Lesch, 2014). DATASET_3. Survey led by Singh and colleagues across four fields in western San Joaquin Valley for the purpose of assessing environmental risk associated with saline drainage (Singh et al,. 2020). Data include: (i.) ECe and SP determinations on 1,080 samples from 273 soil cores and (ii.) 36,236 ECa (EMv and EMh) field measurements. DATASET_4. Soil salinity survey led by USDA-ARS U.S. Salinity Laboratory between 2012 and 2013. The survey covered 21 fields in San Joaquin Valley, California. Data consist of: (i.) ECe and SP determinations on 1,634 samples from 180 soil cores and (ii.) 63,225 ECa (EMv and EMh) field measurements. These data were used previously for large scale soil salinity assessments and is described in detail by Scudiero et al. (2014). DATASET_5. Data from surveys of 6 miscellaneous fields in California led by the USDA-ARS U.S. Salinity Laboratory. Data consist of: (i.) 244 determinations of ECe and SP on samples taken from 62 soil cores and (ii.) 62 corresponding ECa (EMv and EMh) field measurements. DATASET_6. Soil salinity surveys led by the USDA-ARS U.S. Salinity Laboratory between 1999 and 2012. One field in southern San Joaquin Valley was assessed several times over many years. Data consist of: (i.) ECe and SP determinations on 1,640 samples from 239 soil cores and (ii.) 42,458 ECa (EMv and EMh) field measurements. These data have been used in previous works focusing on long-term and short-term monitoring and mapping of the spatial and temporal variability of soil salinity (Corwin, 2008, Corwin, 2012, Scudiero et al., 2017). Majority funding provided by USDA-ARS Office of National Programs. Additional funding provided by Office of Naval Research (No. 3200001344), Coachella Valley Resource Conservation District (No. 09FG340003), and California Department of Water Resources (No. 4600011273). References Corwin, D.L. (2005). Geospatial Measurement of Apparent Soil Electrical Conductivity for Characterizing Soil Spatial Variability. doi: 10.1201/9781420032086 (Chapter 18) Corwin, D.L. (2008). Past, present, and future trends of soil electrical conductivity measurement using geophysical methods. Handbook of Agricultural Geophysics, CRC Press. Corwin, D.L. (2012). Field-scale monitoring of the long-term impact and sustainability of drainage water reuse on the west side of California's San Joaquin Valley. Journal of Environmental Monitoring 14(6), 1576-1596. doi: 10.1039/c2em10796a. Corwin, D.L., Carrillo, M.L.K., Vaughan, P.J., Rhoades, J.D., Cone, D.G. (1999). Evaluation of a GIS-linked model of salt loading to groundwater. Journal of Environmental Quality 28(2), 471-480. doi: 10.2134/jeq1999.00472425002800020012x. Corwin, D.L., Lesch, S. (2014). A simplified regional-scale electromagnetic induction: Salinity calibration model using ANOCOVA modeling techniques. Geoderma. s 230-231. 288-295. 10.1016/j.geoderma.2014.03.019. Scudiero, E., Skaggs, T., Corwin, D.L. (2014). Regional Scale Soil Salinity Evaluation Using Landsat 7, Western San Joaquin Valley, California, USA. Geoderma Regional. 2-3. 82-90. 10.1016/j.geodrs.2014.10.004. Scudiero, E., Skaggs, T. H., Corwin, D. L. (2017). Simplifying field-scale assessment of spatiotemporal changes of soil salinity. Sci. Total Environ., 587–588:273–281. doi:10.1016/j.scitotenv.2017.02.136. Singh, A., Quinn, N.W.T., Benes, S.E., Cassel, F. (2020). Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California. Sustainability 12(16), 6362. U.S. Salinity Laboratory Staff. 1954. Diagnosis and improvement of saline and alkali soils. USDA Agric. Handbook. 60. U.S. Gov. Print. Office, Washington, DC.

This dataset provides the raw inertial data (time, accelerations and angular velocities) used in [1]. The measurements were performed using multiple AHRS Xsens MTi-7 GNSS/INS sensors, during the the first ever full-scale floating installation of an offshore wind turbine tower, in the context of the FOX project. This project was a collaboration between Delft University of Technology, Delft Offshore Turbine and Heerema Marine Contractors.

[1] Domingos, D., Atzampou, P., Meijers, P., Beirão, S., Metrikine, A., van Wingerden, J.W., Wellens, P., 2024. "Full-scale measurements and analysis of the floating installation of an offshore wind turbine tower".

This data set presents snow depth, snow water equivalence (SWE), snow wetness data, and snow pit data from two pine sites and a small clearing at the Local Scale Observation Site (LSOS) of the Cold Land Processes Field Experiment (CLPX) in northern Colorado.

The SPI overall score is a composite score measuring country performance across five pillars: data use, data services, data products, data sources, and data infrastructure. The new Statistical Performance Indicators (SPI) will replace the Statistical Capacity Index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community.

Overview: This is a large-scale dataset with impedance and signal loss data recorded on volunteer test subjects using low-voltage alternate current sine-shaped signals. The signal frequencies are from 50 kHz to 20 MHz.

Applications: The intention of this dataset is to allow to investigate the human body as a signal propagation medium, and capture information related to how the properties of the human body (age, sex, composition etc.), the measurement locations, and the signal frequencies impact the signal loss over the human body.

Overview statistics:

Number of subjects: 30

Number of transmitter locations: 6

Number of receiver locations: 6

Number of measurement frequencies: 19

Input voltage: 1 V

Load resistance: 50 ohm and 1 megaohm

Measurement group statistics:

Height: 174.10 (7.15)

Weight: 72.85 (16.26)

BMI: 23.94 (4.70)

Body fat %: 21.53 (7.55)

Age group: 29.00 (11.25)

Male/female ratio: 50%

Included files:

experiment_protocol_description.docx - protocol used in the experiments

electrode_placement_schematic.png - schematic of placement locations

electrode_placement_photo.jpg - visualization on the experiment, on a volunteer subject

RawData - the full measurement results and experiment info sheets

all_measurements.csv - the most important results extracted to .csv

all_measurements_filtered.csv - same, but after z-score filtering

all_measurements_by_freq.csv - the most important results extracted to .csv, single frequency per row

all_measurements_by_freq_filtered.csv - same, but after z-score filtering

summary_of_subjects.csv - key statistics on the subjects from the experiment info sheets

process_json_files.py - script that creates .csv from the raw data

filter_results.py - outlier removal based on z-score

plot_sample_curves.py - visualization of a randomly selected measurement result subset

plot_measurement_group.py - visualization of the measurement group

CSV file columns:

subject_id - participant's random unique ID

experiment_id - measurement session's number for the participant

height - participant's height, cm

weight - participant's weight, kg

BMI - body mass index, computed from the valued above

body_fat_% - body fat composition, as measured by bioimpedance scales

age_group - age rounded to 10 years, e.g. 20, 30, 40 etc.

male - 1 if male, 0 if female

tx_point - transmitter point number

rx_point - receiver point number

distance - distance, in relative units, between the tx and rx points. Not scaled in terms of participant's height and limb lengths!

tx_point_fat_level - transmitter point location's average fat content metric. Not scaled for each participant individually.

rx_point_fat_level - receiver point location's average fat content metric. Not scaled for each participant individually.

total_fat_level - sum of rx and tx fat levels

bias - constant term to simplify data analytics, always equal to 1.0

CSV file columns, frequency-specific:

tx_abs_Z_... - transmitter-side impedance, as computed by the process_json_files.py script from the voltage drop

rx_gain_50_f_... - experimentally measured gain on the receiver, in dB, using 50 ohm load impedance

rx_gain_1M_f_... - experimentally measured gain on the receiver, in dB, using 1 megaohm load impedance

Acknowledgments: The dataset collection was funded by the Latvian Council of Science, project “Body-Coupled Communication for Body Area Networks”, project No. lzp-2020/1-0358.

References: For a more detailed information, see this article: J. Ormanis, V. Medvedevs, A. Sevcenko, V. Aristovs, V. Abolins, and A. Elsts. Dataset on the Human Body as a Signal Propagation Medium for Body Coupled Communication. Submitted to Elsevier Data in Brief, 2023.

Contact information: info@edi.lv

This dataset contains digitized scale measurement data from sockeye salmon scales taken in Alaska. The locations where scales were sampled are the Copper River, Coghill River, Egegik River, and Hugh Smith Lake. Salmon scales are aged by examining growth annuli on the scale. Sockeye salmon typically spend one to two winters in fresh water, and one to three winters in the ocean prior to returning to freshwater to spawn. Growth during summer months is rapid, and increases the size of the scale by widely spaced circular rings (circuli). Slow growth during winter months leads to more narrowly spaced circuli. In this dataset, measurements are presented as the length (in millimeters) between each circuli. Accompanying these individual measurements is the established age of the fish (in European notation, where a 13 would be 1 year in fresh water and 3 in the ocean), the sex of the fish, and the length of the fish. Included in this dataset are the original excel files for each aged stock, an Rmarkdown document which reformats and merges these files, and a csv file of all of the data contained within the excel files. The included PDF document details the methods for measuring annuli more clearly.

This record provides raw and post-processed data used in the associated paper "A technique for in-situ displacement and strain measurement with laboratory-scale X-ray Computed Tomography." The codes used are provided in a separate software publication "SerialTrackXR", also referenced. The data consist of 3D X-ray computed tomography (X-ray CT) scans, projection images, and load/displacement data of two additively manufactured tensile test coupons made from IN718 with different processing conditions. The 3D images were collected in-situ at progressively increasing levels of applied displacement. The projection images track this displacement both total and as surface maps. Load/displacement data from the load frame used to apply displacement are also provided. A displacement tracking validation dataset, consisting of known rigid body displacements imposed on a nominally un-deformed third test specimen is also included.The X-ray CT data are rather large, each .tiff stack being about 2 GB; the displacement and strain map files are also >1 GB. Other data are relatively smaller. The dataset consists of 170 files, totaling 69.9 GB (as noted above, much of this space is in 3D images and raw data for the 3D images - most users will not need to interact with those raw data).

Discrimination based on skin color, referred to as colorism, has been documented as a con- siderable problem in social science research. Most of this research relies on Likert-type ratings of skin color. For example, the widely used “Massey Martin Scale” (MMS) requires coders to rate subjects on a scale from 1-10, based on the similarity between the subject’s skin tone and ten shades of skin color on a palette. Some scholars have raised questions about measurement error in Likert-type skin color scales. It’s been shown, for example, that Black and White coders apply the MMS differently. We hypothesize that the coding of a person’s skin color will vary depending on the race of persons previously coded. To test this hypothesis, we conducted an experiment using a convenience sample of Mturk workers as coders. We find that the MMS is vulnerable to spillover effects: a person’s skin is coded as “darker,” on average, if he is ob- served following a sequence of White persons than if he is observed following a sequence of Black persons. We also replicate previous work showing that Black and White coders use the scale differently. Finally, having coders cross-reference the palette at the time of coding, rather than recalling the palette from memory, fails to mitigate either race-of-coder or spillover effects. We provide suggestive evidence that use of a pairwise-comparisons approach may overcome some of the issues associated with Likert-type ratings of skin color.

The global railroad track scales market is experiencing robust growth, driven by increasing freight transportation volumes, stringent safety regulations, and the need for efficient railway operations. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This growth is fueled by several key factors, including the expansion of railway networks in developing economies, the rising adoption of advanced weighing technologies (digital scales offering improved accuracy and data integration), and the growing demand for predictive maintenance solutions within the railway sector. The market segmentation reveals a significant share held by digital scales, reflecting the industry's transition towards automation and data-driven decision-making. Applications within maintenance and construction activities account for a substantial portion of the demand, emphasizing the crucial role of accurate weight measurement in ensuring railway infrastructure integrity and safety. Major players such as Standard Scale & Supply Company, Avery Weigh-Tronix, and Rice Lake Weighing Systems are actively shaping the market landscape through continuous innovation and expansion of their product portfolios. Regional variations in market size are influenced by factors including railway infrastructure development, economic growth, and government regulations. North America and Europe currently hold significant market shares, but Asia-Pacific is expected to witness rapid growth due to large-scale infrastructure investments. Despite the positive outlook, challenges such as high initial investment costs for advanced systems and the need for skilled labor for installation and maintenance might pose some restraints to market expansion. However, ongoing technological advancements and favorable government policies are likely to mitigate these challenges over the forecast period. This comprehensive report provides an in-depth analysis of the global railroad track scales market, projected to be worth over $2 billion by 2028. We delve into market concentration, technological advancements, regulatory impacts, and future growth potential, providing invaluable insights for industry stakeholders. This report utilizes data from reputable sources, employing reasonable estimations for figures not publicly available while maintaining transparency.

The residential body composition scale market is experiencing robust growth, driven by increasing health consciousness among consumers and the rising prevalence of obesity and related health issues globally. The market's expansion is fueled by technological advancements leading to more accurate and user-friendly scales, offering insights beyond basic weight measurement. Smart scales, integrating with mobile apps and offering detailed body composition analysis including body fat percentage, muscle mass, bone mass, and hydration levels, are gaining significant traction. This trend is further propelled by the rising adoption of wearable technology and fitness tracking apps, fostering a holistic approach to health management. The market is segmented by application (online vs. offline sales) and type (smart vs. normal scales), with smart scales commanding a larger and rapidly growing share due to their advanced features and data-driven insights. While the initial cost of smart scales might be higher than traditional scales, the long-term value proposition of personalized health data and continuous monitoring is driving adoption. Competition is intense, with established players like Tanita and Omron alongside newer entrants offering innovative features and competitive pricing. Geographic growth is diverse, with North America and Europe currently holding significant market shares, but Asia-Pacific is predicted to experience substantial growth in the coming years due to rising disposable incomes and increasing health awareness in developing economies. Despite challenges such as potential inaccuracies in certain measurement technologies and the need for user compliance in achieving accurate data, the market outlook remains positive, anticipating strong growth throughout the forecast period. The ongoing market expansion will likely be influenced by factors such as evolving consumer preferences towards personalized health solutions, increased integration with health and fitness apps, and further advancements in sensor technology leading to more precise and reliable measurements. Regulatory changes concerning data privacy and security are also expected to play a role in shaping the market landscape. Companies are likely to focus on product innovation, strategic partnerships, and expansion into emerging markets to maintain a competitive edge. The increasing availability of affordable, yet feature-rich, body composition scales is expected to further broaden market penetration across diverse demographics. The growth in online sales channels presents significant opportunities for companies to reach a wider audience and offer personalized recommendations based on user data. Future trends point to increased focus on user experience through intuitive app interfaces and more accessible data visualization tools.

These data were compiled to evaluate the impact of a utility-scale solar energy (Gemini Solar + Storage) development on ecosystem function in the Mojave Desert. Objectives of our study were to identify the short-term effects of construction on soils and vegetation across plant communities and soil types. These data represent a before-after control-impact study to evaluate the effects of utility-scale solar energy development on plant communities and soil ecosystem functioning in the Mojave Desert. These data were collected in the Mojave Desert, approximately 50 km northeast of Las Vegas, Nevada on land administered by the Bureau of Land Management (BLM) between 2021 and 2023. These data were collected by researchers at the U.S. Geological Survey, Southwest Biological Science Center using field observation and sampling protocols for the BLM. These data can be used for before-after control-impact evaluation of the construction of the Gemini solar facility on plant and soil variables.

This article explores to what extent the poor results that are often found when estimating parameters of production functions can be attributed to measurement errors, due to the use of common price deflators across firms. Because of the lack of detailed micro-economic data, econometricians have to rely on industry-wide deflators when computing outputs and intermediate inputs. A unique feature of the longitudinal data used in this paper is that it reports firm-level prices. This allows for a comparative assessment of production function parameters where the outputs and intermediate inputs are computed using both firm-specific prices and industry-wide deflators. The empirical results presented in this paper show that the use of common deflators across firms leads to lower scale estimates, mainly because of a large downward bias in the estimated coefficients for labour.

The global industrial drum scale market is experiencing robust growth, driven by the increasing automation in warehousing, manufacturing, and distribution centers. The rising demand for precise weight measurement in various industries, coupled with stringent regulatory compliance requirements regarding accurate inventory management and product quality control, fuels market expansion. Significant growth is observed across diverse application segments, including warehouses, factories, and distribution centers, with warehouses showing particularly strong adoption due to the need for efficient inventory management and order fulfillment. Stainless steel drum scales maintain a dominant market share due to their durability, corrosion resistance, and suitability for handling various materials. However, carbon steel scales continue to hold a significant presence, particularly in applications where cost-effectiveness is a priority. The market is competitive, with key players like Avery Weigh-Tronix, OHAUS, and Fairbanks Scales vying for market share through product innovation, strategic partnerships, and geographic expansion. North America and Europe currently represent significant market segments, but Asia-Pacific is projected to experience substantial growth due to rapid industrialization and rising investments in logistics infrastructure. Factors like fluctuating raw material prices and economic downturns pose potential restraints, but the overall market outlook remains positive, driven by the long-term trends of automation and improved supply chain management. The forecast period from 2025 to 2033 projects continued market expansion, with a Compound Annual Growth Rate (CAGR) estimated at approximately 7%. This growth is projected to be fueled by increasing adoption of advanced features such as data connectivity and integration with enterprise resource planning (ERP) systems. The market segmentation will likely remain relatively stable, though a potential shift towards higher adoption of smart scales with enhanced data analytics capabilities is anticipated. The competitive landscape will continue to evolve, with mergers, acquisitions, and the introduction of innovative products shaping the dynamics of the market. Regional growth will vary, with emerging economies in Asia-Pacific showing the most significant potential for expansion. However, established markets in North America and Europe will continue to contribute significantly to overall market revenue.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

A dual frame (landline and mobile phone frames) was used to complete 1,000 telephone surveys. About 60% of the completes were from the mobile phone sample whereas landline completes accounted for the remaining 40%. Exclusions: NA Design effect: 1.5

Mode of data collection

Computer Assisted Telephone Interview [cati]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 3.8. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.