How does candidate order on the ballot affect voting behavior when voters rank multiple candidates? I extend the analysis of ballot order effects to electoral systems with ordinal ballots, where voters rank multiple candidates, including ranked-choice voting (RCV). First, I discuss two types of ballot order effects, including "position effects''---voters vote for specific candidates because of their ballot positions---and "pattern ranking''---voters rank candidates geometrically given their grid-style ballots. Next, I discuss experimental designs for identifying and estimating these effects based on ballot order randomization. Moreover, I illustrate the proposed methods by using survey and natural experiments based on mayoral and congressional RCV elections in 2022. I find that while voters seem less susceptible to each ballot position than indicated in previous research, ballot structure can still impact voters' ranking behavior via pattern ranking---even when candidate order is fully randomized. This work has several implications for ballot design, survey research, and ranking data analysis. First, it shows that pattern ranking may affect electoral outcomes in RCV and other systems. Experts have suggested that ballot order randomization may solve the problem. However, this letter demonstrates that pattern ranking may still affect electoral results even when ballot order is fully randomized, which is often considered the best but practically challenging solution. Consequently, we may need to consider an alternative solution to ballot order effects, which does not depend on randomization or rotation. Second, similar effects may impact any survey research using grid-style ranking questions. Future research must investigate the statistical consequences of pattern ranking for survey research. Finally, ranking data allow researchers to study diverse quantities of interest, while targeting many different substantive questions. However, this flexibility also implies that analyzing ranking data can be prone to arbitrary analysis.

Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

Data from the article: Reliable Visual Analysis of Single-Case Data: A Comparison of Rating, Ranking, and Pairwise Methods http://dx.doi.org/10.1080/23311908.2021.1911076

The attached six data sets areQ sort and data analysis data for ranking Q-method for 5 different types of environmental assessment case studies. Respondents were asked to rank which capabilities they consider most important within their lived environment.

Datasets resulting from the Northland Potential Ecosystem report by Nick Singers (Singers & Rogers publication) and Ecosystem Prioritisation and Rarity by John Leathwick. Summaries of each dataset belowPotential Ecosystems:The ecosystem classification used to map potential ecosystems of Northland was developed by the Department of Conservation, as a tool for prioritising ecosystem management (Singers & Rogers 2014). This classification is a synthesis that amalgamates previous classifications and ecological studies aligned to an abiotic framework. It describes a full range of ecosystem types at a variety of scales in an approximation of the natural or potential state as they would have been observed if people arrived today in New Zealand. The classification system has two tables that describe ecosystem drivers of the abiotic environment (Singers and Rogers 2014) and biotic compositional description, which further includes ecosystem distribution and relevant source references (Singers and Rogers 2014). The data contained within these two tables have been used as the fundamental basis for mapping ecosystem types, supported by other readily available ecological descriptions, vegetation maps and relevant GIS layers such as soil maps of the Northland Region.Ecosystem Rarity:This layer consists of the intersection between the primary (see note below) ecosystem cover (as identified in the report ‘Indigenous Biodiversity Ranking for the Northland Region by J. R. Leathwick 2018) and the Northland Potential Ecosystem mapping (as identified in the report ‘A potential ecosystem map for the Northland Region: Explanatory information to accompany the map. Prepared for Northland Regional Council. © Nicholas Singers Ecological Solutions Ltd. NSES Ltd Report 12:2018/2019, June 2018.’Northland Biodiversity Ranking - Lake Ranks:Rankings of Lakes derived from a ranking analysis of indigenous-dominated terrestrial and freshwater ecosystems for the Northland Region.Northland Biodiversity Ranking - River Ranks:Rankings of rivers and streams derived from a ranking analysis of indigenous-dominated terrestrial and freshwater ecosystems for the Northland Region.Northland Biodiversity Ranking - Terrestrial Top 30 Sites:Delineation of the high priority terrestrial sites comprising 30% of the surviving indigenous-dominated ecosystems (or 10.6 % of the Northland Region) as identified in the ranking of terrestrial ecosystem areas derived from a ranking analysis of indigenous-dominated terrestrial ecosystems for the Northland Region.Northland Biodiversity Ranking - Terrestrial Top 30 Sites Additional 5:Delineation of the top 5 % of additional sites that would potentially make the largest additional gains assuming management is applied to the top 30% of sites as identified in the ranking of terrestrial ecosystem areas derived from a ranking analysis of indigenous-dominated terrestrial ecosystems for the Northland Region.

The measurement of change in biological systems through protein quantification is a central theme in modern biosciences and medicine. Label-free MS-based methods have greatly increased the ease and throughput in performing this task. Spectral counting is one such method that uses detected MS2 peptide fragmentation ions as a measure of the protein amount. The method is straightforward to use and has gained widespread interest. Additionally reports on new statistical methods for analyzing spectral count data appear at regular intervals, but a systematic evaluation of these is rarely seen. In this work, we studied how similar the results are from different spectral count data analysis methods, given the same biological input data. For this, we chose the algorithms Beta Binomial, PLGEM, QSpec, and PepC to analyze three biological data sets of varying complexity. For analyzing the capability of the methods to detect differences in protein abundance, we also performed controlled experiments by spiking a mixture of 48 human proteins in varying concentrations into a yeast protein digest to mimic biological fold changes. In general, the agreement of the analysis methods was not particularly good on the proteome-wide scale, as considerable differences were found between the different algorithms. However, we observed good agreements between the methods for the top abundance changed proteins, indicating that for a smaller fraction of the proteome changes are measurable, and the methods may be used as valuable tools in the discovery-validation pipeline when applying a cross-validation approach as described here. Performance ranking of the algorithms using samples of known composition showed PLGEM to be superior, followed by Beta Binomial, PepC, and QSpec. Similarly, the normalized versions of the same method, when available, generally outperformed the standard ones. Statistical detection of protein abundance differences was strongly influenced by the number of spectra acquired for the protein and, correspondingly, its molecular mass.

This dataset consists of papers of universities in the top 30 Scopus Ranking of Ukrainian Universities (May 2023). The data was obtained from Scopus using the search query "AF-ID (“university name”) AND PUBYEAR < 2023 AND PUBYEAR > 2002". Rank-citation curves were also generated for the publications of each university. In this analysis, the rank of publications was plotted along the horizontal axis, while the corresponding citation counts were depicted on the left axis. All types of documents were included in the dataset.

Data of investigation published in the article "Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus".

Abstract of the article:

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyse and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyse two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, WoS used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

As of 2024, Singapore ranked as the most digitally competitive country in the world. Digital competitiveness rankings aim to analyze a country's ability to adopt digital technologies and implement these technologies within enterprises and government organizations. Switzerland and Denmark rounded out the top three, while the United States ranked fourth.

In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100.

Global Health Security Index  Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency.

Universal Health Coverage Index  Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries. 

About this dataset

Context

This list ranks the 281 Cities in the Washington by Hispanic White population, as estimated by the United States Census Bureau. It also highlights population changes in each Cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic White Population: This column displays the rank of Cities in the Washington by their Hispanic White population, using the most recent ACS data available.
• Cities: The Cities for which the rank is shown in the previous column.
• Hispanic White Population: The Hispanic White population of the Cities is shown in this column.
• % of Total Cities Population: This shows what percentage of the total Cities population identifies as Hispanic White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Washington Hispanic White Population: This tells us how much of the entire Washington Hispanic White population lives in that Cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Abstract

South African policymakers are endeavouring to ensure that the poor have better access to financial services. However, a lack of understanding of the financial needs of poor households impedes a broad strategy to attend to this need.
The Financial Diaries study addresses this knowledge gap by examining financial management in rural and urban households. The study is a year-long household survey based on fortnightly interviews in Diepsloot (Gauteng), Langa (Western Cape) and Lugangeni (Eastern Cape). In total, 160 households were involved in this pioneering study which promises to offer important insights into how poor people manage their money as well as the context in which poor people make financial decisions. The study paints a rich picture of the texture of financial markets in townships, highlighting the prevalence of informal financial products, the role of survivalist business and the contribution made by social grants. The Financial Diaries dataset includes highly detailed, daily cash flow data on income, expenditure and financial flows on both a household and individual basis.

Geographic coverage

Langa in Cape Town, Diepsloot in Johannesburg and Lugangeni, a rural village in the Eastern Cape

Analysis unit

Units of analysis in the Financial Diaries Study 2003-2004 include households and individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

To create the sampling frame for the Financial Diaries, the researchers echoed the method used in the Rutherford (2002) and Ruthven (2002), a participatory wealth ranking (PWR). Within South Africa, the participatory wealth ranking method is used by the Small Enterprise Foundation (SEF), a prominent NGO microlender based in the rural Limpopo Province. Simanowitz (1999) compared the PWR method to the Visual Indicator of Poverty (VIP) and found that the VIP test was seen to be at best 70% consistent with the PWR tests. At times one third of the list of households that were defined as the poorest by the VIP test was actually some of the richest according to the PWR. The PWR method was also implicitly assessed in van der Ruit, May and Roberts (2001) by comparing it to the Principle Components Analysis (PCA) used by CGAP as a means to assess client poverty. They found that three quarters of those defined as poor by the PCA were also defined as poor by the PWR. We closely followed the SEF manual to conduct our wealth rankings, and consulted with SEF on adapting the method to urban areas.

The first step is to consult with community leaders and ask how they would divide their community. Within each type of areas, representative neighbourhoods of about 100 households each were randomly chosen. Townships in South Africa are organised by street - with each street or zone having its own street committee. The street committees are meant to know everyone on their street and to serve as stewards of all activity within the street. Each street committee in each area was invited to a central meeting and asked to map their area and give a roster of household names. Following the mapping, each area was visited and the maps and rosters were checked by going door to door with the street committee.

Two references groups were then selected from the street committee and senior members of the community with between four and eight people in each reference group. Each reference group was first asked to indicate how they define a poor household versus those that are well off. This discussion had a dual purpose. First, it relayed information about what each community believes is rich or poor. Second, it started the reference group thinking about which households belong under which heading.

Following this discussion, each reference group then ranked each household in the neighbourhood according to their perceived wealth. The SEF methodology of wealth ranking is de-normalised in that reference groups are invited to put households into as many different wealth piles as they feel in appropriate. Only households that are known by both reference groups were kept in the sample.

The SEF guidelines were used to assign a score to each household in a particular pile. The scores were created by dividing 100 by the number of piles multiplied by the level of the pile. This means that if the poorest pile was number 1, then every household in the pile was assigned a score of 100, representing 100% poverty. If the wealthiest pile was pile number 6, then every household in that pile received a score of 16.7 and every household in pile 5 received a score of 33.3. An average score for both reference groups was taken for the distribution.

One way of assessing how good the results are is to analyse how consistent the rankings were between the two reference groups. According to the SEF methodology, a result is consistent if the scores between the two reference groups have no more than a 25 points difference. A result is inconsistent if the difference between the scores is between 26 and 50 points while a result is unreliable is the difference between the scores is above 50 points. SEF uses both consistent and inconsistent rankings, as long as they use the average across two reference groups - this would mean that 91% of the sample could be used. However, because only used two reference groups were used, only the consistent household for the final sample selection was considered.

To test this further,the number of times that the reference groups put a household in the exact same category was counted. The extent of agreement at either end of the wealth spectrum between the two reference groups was also assessed. This result would be unbiased by how many categories the reference groups put households into.

Following the example used in India and Bangladesh, the sample was divided into three different wealth categories depending on the household's overall score. Making a distinction between three different categories of wealth allowed the following of a similar ranking of wealth to Bangladesh and India, but also it kept the sample from being over-stratified. A sample of 60 households each was then drawn randomly from each area. To draw the sample based on a proportion representation of each wealth ranking within the population would likely leave the sample lacking in wealthier households of some rankings to draw conclusions. Therefore the researchers drew equally from each ranking.

Mode of data collection

Face-to-face [f2f]

This dataset provides the inputs needed for the Galaxy Pathway Analysis workflow training tutorial (https://galaxy-synbiocad.org).

This workflow asseses the performance of predicted pathways by computing 4 criteria (target product flux, thermodynamic feasibility, pathway length, and enzyme availability). A score inform the user about the best candidate pathways to produce a compound of interest. The generated output is a collection of scored and ranked heterologous pathways.

The content of the dataset is as follows:

- A set of pathways provided in the SBML format (Systems Biology Markup Language) to be ranked, modeling heterologous pathways such as those outputted by the RetroSynthesis workflow (https://galaxy-synbiocad.org).

- The GEM (Genome-scale metabolic models) which is a formalized representation of the metabolism of the host organism (the model is E. coli iML1515), provided in the SBML format.

This study leveraged an existing implementation effort in Kaiser Permanente Washington to increase access to cancer genetic services. Using OPTICC Stage I and Stage II methods (see OPTICC.org), this study matched implementation strategies to high priority determinants for routine hereditary cancer risk assessment and delivery of genetic testing, and evaluated a staged, stakeholder-driven approach to program implementation planning. This dataset includes aggregated responses from the Barriers Survey. It includes the mean score as well as score range for impact and persistence, as well as overall score. It then includes the study teams analysis ranking, based on the responses. For the survey responses (middle 3 columns), the higher the number the more impactful/persistent the barrier (1 = not at all influential/has never been a problem, 4= extremely influential/has always been a problem). For the study team ranking/analysis (last column), 1 = most important (so opposite of the survey responses). This data was collected in July 2023. Six participants responded to the survey that fed into this analysis • This data includes aggregate quantitative data

The European Quality of Life survey (EQLS) examines both the objective circumstances of European citizens' lives, and how they feel about those circumstances, and their lives in general. It looks at a range of issues, such as employment, income, education, housing, family, health and work-life balance. It also looks at subjective topics, such as people's levels of happiness, how satisfied they are with their lives, and how they perceive the quality of their societies.

The survey is carried out every four years.The European Foundation for the Improvement of Living and Working Conditions (Eurofound) commissioned GfK EU3C to carry out the survey.

The survey was carried in the 27 European Member States (EU27), and the survey was also implemented in seven non-EU countries. The survey covers residents aged 18 and over.

A selection of key findings from the 2010/11 data released in July 2013 are presented in this briefing: The socio-economic position of Londoners in Europe: An analysis of the 2011 European Quality of Life Survey.

https://s3-eu-west-1.amazonaws.com/londondatastore-upload/eqol-report.PNG" alt="">

For the purposes of the rankings in this report, London is treated as a 35th European country.The themes covered in the analysis below are: volunteering, community relations, trust in society, public services ratings, well-being, health, wealth and poverty, housing, and skills and employment.

The tables following the analysis on page 4 show figures and rankings for:

- London,

- rest of the UK,

- Europe average,

- the highest ranked country, and

- the lowest ranked country.

Internet use data for all European NUTS1 areas included in spreadsheet. Note figures based on low sample sizes marked in pink.

About this dataset

Context

This list ranks the 90 Cities in the Arizona by Non-Hispanic White population, as estimated by the United States Census Bureau. It also highlights population changes in each Cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Non-Hispanic White Population: This column displays the rank of Cities in the Arizona by their Non-Hispanic White population, using the most recent ACS data available.
• Cities: The Cities for which the rank is shown in the previous column.
• Non-Hispanic White Population: The Non-Hispanic White population of the Cities is shown in this column.
• % of Total Cities Population: This shows what percentage of the total Cities population identifies as Non-Hispanic White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Arizona Non-Hispanic White Population: This tells us how much of the entire Arizona Non-Hispanic White population lives in that Cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This repository contains the spreadsheet for the "Ranking Matrix" computations,
as mentioned in the Annex 2 of Deliverable 4.1 "Report on Analysis and on Ranking of Formal Methods"
of the project ASTRail (http://www.astrail.eu).

Ranking of 7 factors.

About this dataset

Context

This list ranks the 43 cities in the Hamilton County, OH by American Indian and Alaska Native (AIAN) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Native American Population: This column displays the rank of cities in the Hamilton County, OH by their American Indian and Alaska Native (AIAN) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Native American Population: The Native American population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Native American. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Hamilton County Native American Population: This tells us how much of the entire Hamilton County, OH Native American population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.