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Statistics
Published in Paul L. Goethals, Natalie M. Scala, Daniel T. Bennett, Mathematics in Cyber Research, 2022
Ordinal data is used for ordering purposes based on their relative position on a scale. Ordinal data can be sorted from highest to lowest, or vice versa. However, calculations cannot be done with ordinal data as they only show sequence instead of value. For example, rank (1st, 2nd, 3rd), a letter grade (A, B, C), or quality (good, average, bad).
What Do My Customers Really Want?
Published in Chris Hook, Ryan Burge, James Bagg, Routines for Results, 2017
Chris Hook, Ryan Burge, James Bagg
Ordinal data allows for rank order, by which data can be sorted but still does not allow for relative degree of difference between them. An example question could be, “When considering a new vehicle, please rank the importance of the following: Reliability, Price, Color, Performance.
Criteria, Factors, and Models
Published in Christian Tominski, Heidrun Schumann, Interactive Visual Data Analysis, 2020
Christian Tominski, Heidrun Schumann
An important property of a data domain is its scale. The scale determines what relations and operations are possible for the data values in the domain. At the top level, we can differentiate qualitative (or categorical) and quantitative (or numerical) data. At a second level, we can further categorize qualitative data into nominal and ordinal data, and quantitative data into discrete and continuous data. The different data scales and the relations and operations they permit are summarized in Table 2.2. Next, we look a bit closer at these different data. Nominal Data For nominal data, we can assume the existence of an equality relation =, which allows us to determine whether two values are equal. This is the most primitive insight that can be gained about data values. Additionally, we can count frequencies of nominal values.An example of nominal data would be identifiers such as names {Anika, Ilka, Maika, Tilo, …}.Ordinal Data For ordinal data, an order relation < exists in addition to the equality relation. This allows us to determine whether a data value is smaller (or less, before, weaker, of lower rank) than another data value. With the help of the order relation, it is possible to sort or rank data values.An example of ordinal data would be age groups such as {children, youths, adults, elders}. With the order relation, we can say that children < adults.
Can you feel the rhythm? Comparing vibrotactile and auditory stimuli in the rhythm video game Jump‘n'Rhythm
Published in Behaviour & Information Technology, 2023
Katya Alvarez-Molina, Anke V. Reinschluessel, Tim Kratky, Martin Scharpenberg, Rainer Malaka
Additionally, data were divided not only by levels but also by lives .8 Therefore, an analysis to find the correlation between the number of lives (per subject and level) and Condition, Level, Age, and Sex was calculated. The analysis used a mixed-effects cumulative logit model. These logistic random effects models are a popular tool to analyse multilevel data, also called hierarchical data with a binary or ordinal outcome. Ordinal data are a categorical variable in which levels have a natural ordering (e.g. light, medium, heavy) (Liu and Hedeker 2006). Furthermore, data can have a hierarchical or clustered structure (e.g. school, families) or repeatedly measured across time. In consequence, using mixed-effects regression models, are helpful to correlate all the variables. In particular, a logistic-based model is helpful for the analysis of ordinal data and multileveled (Liu and Hedeker 2006). Therefore, in the analysis, the target variable was the number of lives (per participant and level), and covariates condition (auditory or vibrotactile), level (Level 1 and Level 2), age (twenty to thirty-one years old), and sex (female, male). Participants’ random effect was incorporated because the taps (absolute accuracy) from the same participant may be correlated. The results of the model are shown in Table 2.
Interactive Visual Exploration of Big Relational Datasets
Published in International Journal of Human–Computer Interaction, 2023
Katerina Vitsaxaki, Stavroula Ntoa, George Margetis, Nicolas Spyratos
Currently, work regarding automated chart suggestion is at a preliminary stage, supporting column and bar charts, line charts, pie charts, scatter charts as well as data tables. In particular:Column charts are employed for combinations of nominal or ordinal data with quantitative data when the number of categories (nominal or ordinal data) is smaller than seven.Bar charts are used for combinations of nominal or ordinal data with quantitative data when the number of categories (nominal or ordinal data) is greater than seven.Line charts are suggested for combinations of time data with quantitative data.Pie charts are suggested for combinations of nominal or ordinal data with quantitative data when the values to be represented are decimal and their sum amounts to 1.Scatter charts are employed when the data for both axes are quantitative.In all other cases, results are presented as data tables.
Perspectives of a new sport-specific Para Shooting classification system for athletes with vision impairment
Published in Journal of Sports Sciences, 2021
Peter M. Allen, David L. Mann, Ian van der Linde, Eldré W. Beukes
To determine whether there were any statistically significant differences between groups, one-way ANOVAs were used in cases of continuous data with Tukey post-hoc testing performed where significant differences were found. ANOVAs were not always possible when comparing the new versus previous system as not all participants had experience with the previous system, and there were thus too few cases for this comparision. In these cases each system was compared separatedly. A Chi-squared test was used to analyse categorical data. The Kruskal-Wallis H non-parametric test was used for ordinal data with more than two levels and the Freidman test to analyse ordinal data with just one factor. For all analyses, a two-tailed significance level of.05 was considered statistically significant.