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Related Concept Videos

Nominal Level of Measurement00:56

Nominal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal scale is...
Bar Graph01:07

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Sign Test for Nominal Data01:12

Sign Test for Nominal Data

The sign test is a nonparametric method used to evaluate hypotheses about the median of a single sample or to compare the medians of two related samples. The sign test is particularly useful when dealing with nominal data, which includes distinct categories without an inherent order, such as names, labels, and preferences. Nominal data restricts statistical analysis to evaluating population proportions rather than mean or median values that require continuous data.
For example, consider a...
Multiple Bar Graph01:07

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
Ogive Graph01:07

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this type...

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Related Experiment Video

Updated: May 21, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Informative Missingness in Nominal Data: A Graph-Theoretic Approach to Revealing Hidden Structure.

Ehsan Zangene1, Veit Schwämmle2, Mohieddin Jafari1,3,4

  • 1Department of Pharmacology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

Computational and Structural Biotechnology Journal
|May 20, 2026
PubMed
Summary
This summary is machine-generated.

Missing data in nominal datasets can be informative. A graph-theoretic approach reveals hidden structures and constraints by analyzing missing value patterns, enhancing data understanding.

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Last Updated: May 21, 2026

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Area of Science:

  • Data Science
  • Graph Theory
  • Network Analysis

Background:

  • Missing data in nominal datasets is often excluded or imputed, overlooking potential insights.
  • The structure of missingness can reflect underlying biological, ecological, or operational constraints.

Purpose of the Study:

  • To present a novel graph-theoretic framework for analyzing missing data in nominal datasets.
  • To demonstrate that missing value patterns can serve as informative signals rather than mere analytical obstacles.

Main Methods:

  • Representing nominal variables as nodes and associations (observed or missing) as edges in bipartite graphs.
  • Analyzing graph properties like modularity, nestedness, and projection-based similarities.
  • Applying the framework to diverse case studies including proteomics, drug screening, ecological networks, and HR analytics.

Main Results:

  • The structure of missing values in various datasets contains significant information about latent substructures and constraints.
  • Graph-based analysis of missing data patterns effectively distinguishes between data missing at random and not at random.
  • The approach enhances structural understanding and provides complementary signals for downstream tasks like clustering.

Conclusions:

  • Missing values in nominal data are valuable sources of insight when analyzed appropriately.
  • A graph-theoretic perspective reinterprets missing data from a nuisance to an informative signal.
  • This method enriches the understanding of complex systems across multiple scientific domains.