Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

27.8K
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. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
27.8K
Ranks01:02

Ranks

311
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
311
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

334
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
334
Time-Series Graph00:54

Time-Series Graph

4.7K
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...
4.7K
Outliers and Influential Points01:08

Outliers and Influential Points

4.9K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.9K
Interval Level of Measurement00:55

Interval Level of Measurement

16.6K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
16.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ir-organometallic compounds-mediated internal and external dual-phototheranostics for collaborative antitumor therapy.

Biomaterials·2025
Same author

Suicidal thoughts and behaviors associated with fluoroquinolone antibiotics: a real-world pharmacovigilance analysis.

Frontiers in pharmacology·2025
Same author

TongueNet: a multi-modal fusion and multi-label classification model for traditional Chinese Medicine tongue diagnosis.

Frontiers in physiology·2025
Same author

Efficacy analysis of 5-aminolevulinic acid photodynamic therapy for vulvar lichen sclerosus in women of childbearing age.

The Journal of dermatological treatment·2025
Same author

Selenium-Doped Carbon Dots as a Multipronged Nanoplatform to Alleviate Oxidative Stress and Ferroptosis for the Reversal of Acute Kidney Injury.

ACS nano·2025
Same author

Elevated Serum SERPINE2 Levels are Linked to Impaired Renal Function in Patients with Type 2 Diabetes Mellitus.

Diabetes therapy : research, treatment and education of diabetes and related disorders·2025

Related Experiment Video

Updated: Oct 24, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.6K

Structural change detection in ordinal time series.

Fuxiao Li1, Mengli Hao1, Lijuan Yang2

  • 1Department of Applied Mathematics, Xi'an University of Technology, Xi'an, Shaanxi, People's Republic of China.

Plos One
|August 16, 2021
PubMed
Summary

This study introduces two statistical methods for detecting structural changes in ordinal healthcare time series data. The findings aid in identifying critical shifts in real-time health data analysis.

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.1K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Related Experiment Videos

Last Updated: Oct 24, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.6K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.1K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Area of Science:

  • Statistics
  • Health Informatics
  • Time Series Analysis

Background:

  • Real-time healthcare data analysis is increasingly complex.
  • Ordinal time series are common in health applications.
  • Detecting structural changes is crucial for timely interventions.

Purpose of the Study:

  • To propose and evaluate novel statistical methods for change-point detection in ordinal time series.
  • To analyze the performance of two distinct test statistics for cumulative logistic regression models.
  • To investigate the consistency of change-point estimators and propose a method for multiple change-point detection.

Main Methods:

  • Development of two test statistics: standardized efficient score vector and a weighted quadratic form.
  • Derivation of asymptotic distributions under the null hypothesis.
  • Proof of consistency under the alternative hypothesis.
  • Application of a binary segmentation procedure for multiple change-point estimation.

Main Results:

  • Both proposed statistics effectively detect structural changes in ordinal time series.
  • The standardized efficient score vector performs better for central change-points.
  • The weighted quadratic form is preferable for change-points at data's beginning or end.
  • The standardized score vector can identify the cause of change-point detection.
  • Analysis of sleep data confirmed the existence of a structural change.

Conclusions:

  • The developed methods provide robust tools for change-point detection in healthcare ordinal time series.
  • The choice of statistic depends on the expected location of the change-point.
  • These methods enhance the analysis of real-time health data, enabling better understanding of underlying processes.