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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal 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. 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 in the...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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 from...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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...
Ranks01:02

Ranks

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...

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Related Experiment Video

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A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

On analyzing ordinal data when responses and covariates are both missing at random.

Subrata Rana1, Surupa Roy2, Kalyan Das1

  • 1Department of Statistics, University of Calcutta, Kolkata, India.

Statistical Methods in Medical Research
|June 28, 2013
PubMed
Summary

This study addresses missing data in biomedical research by developing a joint model for ordinal responses and covariates. The new method accounts for associations between missing data, improving inference in complex datasets.

Keywords:
Incomplete dataconfidence intervalrelative likelihoodsemi-Bayesian approach

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Last Updated: May 10, 2026

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Biomedical Data Analysis

Background:

  • Missing data in biomedical studies, whether responses or covariates, can lead to biased inference.
  • Existing methods often address missing responses or covariates separately, neglecting their interdependence.
  • Handling simultaneously missing ordinal responses and covariates presents significant modeling and computational challenges.

Purpose of the Study:

  • To develop and evaluate a joint statistical model for analyzing data with simultaneously missing ordinal responses and covariates.
  • To investigate the impact of the association between missing data processes on inference.
  • To provide robust analytical methods for complex biomedical datasets with substantial missingness.

Main Methods:

  • Development of a joint model incorporating associations between ordinal response variables, covariates, and missing data indicators.
  • Application of Markov chain Monte Carlo (MCMC) and Monte Carlo relative likelihood approaches for model analysis.
  • Evaluation of parameter estimation performance in finite samples using simulation studies.

Main Results:

  • The proposed joint model effectively handles simultaneously missing ordinal responses and covariates.
  • Both MCMC and Monte Carlo relative likelihood methods demonstrate reliable parameter estimation.
  • The analysis of an orthodontic study dataset yields significant insights into human habits.

Conclusions:

  • Joint modeling provides a powerful framework for addressing complex missing data scenarios in biomedical research.
  • The developed methods offer improved analytical solutions for studies with missing ordinal outcomes and covariates.
  • This approach enhances the accuracy of statistical inference in the presence of substantial missing data.